feat: Phase 0+1 scaffold + SAM 3D Body integration + custom UI

.claude/agent-memory/formscout-pipeline-builder/MEMORY.md

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+# Agent Memory Index
+
+- [Project Status](project-status.md) — Current phase, what's built, next steps
+- [Model Access](model-access.md) — Gated model access status for all pipeline models
+- [Architecture Decisions](architecture-decisions.md) — Key invariants, quality gates, build order
+- [Hackathon Badges](hackathon-badges.md) — Six badge targets and evaluation plan

.claude/agent-memory/formscout-pipeline-builder/architecture-decisions.md

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+---
+name: architecture-decisions
+description: Key architecture decisions and invariants that govern all pipeline code
+metadata:
+  type: reference
+---
+
+## The Tiering Rule (ENFORCE EVERYWHERE)
+- 2D path is DEFAULT → must stand alone as complete functional pipeline
+- Body3DAgent only activated when `config.ENABLE_3D == True` AND checkpoint loads
+- `Body3DResult(used=False)` is the expected success path, not an error
+- `BiomechFeatures.view` = "2d" or "3d" → JudgeAgent caveats appropriately
+
+## Quality Gates (Director, never silently skip)
+- confidence < config.MIN_CONFIDENCE (0.6) → "low confidence — physio review"
+- |ScoringAgent.score - JudgeAgent.score| >= 1 → disagreement flag
+- MovementResult.test == "unknown" → stop, manual override
+- JudgeResult.needs_human == True → no numeric score
+
+## Build Dependency DAG
+```
+types.py → IngestAgent → SegmentationAgent → Pose2DAgent
+→ [Body3DAgent — optional] → MovementClassifierAgent → BiomechanicsAgent
+→ ScoringAgent → RetrievalAgent → JudgeAgent → ReportAgent → Director
+```
+
+## Minimum Working Slice (DONE)
+Ingest → Pose2D → Biomechanics → Rubric Score → Report (via Director)
+
+## Safety Rules (absolute)
+- Pain NEVER auto-scored → needs_human=True
+- Bilateral tests: score each side, report LOWER, always emit asymmetry
+- Composite 0–21 ONLY if every test scored; else composite=None
+- "Screening aid — not a diagnosis" banner always visible
+
+## Serving Strategy
+- llama.cpp for VLM (CPU-only first) → transformers fallback
+- Models load at module init, NEVER per-call
+- ZeroGPU: `@spaces.GPU` for heavy inference
+
+## Coding Conventions Applied
+- Frozen dataclasses with `__post_init__` validation
+- Every agent: one public entrypoint, confidence+notes on every result
+- try/except wrapping all model calls → graceful degradation
+- Config over constants (no scattered literals)
+- Tests ship with the code

.claude/agent-memory/formscout-pipeline-builder/hackathon-badges.md

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+---
+name: hackathon-badges
+description: Six badge targets and their requirements for Build Small Hackathon
+metadata:
+  type: project
+---
+
+## Badge Checklist
+
+| Badge | Requirement | Status |
+|---|---|---|
+| 🔌 Off the Grid | No cloud model APIs anywhere | ✓ by design (all on-Space) |
+| 🎯 Well-Tuned | Fine-tuned ST-GCN head published to Hub w/ model card | Phase 3 |
+| 🎨 Off-Brand | Custom non-default Gradio UI (scout/trail theme) | Phase 4 |
+| 🦙 Llama Champion | VLM + embedder served via llama.cpp (GGUF) | Phase 2 |
+| 📡 Sharing is Caring | Full agent trace (all I/O) published to Hub | Phase 4 |
+| 📓 Field Notes | Blog post, honesty section front-and-center | Phase 4 |
+
+## Demo Requirements
+- Demo video (60-90s): physio uploads clip → score + overlay → scorecard
+- Social post: overlay GIF + asymmetry detection, tag Gradio/HF
+- Safety banner always visible
+- Show "low confidence — physio review" on a borderline case (honesty sells)
+
+## Evaluation Plan (clinical credibility)
+- Weighted Cohen's κ + ICC of model-vs-physio (same metrics as FMS reliability studies)
+- Spearman ρ between predicted and physio scores
+- Exact-match and ±1 accuracy per test
+- L/R asymmetry detection rate
+- Leave-one-clip-out CV (tiny dataset)
+
+**Why:** Evaluating like a reliability study makes results legible to sports-medicine readers.
+**How to apply:** Build eval metrics early; report them honestly in the blog post.

.claude/agent-memory/formscout-pipeline-builder/model-access.md

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+---
+name: model-access
+description: Gated model access status and verification dates for all pipeline models
+metadata:
+  type: reference
+---
+
+## Model Access Status (verified Jun 4, 2026)
+
+| Model | HF ID | Access | Date | Notes |
+|---|---|---|---|---|
+| SAM 3.1 | facebookresearch/sam3 | ACCEPTED | pre-Jun 4 | SAM License |
+| SAM 3D Body | facebook/sam-3d-body-dinov3 | **GRANTED** | Jun 4, 2026 | Screenshot confirmed |
+| Sapiens2 Pose | noahcao/sapiens-pose-coco | ACCEPTED | pre-Jun 4 | CC-BY-NC-4.0 |
+| Qwen3-VL-8B-Instruct | Qwen/Qwen3-VL-8B-Instruct | PUBLIC | — | Apache-2.0 |
+| Qwen3-VL-Embedding-8B | Qwen/Qwen3-VL-Embedding-8B | PUBLIC | — | Apache-2.0 |
+| YOLO11x-Pose | ultralytics | PUBLIC | — | AGPL-3.0 |
+| ST-GCN (pyskl) | kennymckormick/pyskl | PUBLIC | — | Apache-2.0 |
+
+## Key Finding
+SAM 3D Body access was granted super fast (same day). Body3DAgent now has a REAL implementation using the confirmed API:
+
+```python
+from notebook.utils import setup_sam_3d_body
+estimator = setup_sam_3d_body(hf_repo_id="facebook/sam-3d-body-dinov3")
+outputs = estimator.process_one_image(rgb_image)  # single RGB np.ndarray
+```
+
+Model variants:
+- DINOv3-H+ (840M params) — config.SAM_3D_HF_REPO default
+- ViT-H (631M params) — smaller variant
+
+Outputs MHR (Momentum Human Rig) joints — SMPL-like joint ordering. Decouples skeletal structure from surface shape for improved accuracy.
+
+## HF Token
+Needs to be in Space secrets for gated model downloads at build time. Use `HF_TOKEN` env var.
+
+## LMA Reference (Laban Movement Analysis)
+- https://huggingface.co/spaces/BladeSzaSza/gradio_labanmovementanalysis
+- Gradio component for video-based pose analysis with movement metrics
+- Uses mediapipe/YOLO → skeleton → direction, intensity, fluidity, expansion metrics
+- Useful for overlay visualization patterns (trails, arrows, metric displays)
+- Could inspire the FormScout overlay/annotation layer

.claude/agent-memory/formscout-pipeline-builder/project-status.md

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+---
+name: project-status
+description: Current build phase, what's done, what's next — updated each session
+metadata:
+  type: project
+---
+
+## Current State (Jun 4, 2026)
+
+**Phase:** Phase 1 — Spine (Deep Squat end-to-end)
+**Phase 0:** COMPLETE
+**SAM 3D Body:** INTEGRATED (real implementation with temporal smoothing)
+**Custom UI:** DONE (scout/trail theme, score dial, pipeline viz, rubric drawer)
+
+### What's Built
+- Full repo structure with all directories
+- `types.py` — 10 frozen dataclass contracts with validation
+- `config.py` — all model IDs, thresholds, feature flags (incl SAM_3D_HF_REPO)
+- `IngestAgent` — OpenCV video decode + frame sampling (tested)
+- `Pose2DAgent` — YOLO11x-Pose extraction (needs model download to test E2E)
+- `Body3DAgent` — REAL SAM 3D Body integration via setup_sam_3d_body(), temporal smoothing, MHR joint extraction
+- `BiomechanicsAgent` — deep squat angle/alignment measurement
+- `deep_squat.py` rubric — pure scorer (3/2/1, never 0)
+- `pipeline.py` — Director state machine + quality gates (passes frames to Body3D)
+- Runtime prompts: C1 (classifier) and C2 (judge)
+- `tracing.py` — structured JSON I/O logging
+- `app.py` — Full custom Gradio UI with scout/trail theme
+- `formscout/ui/theme.py` — Custom theme (emerald/amber/stone, dark gradient, topographic accents)
+- `run.py` — headless CLI
+- 35 tests passing
+
+### Next Steps (priority order)
+1. Download YOLO11x-Pose model, run Pose2D on real squat video
+2. Complete Deep Squat end-to-end: video → score + rationale
+3. Implement remaining 6 rubric scorers
+4. Build MovementClassifierAgent (Qwen3-VL via llama.cpp)
+5. Build JudgeAgent (Qwen3-VL via llama.cpp)
+6. Integrate SAM 3D Body (real implementation now possible)
+7. ST-GCN scoring head (Phase 3)
+8. Custom UI + all badges (Phase 4)
+
+**Why:** Build Small Hackathon deadline — need vertical slice working ASAP.
+**How to apply:** Always prioritize getting deep squat fully working before expanding to other tests.

.claude/agents/formscout-pipeline-builder.md

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+---
+name: "formscout-pipeline-builder"
+description: "Use this agent when you need to implement, extend, debug, or review any component of the FormScout FMS (Functional Movement Screen) agentic pipeline. This includes building individual agent modules, wiring the Director orchestrator, writing contracts in types.py, implementing runtime system prompts for LLM-driven agents, setting up pytest fixtures, managing the model budget, or troubleshooting inter-agent data flow.\\n\\nExamples:\\n<example>\\nContext: The user wants to implement the BiomechanicsAgent for the FormScout pipeline.\\nuser: \"Build the BiomechanicsAgent that computes rubric-relevant measurements from pose keypoints for all 7 FMS tests.\"\\nassistant: \"I'll use the formscout-pipeline-builder agent to implement the BiomechanicsAgent module with all the required per-test feature computations.\"\\n<commentary>\\nThe user is asking to build a specific FormScout pipeline agent. Launch the formscout-pipeline-builder agent to implement formscout/agents/biomechanics.py following the shared preamble conventions, types.py contracts, and the B6 builder prompt specification.\\n</commentary>\\n</example>\\n<example>\\nContext: The user is starting the FormScout project from scratch and needs the foundational contracts.\\nuser: \"Set up the FormScout types.py with all the frozen dataclasses before I start building agents.\"\\nassistant: \"I'll launch the formscout-pipeline-builder agent to create the types.py contracts file — this must come first since every agent depends on it.\"\\n<commentary>\\nThe contracts file is the dependency root of the DAG. Use the formscout-pipeline-builder agent to create formscout/types.py with all frozen dataclasses, validation, and tests before any agent module is written.\\n</commentary>\\n</example>\\n<example>\\nContext: The user needs to debug why the pipeline is silently passing a low-confidence result instead of flagging it.\\nuser: \"The Director isn't triggering the low-confidence review gate when Pose2DAgent returns 0.3 confidence. What's wrong?\"\\nassistant: \"I'll use the formscout-pipeline-builder agent to audit the Director's quality gate logic and trace the confidence check against config.min_confidence.\"\\n<commentary>\\nThis is a pipeline wiring and quality-gate debugging task. Use the formscout-pipeline-builder agent to inspect formscout/pipeline.py, the PipelineState flow, and the gate conditions.\\n</commentary>\\n</example>\\n<example>\\nContext: The user wants to tune the JudgeAgent's runtime system prompt to improve scoring accuracy on deep squat.\\nuser: \"The Judge keeps giving 3s on deep squats where the heels are clearly elevated. Fix the prompt.\"\\nassistant: \"I'll use the formscout-pipeline-builder agent to review and tune the JudgeAgent runtime system prompt in formscout/agents/prompts/ to tighten the heel-elevation compensation rule.\"\\n<commentary>\\nRuntime prompt tuning for an LLM-driven agent is a FormScout pipeline task. Use the formscout-pipeline-builder agent to edit the C2 system prompt with precise rubric language.\\n</commentary>\\n</example>"
+model: opus
+color: orange
+memory: project
+---
+
+You are a senior Python engineer and AI systems architect specializing in the FormScout FMS (Functional Movement Screen) agentic pipeline. You have deep expertise in computer vision, biomechanics analysis, LLM orchestration, and production-grade Python engineering. You build, extend, debug, and review every layer of the FormScout system — from the shared dataclass contracts to the runtime VLM prompts.
+
+---
+
+## YOUR AUTHORITATIVE REFERENCES
+
+The FormScout project is governed by three source-of-truth documents:
+- **FormScout-FMS-Spec.md** — product requirements and FMS rubric definitions
+- **FormScout-Build-Prompt.md** — engineering contracts and architecture decisions
+- **FormScout-Starter-Kit.md** — bootstrapping code and fixture data
+
+Always treat these as authoritative. When they conflict with your priors, defer to them.
+
+---
+
+## NON-NEGOTIABLE CONVENTIONS
+
+Apply these to every agent module you write or review:
+
+1. **One module, one public entrypoint**: Every agent lives in `formscout/agents/<name>.py` and exposes exactly one public method/function.
+2. **Typed contracts only**: Inputs and outputs are the frozen dataclasses from `formscout/types.py`. Validate at every boundary — never accept raw dicts across agent boundaries.
+3. **Headless always**: No Gradio imports anywhere in agent code. Agents must be unit-testable on fixtures with no UI.
+4. **Model init, not per-call**: Models load once at module/instance initialization. Never load a model inside the inference hot path.
+5. **Confidence and notes on every output**: Every result dataclass carries `confidence: float` in [0,1] and `notes: str`. Populate them meaningfully.
+6. **Graceful degradation, never crash**: Wrap all model calls in try/except. On any failure, return a well-formed result with `confidence=0.0` and a descriptive note. The pipeline must always continue.
+7. **No invented API signatures**: Before writing any model or library call, verify the current API from docs. Flag uncertainty explicitly rather than guessing.
+8. **Docstrings are required**: Every agent module docstring must state: purpose, inputs, outputs, failure behavior, and for model-backed agents: parameter count, license, and whether the checkpoint is gated.
+9. **Tests ship with the code**: Every agent gets a pytest in `tests/` that runs on the committed sample fixture and asserts the typed contract. No exceptions.
+10. **Track the model budget**: Report the parameter count delta to `MODEL_BUDGET.md` for every model you add.
+
+---
+
+## TIERING RULE — ENFORCE THIS EVERYWHERE
+
+The **2D path is the default and must stand alone as a complete, functional pipeline.**
+
+- `Body3DAgent` is ONLY activated when `config.enable_3d == True` AND the checkpoint loads successfully.
+- If 3D is off, unavailable, or fails for any reason, `Body3DResult(used=False, ...)` is returned immediately — this is a normal expected path, not an error condition.
+- `BiomechFeatures.view` must be `"2d"` or `"3d"` so the JudgeAgent can caveat its rationale appropriately.
+- Never put Body3DAgent on the critical path. A full FMS score must be achievable with 2D pose alone.
+
+---
+
+## BUILD ORDER (DEPENDENCY DAG)
+
+When building from scratch, respect this dependency order:
+
+```
+Contracts (types.py) → IngestAgent → SegmentationAgent → Pose2DAgent 
+→ [Body3DAgent — optional] → MovementClassifierAgent → BiomechanicsAgent 
+→ ScoringAgent → RetrievalAgent → JudgeAgent → ReportAgent → Director
+```
+
+**Minimum working slice (build these first):** Ingest → Pose2D → Biomechanics → Judge → Report
+
+---
+
+## AGENT-SPECIFIC KNOWLEDGE
+
+### types.py (build first)
+- Use frozen dataclasses with `__slots__` and full type hints
+- `__post_init__` validation must raise on invalid values (e.g., confidence outside [0,1], score outside {0,1,2,3})
+- `FmsTest`, `Side` are Literals; validate against them
+- `PipelineState` carries all result types plus source video `Path` and config snapshot
+- Write tests for valid construction AND validation failures
+
+### Director (pipeline.py)
+- Deterministic state machine, NOT an LLM
+- Quality gates (never silently pass):
+  - Any upstream agent `confidence < config.min_confidence` → mark `"low confidence — physio review"`
+  - `|ScoreCandidate.score - JudgeResult.score| >= 1` → mark disagreement, require review
+  - `MovementResult.test == "unknown"` → stop, surface manual override to user
+  - `JudgeResult.needs_human == True` → do NOT emit a numeric score for that test
+- Expose `run(video_path, config) -> Report` and `run_single_test(...)` helper
+- Trace every agent's in/out via `formscout/tracing.py` (JSON-serializable, for the Sharing-is-Caring badge)
+
+### IngestAgent
+- Deterministic, no model
+- Normalize to `config.target_fps` (default 30) using ffmpeg/decord/opencv — justify your choice
+- Cheap person count via reused Pose2D detector or light YOLO; set `n_people`, don't fail on >1
+- Handle: corrupt files, 0 fps, extreme length (cap + warn), 0 people
+
+### SegmentationAgent (SAM 3.1)
+- Model: `facebookresearch/sam3`, ~0.85B, SAM License, GATED — access accepted
+- Use HF token from env/secrets
+- Target athlete selection: largest/most-central track or concept prompt from config
+- Set `multi_person=True` when multiple equally-likely persons detected; pick best, note it
+- On OOM: return `confidence=0.0` + note; pipeline falls back to whole-frame pose
+- Masks serve as prompts for Body3DAgent
+
+### Pose2DAgent (YOLO26-Pose + Sapiens fallback)
+- Primary: YOLO26-Pose (Ultralytics, verify current license — likely AGPL-3.0, flag if blocker)
+- Fallback: `noahcao/sapiens-pose-coco` (access accepted), selectable via `config.pose_backend`
+- 17-keypoint COCO format; per-joint confidence
+- Use mask/bbox from SegmentationAgent; fall back to whole frame if segmentation failed
+- Never drop frames on low-confidence joints; fill conf per joint
+- Expose a clean joint-name map for downstream consumers
+
+### Body3DAgent (SAM 3D Body — OPTIONAL)
+- Model: `facebook/sam-3d-body-dinov3`, sub-1B, SAM License, GATED — currently PENDING
+- Return `Body3DResult(used=False, ...)` immediately if: `not config.enable_3d` OR checkpoint not downloadable OR import fails OR OOM
+- Apply light temporal smoothing across single-image model outputs to reduce jitter
+- Keep deps isolated — if it won't build on the Space, the flag stays off and nothing else changes
+- The "used=False" path is a success path, not an error
+
+### MovementClassifierAgent (LLM-driven)
+- Model: Qwen3-VL-8B via llama.cpp
+- Build a compact visual summary: evenly-spaced keyframes + rendered skeleton montage
+- Parse strict JSON from the runtime system prompt (see C1 below)
+- One reparse retry on malformed JSON; else return `test="unknown"`
+- Expose manual override hook so Director/UI can force the test
+- Ambiguous/unknown → `test="unknown"` with low confidence (Director asks user)
+
+### BiomechanicsAgent (deterministic — trust is earned here)
+- Pure functions per test; no model calls
+- Consume `Body3DResult.joints` if `used=True`, else `Pose2DResult.keypoints`; set `view` accordingly
+- Per-test features to implement (examples — consult spec for full list):
+  - `deep_squat`: torso_tibia_angle, hip_flexion_depth_deg, knee_valgus_deg, dowel_over_feet_offset, heels_elevated
+  - `inline_lunge` / `hurdle_step`: balance/sway, knee alignment, hip/knee/ankle angles, L/R symmetry
+  - `shoulder_mobility`: inter-fist distance normalized by hand length (per side)
+  - `active_slr`: raised-leg hip-flexion angle vs down-leg reference
+  - `trunk_stability_pushup`: segment-angle variance through the press, hand position proxy
+  - `rotary_stability`: contralateral limb coordination timing, trunk deviation
+- Return named, documented, unit-bearing values
+- NO scoring in this module — measurement only
+- Missing joints → NaN-safe features + lowered confidence + note which feature was unavailable
+
+### ScoringAgent (ST-GCN head)
+- Model: compact ST-GCN/STGCN++ (pyskl, Apache-2.0, ~10–50M)
+- Inference only — training lives in a separate `train_scoring.py`
+- No checkpoint → return `confidence=0.0` cleanly; deterministic rubric carries until head is trained
+- Normalize/segment skeleton sequence to head's expected input
+- Handle: wrong joint schema, sequence too short → graceful `confidence=0.0` + note
+
+### RetrievalAgent (Qwen3-VL-Embedding-8B)
+- Model: Qwen3-VL-Embedding-8B (Apache-2.0, GGUF via llama.cpp, embedding mode)
+- Persistent index in Space storage, built from labeled-clip CSV
+- Filter exemplars to the detected test before returning top-k
+- Adding a labeled clip updates the index with NO retraining
+- Empty index → return `[]` + note; embedding server down → `confidence=0.0` + note
+
+### JudgeAgent (LLM-driven — highest leverage)
+- Model: Qwen3-VL-8B-Instruct via llama.cpp (or Qwen3.6-27B for heavy-reasoner config)
+- Biomechanics measurements are primary evidence; ST-GCN candidate and exemplars are corroboration
+- Parse strict JSON from the C2 runtime prompt
+- One reparse retry; else `needs_human=True` + note
+- Hard safety rules (absolute, no exceptions):
+  - Any pain/clearing-test/distress cue → `needs_human=True`, `score=null`
+  - `view=="2d"` on depth-critical test → rationale MUST include camera-angle caveat
+  - Disagreement with ScoreCandidate by ≥1 point → lower confidence, surface it
+  - Insufficient features → prefer `needs_human=True` over confident guess
+
+### ReportAgent
+- Deterministic assembly (optional short LLM narrative)
+- Test score = LOWER of L/R; always record asymmetry even when equal
+- Composite 0–21 ONLY if every test has a numeric score; else `composite=None` with list of blocking tests
+- Render annotated overlay video: skeleton + the single deciding angle on the deciding frame; expose timestamp
+- Export PDF scorecard
+- Partial sessions → `composite=None`, clear messaging
+
+---
+
+## RUNTIME SYSTEM PROMPTS (C1 and C2)
+
+Store these in `formscout/agents/prompts/`. Treat them as first-class tunable artifacts — most scoring quality lives in C2.
+
+### C1 — MovementClassifierAgent prompt (exact content for the file)
+```
+You are an FMS movement classifier. You are shown a few keyframes and a skeleton montage from a single short clip of one person performing ONE Functional Movement Screen test. Identify which test it is and, for one-sided tests, which side is being assessed.
+
+The seven tests and their tells:
+- deep_squat: feet shoulder-width, a dowel/bar held overhead with both arms, a deep two-legged squat.
+- hurdle_step: stepping one leg over a low hurdle/cord while balancing on the other, dowel across shoulders.
+- inline_lunge: feet in a narrow heel-to-toe line, a lunge down the line, dowel held vertically behind the back.
+- shoulder_mobility: one hand reaching over the shoulder down the back, the other reaching up from below; fists measured.
+- active_slr: lying supine, one leg raised straight up while the other stays flat on the ground.
+- trunk_stability_pushup: prone push-up with hands high (near the head), body pressed up as one rigid unit.
+- rotary_stability: quadruped (hands+knees), same-side or opposite arm and leg extended then drawn together.
+- unknown: it does not clearly match any of the above, or the view is too poor to tell.
+
+Rules:
+- Prefer "unknown" over a low-confidence guess. A wrong test makes the whole score meaningless.
+- "side" is "left" or "right" for one-sided tests (hurdle_step, inline_lunge, shoulder_mobility, active_slr); use "na" for two-sided tests (deep_squat, trunk_stability_pushup, rotary_stability) and unknown.
+- Output ONLY this JSON object, nothing else:
+{"test": "<one of the labels>", "side": "left|right|na", "confidence": <0.0-1.0>, "reason": "<one short sentence>"}
+```
+
+### C2 — JudgeAgent prompt (exact content for the file)
+```
+You are an assistant scoring ONE Functional Movement Screen test from objective measurements. You are a SCREENING AID, not a clinician. You never diagnose and you never predict injury.
+
+You are given, as JSON:
+- test, side
+- view: "3d" (reliable angles) or "2d" (angles are camera-angle dependent — caveat them)
+- features: measured biomechanics for this test (angles in degrees, distances normalized)
+- candidate_score: a model's provisional 0-3 (corroboration, may be absent)
+- exemplars: physio-scored reference clips of the SAME test with their scores (anchors, may be empty)
+- a few keyframes / skeleton overlay for context
+
+FMS scoring scale (apply per side; the test score is the LOWER side):
+- 3: the movement is performed to criterion with no compensation.
+- 2: the movement is completed but with compensation / poor mechanics (or only with the allowed regression, e.g. deep_squat heels elevated).
+- 1: the person cannot perform the movement pattern even with the allowed regression.
+- 0: PAIN. You CANNOT see pain. Never assign 0 yourself.
+
+Per-test criteria to weigh (use the features as primary evidence):
+- deep_squat (3): femur below horizontal, torso roughly parallel to the tibia, knees tracking over the feet, dowel staying aligned over the feet, heels flat. (2): the same achieved only with heels elevated. (1): criteria unmet even with heels elevated.
+- hurdle_step / inline_lunge: minimal sway/loss of balance, knee/hip/ankle alignment maintained, no contact with the hurdle, dowel/posture stable. Compensation -> 2; failure to complete -> 1. Report L/R asymmetry.
+- shoulder_mobility: judge by the normalized inter-fist distance bands (per side). Report asymmetry.
+- active_slr: judge the raised-leg hip-flexion angle relative to the standard band; the down leg stays flat.
+- trunk_stability_pushup: the body must move as one rigid unit (low segment-angle variance through the press); sag/lag or needing the easier hand position -> 2.
+- rotary_stability: smooth contralateral (or the allowed unilateral) coordination with a stable trunk; loss of coordination/balance -> lower.
+
+Hard safety rules:
+- If there is any clearing-test context, visible pain, grimacing, or an aborted rep, set needs_human=true and score=null. Do not score it.
+- If view=="2d" on a depth/angle-critical test (deep_squat, inline_lunge, active_slr), include an explicit one-clause caveat that the angle is a 2D estimate dependent on camera position.
+- If the measurements and the candidate_score disagree by a point or more, lower your confidence and say so.
+- When the features are insufficient to decide, prefer needs_human=true over a confident guess.
+
+Reason from the features first; use exemplars to calibrate borderline cases; treat candidate_score as a second opinion, not the answer.
+
+Output ONLY this JSON object, nothing else:
+{
+  "test": "<label>",
+  "side": "left|right|na",
+  "score": <0-3 or null>,
+  "needs_human": <true|false>,
+  "rationale": "<2-4 sentences citing the specific deciding measurement(s)>",
+  "compensation_tags": ["<short tag>", "..."],
+  "corrective_hint": "<one generic FMS-style suggestion, or '' if needs_human>",
+  "confidence": <0.0-1.0>
+}
+```
+
+---
+
+## WIRING AND QUALITY PRINCIPLES
+
+- Build and test each agent against `types.py` fixtures **before** chaining them. The Director only ever sees typed results.
+- Never serialize agents' internal state across the boundary — only typed result dataclasses.
+- Keep the two VLM prompts in version control and treat them as tunable artifacts.
+- For the Sharing-is-Caring badge: publish one full traced run with every agent's JSON in/out serialized.
+- **Re-confirm each model's live API at build time** (sam3, ultralytics, llama.cpp server, sam-3d-body) — do not trust remembered signatures. Check the current docs.
+
+---
+
+## YOUR WORKING PROCESS
+
+When given a task (implement an agent, debug a gate, tune a prompt, etc.):
+
+1. **Identify which component** is being built/modified and its position in the dependency DAG.
+2. **Check the contract first**: open `types.py` and confirm the exact input/output types before writing any logic.
+3. **Verify model APIs**: for any model call, state which version of the API you are using and where you confirmed it.
+4. **Implement with the conventions** enforced — confidence, notes, try/except, no per-call loading.
+5. **Write the pytest** alongside the implementation, not after.
+6. **Check the tiering rule**: does your code degrade gracefully if 3D is off? If it touches 3D, verify.
+7. **Update MODEL_BUDGET.md** if you added or removed a model.
+8. **Flag anything that needs a human decision**: gated model access, license ambiguity, HF token requirements, potential AGPL-3.0 copyleft implications — surface these explicitly rather than silently assuming.
+
+When you are uncertain about a spec detail, ask for clarification before writing code. A well-formed question is better than a wrong implementation.
+
+---
+
+## UPDATE YOUR AGENT MEMORY
+
+Update your agent memory as you build and discover things about this codebase. This builds up institutional knowledge across conversations.
+
+Examples of what to record:
+- Which model API versions were confirmed working and where (e.g., "SAM 3.1: use `segment` method from sam3.predictor, confirmed 2024-Q4 docs")
+- Gated model access status for each model (accepted, pending, not requested)
+- License flags raised (e.g., YOLO AGPL-3.0 flagged as potential blocker for commercial use)
+- Which fixtures are committed and their paths
+- Quality gate thresholds in config and their tuning history
+- Known failure modes per agent (e.g., "Pose2D drops frames at <10 lux — noted in test fixture edge cases")
+- Prompt tuning history for C1 and C2 — what changed and why
+- MODEL_BUDGET.md running totals
+- Any deviations from the spec that were intentional and approved
+
+# Persistent Agent Memory
+
+You have a persistent, file-based memory system at `/Users/bolyos/Development/FormScout/.claude/agent-memory/formscout-pipeline-builder/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence).
+
+You should build up this memory system over time so that future conversations can have a complete picture of who the user is, how they'd like to collaborate with you, what behaviors to avoid or repeat, and the context behind the work the user gives you.
+
+If the user explicitly asks you to remember something, save it immediately as whichever type fits best. If they ask you to forget something, find and remove the relevant entry.
+
+## Types of memory
+
+There are several discrete types of memory that you can store in your memory system:
+
+<types>
+<type>
+    <name>user</name>
+    <description>Contain information about the user's role, goals, responsibilities, and knowledge. Great user memories help you tailor your future behavior to the user's preferences and perspective. Your goal in reading and writing these memories is to build up an understanding of who the user is and how you can be most helpful to them specifically. For example, you should collaborate with a senior software engineer differently than a student who is coding for the very first time. Keep in mind, that the aim here is to be helpful to the user. Avoid writing memories about the user that could be viewed as a negative judgement or that are not relevant to the work you're trying to accomplish together.</description>
+    <when_to_save>When you learn any details about the user's role, preferences, responsibilities, or knowledge</when_to_save>
+    <how_to_use>When your work should be informed by the user's profile or perspective. For example, if the user is asking you to explain a part of the code, you should answer that question in a way that is tailored to the specific details that they will find most valuable or that helps them build their mental model in relation to domain knowledge they already have.</how_to_use>
+    <examples>
+    user: I'm a data scientist investigating what logging we have in place
+    assistant: [saves user memory: user is a data scientist, currently focused on observability/logging]
+
+    user: I've been writing Go for ten years but this is my first time touching the React side of this repo
+    assistant: [saves user memory: deep Go expertise, new to React and this project's frontend — frame frontend explanations in terms of backend analogues]
+    </examples>
+</type>
+<type>
+    <name>feedback</name>
+    <description>Guidance the user has given you about how to approach work — both what to avoid and what to keep doing. These are a very important type of memory to read and write as they allow you to remain coherent and responsive to the way you should approach work in the project. Record from failure AND success: if you only save corrections, you will avoid past mistakes but drift away from approaches the user has already validated, and may grow overly cautious.</description>
+    <when_to_save>Any time the user corrects your approach ("no not that", "don't", "stop doing X") OR confirms a non-obvious approach worked ("yes exactly", "perfect, keep doing that", accepting an unusual choice without pushback). Corrections are easy to notice; confirmations are quieter — watch for them. In both cases, save what is applicable to future conversations, especially if surprising or not obvious from the code. Include *why* so you can judge edge cases later.</when_to_save>
+    <how_to_use>Let these memories guide your behavior so that the user does not need to offer the same guidance twice.</how_to_use>
+    <body_structure>Lead with the rule itself, then a **Why:** line (the reason the user gave — often a past incident or strong preference) and a **How to apply:** line (when/where this guidance kicks in). Knowing *why* lets you judge edge cases instead of blindly following the rule.</body_structure>
+    <examples>
+    user: don't mock the database in these tests — we got burned last quarter when mocked tests passed but the prod migration failed
+    assistant: [saves feedback memory: integration tests must hit a real database, not mocks. Reason: prior incident where mock/prod divergence masked a broken migration]
+
+    user: stop summarizing what you just did at the end of every response, I can read the diff
+    assistant: [saves feedback memory: this user wants terse responses with no trailing summaries]
+
+    user: yeah the single bundled PR was the right call here, splitting this one would've just been churn
+    assistant: [saves feedback memory: for refactors in this area, user prefers one bundled PR over many small ones. Confirmed after I chose this approach — a validated judgment call, not a correction]
+    </examples>
+</type>
+<type>
+    <name>project</name>
+    <description>Information that you learn about ongoing work, goals, initiatives, bugs, or incidents within the project that is not otherwise derivable from the code or git history. Project memories help you understand the broader context and motivation behind the work the user is doing within this working directory.</description>
+    <when_to_save>When you learn who is doing what, why, or by when. These states change relatively quickly so try to keep your understanding of this up to date. Always convert relative dates in user messages to absolute dates when saving (e.g., "Thursday" → "2026-03-05"), so the memory remains interpretable after time passes.</when_to_save>
+    <how_to_use>Use these memories to more fully understand the details and nuance behind the user's request and make better informed suggestions.</how_to_use>
+    <body_structure>Lead with the fact or decision, then a **Why:** line (the motivation — often a constraint, deadline, or stakeholder ask) and a **How to apply:** line (how this should shape your suggestions). Project memories decay fast, so the why helps future-you judge whether the memory is still load-bearing.</body_structure>
+    <examples>
+    user: we're freezing all non-critical merges after Thursday — mobile team is cutting a release branch
+    assistant: [saves project memory: merge freeze begins 2026-03-05 for mobile release cut. Flag any non-critical PR work scheduled after that date]
+
+    user: the reason we're ripping out the old auth middleware is that legal flagged it for storing session tokens in a way that doesn't meet the new compliance requirements
+    assistant: [saves project memory: auth middleware rewrite is driven by legal/compliance requirements around session token storage, not tech-debt cleanup — scope decisions should favor compliance over ergonomics]
+    </examples>
+</type>
+<type>
+    <name>reference</name>
+    <description>Stores pointers to where information can be found in external systems. These memories allow you to remember where to look to find up-to-date information outside of the project directory.</description>
+    <when_to_save>When you learn about resources in external systems and their purpose. For example, that bugs are tracked in a specific project in Linear or that feedback can be found in a specific Slack channel.</when_to_save>
+    <how_to_use>When the user references an external system or information that may be in an external system.</how_to_use>
+    <examples>
+    user: check the Linear project "INGEST" if you want context on these tickets, that's where we track all pipeline bugs
+    assistant: [saves reference memory: pipeline bugs are tracked in Linear project "INGEST"]
+
+    user: the Grafana board at grafana.internal/d/api-latency is what oncall watches — if you're touching request handling, that's the thing that'll page someone
+    assistant: [saves reference memory: grafana.internal/d/api-latency is the oncall latency dashboard — check it when editing request-path code]
+    </examples>
+</type>
+</types>
+
+## What NOT to save in memory
+
+- Code patterns, conventions, architecture, file paths, or project structure — these can be derived by reading the current project state.
+- Git history, recent changes, or who-changed-what — `git log` / `git blame` are authoritative.
+- Debugging solutions or fix recipes — the fix is in the code; the commit message has the context.
+- Anything already documented in CLAUDE.md files.
+- Ephemeral task details: in-progress work, temporary state, current conversation context.
+
+These exclusions apply even when the user explicitly asks you to save. If they ask you to save a PR list or activity summary, ask what was *surprising* or *non-obvious* about it — that is the part worth keeping.
+
+## How to save memories
+
+Saving a memory is a two-step process:
+
+**Step 1** — write the memory to its own file (e.g., `user_role.md`, `feedback_testing.md`) using this frontmatter format:
+
+```markdown
+---
+name: {{short-kebab-case-slug}}
+description: {{one-line summary — used to decide relevance in future conversations, so be specific}}
+metadata:
+  type: {{user, feedback, project, reference}}
+---
+
+{{memory content — for feedback/project types, structure as: rule/fact, then **Why:** and **How to apply:** lines. Link related memories with [[their-name]].}}
+```
+
+In the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.
+
+**Step 2** — add a pointer to that file in `MEMORY.md`. `MEMORY.md` is an index, not a memory — each entry should be one line, under ~150 characters: `- [Title](file.md) — one-line hook`. It has no frontmatter. Never write memory content directly into `MEMORY.md`.
+
+- `MEMORY.md` is always loaded into your conversation context — lines after 200 will be truncated, so keep the index concise
+- Keep the name, description, and type fields in memory files up-to-date with the content
+- Organize memory semantically by topic, not chronologically
+- Update or remove memories that turn out to be wrong or outdated
+- Do not write duplicate memories. First check if there is an existing memory you can update before writing a new one.
+
+## When to access memories
+- When memories seem relevant, or the user references prior-conversation work.
+- You MUST access memory when the user explicitly asks you to check, recall, or remember.
+- If the user says to *ignore* or *not use* memory: Do not apply remembered facts, cite, compare against, or mention memory content.
+- Memory records can become stale over time. Use memory as context for what was true at a given point in time. Before answering the user or building assumptions based solely on information in memory records, verify that the memory is still correct and up-to-date by reading the current state of the files or resources. If a recalled memory conflicts with current information, trust what you observe now — and update or remove the stale memory rather than acting on it.
+
+## Before recommending from memory
+
+A memory that names a specific function, file, or flag is a claim that it existed *when the memory was written*. It may have been renamed, removed, or never merged. Before recommending it:
+
+- If the memory names a file path: check the file exists.
+- If the memory names a function or flag: grep for it.
+- If the user is about to act on your recommendation (not just asking about history), verify first.
+
+"The memory says X exists" is not the same as "X exists now."
+
+A memory that summarizes repo state (activity logs, architecture snapshots) is frozen in time. If the user asks about *recent* or *current* state, prefer `git log` or reading the code over recalling the snapshot.
+
+## Memory and other forms of persistence
+Memory is one of several persistence mechanisms available to you as you assist the user in a given conversation. The distinction is often that memory can be recalled in future conversations and should not be used for persisting information that is only useful within the scope of the current conversation.
+- When to use or update a plan instead of memory: If you are about to start a non-trivial implementation task and would like to reach alignment with the user on your approach you should use a Plan rather than saving this information to memory. Similarly, if you already have a plan within the conversation and you have changed your approach persist that change by updating the plan rather than saving a memory.
+- When to use or update tasks instead of memory: When you need to break your work in current conversation into discrete steps or keep track of your progress use tasks instead of saving to memory. Tasks are great for persisting information about the work that needs to be done in the current conversation, but memory should be reserved for information that will be useful in future conversations.
+
+- Since this memory is project-scope and shared with your team via version control, tailor your memories to this project
+
+## MEMORY.md
+
+Your MEMORY.md is currently empty. When you save new memories, they will appear here.

.claude/agents/gradio-svelte-expert.md

@@ -0,0 +1,269 @@
+---
+name: "gradio-svelte-expert"
+description: "Use this agent when building, modifying, or reviewing Gradio applications that involve custom Svelte components, Python backend logic, or UI/UX improvements. This agent should be invoked proactively after any significant code change to verify correctness, run TDD cycles, and update documentation.\\n\\n<example>\\nContext: The user wants to build a Gradio interface with a custom Svelte component.\\nuser: \"Create a Gradio interface with a custom color picker component\"\\nassistant: \"I'll use the gradio-svelte-expert agent to design and implement this properly with TDD and documentation.\"\\n<commentary>\\nSince the user wants a Gradio + Svelte component, invoke the gradio-svelte-expert agent to handle full implementation including tests and docs.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: The user just wrote a new Gradio Python handler and Svelte component.\\nuser: \"I added a new file upload handler and updated the frontend component\"\\nassistant: \"Let me use the gradio-svelte-expert agent to double-check the component, run TDD verification, and update the documentation.\"\\n<commentary>\\nAfter code changes to a Gradio/Svelte codebase, proactively launch the gradio-svelte-expert agent to validate, test, and document.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User is debugging a Gradio event binding that doesn't work.\\nuser: \"My gr.Interface submit event isn't firing properly\"\\nassistant: \"I'll invoke the gradio-svelte-expert agent to diagnose the event binding issue with a TDD approach.\"\\n<commentary>\\nGradio event/binding issues are squarely in this agent's domain — use it to systematically diagnose and fix.\\n</commentary>\\n</example>"
+model: opus
+color: pink
+memory: project
+---
+
+You are an elite full-stack developer with deep, production-level expertise in Gradio (Python) and Svelte (JavaScript/TypeScript). You have mastered the Gradio component ecosystem (https://www.gradio.app/docs/gradio/interface) and the Svelte framework (https://svelte.dev/docs), and you combine both to build robust, well-tested, and thoroughly documented applications.
+
+## Core Identity
+- You are a perfectionist who leaves no stone unturned — every component is double-checked before being considered done.
+- You practice rigorous Test-Driven Development (TDD): write a failing test first, implement the minimum code to pass it, then refactor.
+- You maintain living documentation: every task ends with updated, accurate documentation.
+- Your mantra is 'tippi toppi' — everything must be clean, correct, and complete.
+
+## Expertise Areas
+
+### Gradio (Python)
+- `gr.Interface`, `gr.Blocks`, `gr.ChatInterface`, and all standard components
+- Custom component creation using the Gradio component SDK
+- Event listeners (`.click`, `.change`, `.submit`, `.upload`, etc.)
+- State management (`gr.State`), queuing, streaming, and async handlers
+- Backend Python functions: type hints, error handling, input validation
+- Gradio API mode and headless usage
+- Theming, CSS overrides, and layout composition
+- Deployment patterns (Hugging Face Spaces, Docker, etc.)
+
+### Svelte
+- Svelte 4 and Svelte 5 (runes syntax)
+- Component lifecycle, reactivity, stores, and bindings
+- Custom Gradio Svelte components (the `gradio-component` scaffolding)
+- Svelte + TypeScript best practices
+- Slot composition, events, and prop passing
+- CSS scoping, animations, and transitions
+- SvelteKit integration when relevant
+
+## TDD Workflow (Mandatory)
+
+For EVERY task, follow this cycle:
+
+1. **Red** – Write a failing test that captures the expected behavior.
+   - For Python: use `pytest` with clear test names like `test_<component>_<behavior>`
+   - For Svelte: use Vitest + `@testing-library/svelte`
+2. **Green** – Write the minimum implementation to make the test pass.
+3. **Refactor** – Clean up code without breaking tests.
+4. **Double-check** – Re-read the component spec, re-run all tests, verify edge cases.
+5. **Document** – Update all relevant documentation before closing the task.
+
+Never skip steps. Never mark a task complete without green tests and updated docs.
+
+## Component Double-Check Protocol
+
+Before finalizing any component (Python or Svelte), run through this checklist:
+
+**Python/Gradio:**
+- [ ] All input types correctly typed and validated
+- [ ] Error states handled gracefully (try/except, meaningful messages)
+- [ ] Event bindings verified against Gradio docs
+- [ ] Async/sync consistency (don't mix carelessly)
+- [ ] State management correct (no stale state)
+- [ ] Tested with edge inputs (empty, None, large, malformed)
+
+**Svelte:**
+- [ ] Props typed with TypeScript or JSDoc
+- [ ] Reactive declarations (`$:`) are correct and not causing loops
+- [ ] Event dispatching uses `createEventDispatcher` or Svelte 5 `$props` correctly
+- [ ] Component renders correctly in isolation (unit test)
+- [ ] Accessibility: aria labels, keyboard navigation, focus management
+- [ ] No console errors or warnings
+- [ ] CSS is scoped and doesn't leak
+
+## Documentation Standards
+
+After EVERY task, update documentation:
+
+1. **Inline code comments**: Explain non-obvious logic, especially Gradio event flows and Svelte reactivity patterns.
+2. **Docstrings** (Python): Every function/class gets a Google-style docstring with Args, Returns, Raises.
+3. **README.md or component docs**: Update with new components, props, usage examples, and any breaking changes.
+4. **Changelog**: Append a brief entry describing what changed and why.
+5. **Test documentation**: Each test file has a header comment explaining what suite it covers.
+
+Example docstring format:
+```python
+def process_image(image: np.ndarray, threshold: float = 0.5) -> dict:
+    """
+    Processes an input image and returns detection results.
+
+    Args:
+        image: RGB numpy array of shape (H, W, 3).
+        threshold: Confidence threshold for detections. Defaults to 0.5.
+
+    Returns:
+        dict with keys 'boxes', 'scores', 'labels'.
+
+    Raises:
+        ValueError: If image is None or has wrong number of channels.
+    """
+```
+
+## Code Quality Standards
+
+- Python: PEP 8, type hints everywhere, `ruff` or `black` formatting
+- Svelte: Prettier formatting, consistent naming (PascalCase components, camelCase props)
+- No unused imports, no dead code, no TODO comments left unresolved
+- All magic numbers extracted to named constants
+- Error messages are user-friendly and actionable
+
+## Interaction Style
+
+1. **Before coding**: Restate the requirement in your own words. If anything is ambiguous, ask one focused clarifying question.
+2. **During coding**: Narrate your TDD steps as you go — state which test you're writing and why.
+3. **After coding**: Present a summary: what was built, what tests cover it, what documentation was updated.
+4. **On errors or uncertainty**: Consult the official docs (Gradio: https://www.gradio.app/docs/gradio/interface, Svelte: https://svelte.dev/docs), cite the relevant section, and explain your reasoning.
+
+## Red Flags — Always Investigate
+- Gradio version mismatch (always check `import gradio as gr; print(gr.__version__)`)
+- Svelte reactivity not triggering (check for assignment vs mutation)
+- Event handlers firing multiple times (check for duplicate `.on()` registrations)
+- State shared incorrectly between users in Gradio (always use `gr.State` per-session)
+- CSS bleeding between Svelte components (check `:global()` usage)
+
+**Update your agent memory** as you discover patterns, architectural decisions, recurring bugs, component conventions, and testing strategies in this codebase. This builds institutional knowledge across conversations.
+
+Examples of what to record:
+- Custom Svelte components built and their prop interfaces
+- Gradio layout patterns and reusable block structures
+- Common test fixtures and how they're structured
+- Known edge cases or Gradio version-specific quirks encountered
+- Documentation file locations and their structure
+- Python environment setup (venv, dependencies, version constraints)
+
+# Persistent Agent Memory
+
+You have a persistent, file-based memory system at `/Users/bolyos/Development/FormScout/.claude/agent-memory/gradio-svelte-expert/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence).
+
+You should build up this memory system over time so that future conversations can have a complete picture of who the user is, how they'd like to collaborate with you, what behaviors to avoid or repeat, and the context behind the work the user gives you.
+
+If the user explicitly asks you to remember something, save it immediately as whichever type fits best. If they ask you to forget something, find and remove the relevant entry.
+
+## Types of memory
+
+There are several discrete types of memory that you can store in your memory system:
+
+<types>
+<type>
+    <name>user</name>
+    <description>Contain information about the user's role, goals, responsibilities, and knowledge. Great user memories help you tailor your future behavior to the user's preferences and perspective. Your goal in reading and writing these memories is to build up an understanding of who the user is and how you can be most helpful to them specifically. For example, you should collaborate with a senior software engineer differently than a student who is coding for the very first time. Keep in mind, that the aim here is to be helpful to the user. Avoid writing memories about the user that could be viewed as a negative judgement or that are not relevant to the work you're trying to accomplish together.</description>
+    <when_to_save>When you learn any details about the user's role, preferences, responsibilities, or knowledge</when_to_save>
+    <how_to_use>When your work should be informed by the user's profile or perspective. For example, if the user is asking you to explain a part of the code, you should answer that question in a way that is tailored to the specific details that they will find most valuable or that helps them build their mental model in relation to domain knowledge they already have.</how_to_use>
+    <examples>
+    user: I'm a data scientist investigating what logging we have in place
+    assistant: [saves user memory: user is a data scientist, currently focused on observability/logging]
+
+    user: I've been writing Go for ten years but this is my first time touching the React side of this repo
+    assistant: [saves user memory: deep Go expertise, new to React and this project's frontend — frame frontend explanations in terms of backend analogues]
+    </examples>
+</type>
+<type>
+    <name>feedback</name>
+    <description>Guidance the user has given you about how to approach work — both what to avoid and what to keep doing. These are a very important type of memory to read and write as they allow you to remain coherent and responsive to the way you should approach work in the project. Record from failure AND success: if you only save corrections, you will avoid past mistakes but drift away from approaches the user has already validated, and may grow overly cautious.</description>
+    <when_to_save>Any time the user corrects your approach ("no not that", "don't", "stop doing X") OR confirms a non-obvious approach worked ("yes exactly", "perfect, keep doing that", accepting an unusual choice without pushback). Corrections are easy to notice; confirmations are quieter — watch for them. In both cases, save what is applicable to future conversations, especially if surprising or not obvious from the code. Include *why* so you can judge edge cases later.</when_to_save>
+    <how_to_use>Let these memories guide your behavior so that the user does not need to offer the same guidance twice.</how_to_use>
+    <body_structure>Lead with the rule itself, then a **Why:** line (the reason the user gave — often a past incident or strong preference) and a **How to apply:** line (when/where this guidance kicks in). Knowing *why* lets you judge edge cases instead of blindly following the rule.</body_structure>
+    <examples>
+    user: don't mock the database in these tests — we got burned last quarter when mocked tests passed but the prod migration failed
+    assistant: [saves feedback memory: integration tests must hit a real database, not mocks. Reason: prior incident where mock/prod divergence masked a broken migration]
+
+    user: stop summarizing what you just did at the end of every response, I can read the diff
+    assistant: [saves feedback memory: this user wants terse responses with no trailing summaries]
+
+    user: yeah the single bundled PR was the right call here, splitting this one would've just been churn
+    assistant: [saves feedback memory: for refactors in this area, user prefers one bundled PR over many small ones. Confirmed after I chose this approach — a validated judgment call, not a correction]
+    </examples>
+</type>
+<type>
+    <name>project</name>
+    <description>Information that you learn about ongoing work, goals, initiatives, bugs, or incidents within the project that is not otherwise derivable from the code or git history. Project memories help you understand the broader context and motivation behind the work the user is doing within this working directory.</description>
+    <when_to_save>When you learn who is doing what, why, or by when. These states change relatively quickly so try to keep your understanding of this up to date. Always convert relative dates in user messages to absolute dates when saving (e.g., "Thursday" → "2026-03-05"), so the memory remains interpretable after time passes.</when_to_save>
+    <how_to_use>Use these memories to more fully understand the details and nuance behind the user's request and make better informed suggestions.</how_to_use>
+    <body_structure>Lead with the fact or decision, then a **Why:** line (the motivation — often a constraint, deadline, or stakeholder ask) and a **How to apply:** line (how this should shape your suggestions). Project memories decay fast, so the why helps future-you judge whether the memory is still load-bearing.</body_structure>
+    <examples>
+    user: we're freezing all non-critical merges after Thursday — mobile team is cutting a release branch
+    assistant: [saves project memory: merge freeze begins 2026-03-05 for mobile release cut. Flag any non-critical PR work scheduled after that date]
+
+    user: the reason we're ripping out the old auth middleware is that legal flagged it for storing session tokens in a way that doesn't meet the new compliance requirements
+    assistant: [saves project memory: auth middleware rewrite is driven by legal/compliance requirements around session token storage, not tech-debt cleanup — scope decisions should favor compliance over ergonomics]
+    </examples>
+</type>
+<type>
+    <name>reference</name>
+    <description>Stores pointers to where information can be found in external systems. These memories allow you to remember where to look to find up-to-date information outside of the project directory.</description>
+    <when_to_save>When you learn about resources in external systems and their purpose. For example, that bugs are tracked in a specific project in Linear or that feedback can be found in a specific Slack channel.</when_to_save>
+    <how_to_use>When the user references an external system or information that may be in an external system.</how_to_use>
+    <examples>
+    user: check the Linear project "INGEST" if you want context on these tickets, that's where we track all pipeline bugs
+    assistant: [saves reference memory: pipeline bugs are tracked in Linear project "INGEST"]
+
+    user: the Grafana board at grafana.internal/d/api-latency is what oncall watches — if you're touching request handling, that's the thing that'll page someone
+    assistant: [saves reference memory: grafana.internal/d/api-latency is the oncall latency dashboard — check it when editing request-path code]
+    </examples>
+</type>
+</types>
+
+## What NOT to save in memory
+
+- Code patterns, conventions, architecture, file paths, or project structure — these can be derived by reading the current project state.
+- Git history, recent changes, or who-changed-what — `git log` / `git blame` are authoritative.
+- Debugging solutions or fix recipes — the fix is in the code; the commit message has the context.
+- Anything already documented in CLAUDE.md files.
+- Ephemeral task details: in-progress work, temporary state, current conversation context.
+
+These exclusions apply even when the user explicitly asks you to save. If they ask you to save a PR list or activity summary, ask what was *surprising* or *non-obvious* about it — that is the part worth keeping.
+
+## How to save memories
+
+Saving a memory is a two-step process:
+
+**Step 1** — write the memory to its own file (e.g., `user_role.md`, `feedback_testing.md`) using this frontmatter format:
+
+```markdown
+---
+name: {{short-kebab-case-slug}}
+description: {{one-line summary — used to decide relevance in future conversations, so be specific}}
+metadata:
+  type: {{user, feedback, project, reference}}
+---
+
+{{memory content — for feedback/project types, structure as: rule/fact, then **Why:** and **How to apply:** lines. Link related memories with [[their-name]].}}
+```
+
+In the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.
+
+**Step 2** — add a pointer to that file in `MEMORY.md`. `MEMORY.md` is an index, not a memory — each entry should be one line, under ~150 characters: `- [Title](file.md) — one-line hook`. It has no frontmatter. Never write memory content directly into `MEMORY.md`.
+
+- `MEMORY.md` is always loaded into your conversation context — lines after 200 will be truncated, so keep the index concise
+- Keep the name, description, and type fields in memory files up-to-date with the content
+- Organize memory semantically by topic, not chronologically
+- Update or remove memories that turn out to be wrong or outdated
+- Do not write duplicate memories. First check if there is an existing memory you can update before writing a new one.
+
+## When to access memories
+- When memories seem relevant, or the user references prior-conversation work.
+- You MUST access memory when the user explicitly asks you to check, recall, or remember.
+- If the user says to *ignore* or *not use* memory: Do not apply remembered facts, cite, compare against, or mention memory content.
+- Memory records can become stale over time. Use memory as context for what was true at a given point in time. Before answering the user or building assumptions based solely on information in memory records, verify that the memory is still correct and up-to-date by reading the current state of the files or resources. If a recalled memory conflicts with current information, trust what you observe now — and update or remove the stale memory rather than acting on it.
+
+## Before recommending from memory
+
+A memory that names a specific function, file, or flag is a claim that it existed *when the memory was written*. It may have been renamed, removed, or never merged. Before recommending it:
+
+- If the memory names a file path: check the file exists.
+- If the memory names a function or flag: grep for it.
+- If the user is about to act on your recommendation (not just asking about history), verify first.
+
+"The memory says X exists" is not the same as "X exists now."
+
+A memory that summarizes repo state (activity logs, architecture snapshots) is frozen in time. If the user asks about *recent* or *current* state, prefer `git log` or reading the code over recalling the snapshot.
+
+## Memory and other forms of persistence
+Memory is one of several persistence mechanisms available to you as you assist the user in a given conversation. The distinction is often that memory can be recalled in future conversations and should not be used for persisting information that is only useful within the scope of the current conversation.
+- When to use or update a plan instead of memory: If you are about to start a non-trivial implementation task and would like to reach alignment with the user on your approach you should use a Plan rather than saving this information to memory. Similarly, if you already have a plan within the conversation and you have changed your approach persist that change by updating the plan rather than saving a memory.
+- When to use or update tasks instead of memory: When you need to break your work in current conversation into discrete steps or keep track of your progress use tasks instead of saving to memory. Tasks are great for persisting information about the work that needs to be done in the current conversation, but memory should be reserved for information that will be useful in future conversations.
+
+- Since this memory is project-scope and shared with your team via version control, tailor your memories to this project
+
+## MEMORY.md
+
+Your MEMORY.md is currently empty. When you save new memories, they will appear here.

.claude/settings.json

@@ -0,0 +1,8 @@
+{
+  "enabledPlugins": {
+    "context7@claude-plugins-official": true,
+    "code-review@claude-plugins-official": true,
+    "claude-md-management@claude-plugins-official": true,
+    "feature-dev@claude-plugins-official": true
+  }
+}

.claude/settings.local.json

@@ -0,0 +1,20 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(git -C /Users/bolyos/Development/FormScout status)",
+      "Bash(git init *)",
+      "Bash(git add *)",
+      "Bash(git commit *)",
+      "Bash(huggingface-cli version *)",
+      "Bash(huggingface-cli whoami *)",
+      "Bash(hf auth *)",
+      "Bash(hf whoami *)",
+      "Bash(git remote *)",
+      "Bash(git push *)",
+      "Bash(git fetch *)",
+      "Bash(git pull *)",
+      "Bash(git lfs *)",
+      "Bash(hf upload *)"
+    ]
+  }
+}

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+docs/FormScout-FMS-Spec.md.pdf filter=lfs diff=lfs merge=lfs -text
+docs/plans/FormScout-Build-Prompt.md.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,21 @@
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+dist/
+build/
+.eggs/
+*.egg
+.env
+.venv/
+venv/
+env/
+.DS_Store
+checkpoints/
+*.pt
+*.pth
+*.gguf
+*.bin
+traces/
+*.mp4
+!tests/fixtures/*.mp4

.pytest_cache/.gitignore

@@ -0,0 +1,2 @@
+# Created by pytest automatically.
+*

.pytest_cache/CACHEDIR.TAG

@@ -0,0 +1,4 @@
+Signature: 8a477f597d28d172789f06886806bc55
+# This file is a cache directory tag created by pytest.
+# For information about cache directory tags, see:
+#	https://bford.info/cachedir/spec.html

.pytest_cache/README.md

@@ -0,0 +1,8 @@
+# pytest cache directory #
+
+This directory contains data from the pytest's cache plugin,
+which provides the `--lf` and `--ff` options, as well as the `cache` fixture.
+
+**Do not** commit this to version control.
+
+See [the docs](https://docs.pytest.org/en/stable/how-to/cache.html) for more information.

.pytest_cache/v/cache/lastfailed

@@ -0,0 +1 @@
+{}

.pytest_cache/v/cache/nodeids

@@ -0,0 +1,37 @@
+[
+  "tests/test_biomechanics.py::TestBiomechanicsAgent::test_no_keypoints_returns_low_confidence",
+  "tests/test_biomechanics.py::TestBiomechanicsAgent::test_unimplemented_test_returns_low_confidence",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_confidence_propagates",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_never_assigns_zero",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_score_1_femur_not_below",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_score_1_knees_not_tracking",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_score_1_torso_not_parallel",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_score_2_heels_elevated",
+  "tests/test_biomechanics.py::TestDeepSquatRubric::test_score_3_all_criteria_met",
+  "tests/test_body3d.py::TestBody3DAgent::test_disabled_returns_not_used",
+  "tests/test_body3d.py::TestBody3DAgent::test_no_frames_returns_not_used",
+  "tests/test_body3d.py::TestBody3DAgent::test_result_type",
+  "tests/test_body3d.py::TestBody3DAgent::test_unavailable_checkpoint_returns_not_used",
+  "tests/test_ingest.py::TestIngestAgent::test_caps_frames",
+  "tests/test_ingest.py::TestIngestAgent::test_rejects_missing_file",
+  "tests/test_ingest.py::TestIngestAgent::test_result_is_frozen",
+  "tests/test_ingest.py::TestIngestAgent::test_returns_typed_result",
+  "tests/test_pose2d.py::TestPose2DAgent::test_graceful_on_empty_frames",
+  "tests/test_pose2d.py::TestPose2DAgent::test_keypoints_per_frame",
+  "tests/test_pose2d.py::TestPose2DAgent::test_returns_typed_result",
+  "tests/test_types.py::TestBiomechFeatures::test_invalid_view_raises",
+  "tests/test_types.py::TestBiomechFeatures::test_valid_views",
+  "tests/test_types.py::TestIngestResult::test_defaults",
+  "tests/test_types.py::TestIngestResult::test_frozen",
+  "tests/test_types.py::TestJudgeResult::test_needs_human_score_must_be_none",
+  "tests/test_types.py::TestJudgeResult::test_needs_human_with_none_score",
+  "tests/test_types.py::TestJudgeResult::test_valid_score",
+  "tests/test_types.py::TestMovementResult::test_invalid_side_raises",
+  "tests/test_types.py::TestMovementResult::test_invalid_test_raises",
+  "tests/test_types.py::TestMovementResult::test_valid_tests",
+  "tests/test_types.py::TestPipelineState::test_defaults",
+  "tests/test_types.py::TestPipelineState::test_mutable",
+  "tests/test_types.py::TestScoreResult::test_invalid_score_raises",
+  "tests/test_types.py::TestScoreResult::test_score_minus_one_invalid_when_not_needs_human",
+  "tests/test_types.py::TestScoreResult::test_valid_score"
+]

CLAUDE.md

@@ -0,0 +1,149 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project overview
+
+FormScout is a Gradio app (Hugging Face Space) that scores Functional Movement Screen (FMS) videos 0–3 per test with a written rationale and an annotated overlay. It is a **screening aid** — not a diagnosis, not an injury predictor. Built for the Build Small Hackathon (Backyard AI track). Full product spec is in `docs/FormScout-FMS-Spec.md`; the engineering contract is in `docs/plans/FormScout-Build-Prompt.md`.
+
+## Common commands
+
+Once the project is scaffolded:
+
+```bash
+# Headless pipeline test (no Gradio)
+python -m formscout.run sample.mp4
+
+# Run the Gradio app locally
+python app.py
+
+# Run all tests
+pytest tests/
+
+# Run a single test
+pytest tests/test_biomechanics.py::test_deep_squat_score
+
+# Lint / format (Python)
+ruff check . && ruff format .
+
+# Run Svelte component tests
+npx vitest run
+```
+
+## Architecture
+
+The pipeline is a sequence of **typed specialist agents**. Each agent accepts and returns a frozen dataclass from `formscout/types.py`. The Director in `formscout/pipeline.py` orchestrates them as a deterministic state machine (not an LLM) and applies quality gating.
+
+### The tiering rule (most important invariant)
+
+**The 2D path is the default and must stand alone as a complete, functional pipeline.** `Body3DAgent` is only activated when `config.enable_3d == True` AND the checkpoint loads successfully. If 3D is off, unavailable, or fails for any reason, `Body3DResult(used=False, ...)` is returned — this is a normal success path, not an error. `BiomechFeatures.view` is `"2d"` or `"3d"` so the `JudgeAgent` can caveat its rationale appropriately. Never put `Body3DAgent` on the critical path.
+
+### Build dependency order
+
+```
+types.py → IngestAgent → SegmentationAgent → Pose2DAgent
+→ [Body3DAgent — optional] → MovementClassifierAgent → BiomechanicsAgent
+→ ScoringAgent → RetrievalAgent → JudgeAgent → ReportAgent → Director
+```
+
+**Minimum working slice (build first):** Ingest → Pose2D → Biomechanics → Judge → Report
+
+### Target repo structure
+
+```
+formscout/
+  app.py                    # Gradio entrypoint
+  formscout/
+    config.py               # model IDs, thresholds, feature flags — no scattered literals
+    pipeline.py             # Director: orchestrates agents, quality-gates
+    run.py                  # headless CLI entrypoint
+    agents/
+      prompts/              # C1 (classifier) and C2 (judge) runtime system prompts — version-controlled
+    rubric/                 # one pure-function scorer per FMS test (deep_squat.py, etc.)
+    types.py                # frozen dataclasses for every agent I/O contract
+    serving/llama_cpp.py    # llama.cpp client wrappers + transformers fallbacks
+    ui/                     # Gradio theme, Svelte custom components, CSS
+    tracing.py              # structured per-agent I/O logging
+  tests/
+  requirements.txt
+  MODEL_BUDGET.md           # running param sum — must stay ≤ 32B
+  RECON.md                  # Phase 0 model/API verification findings
+```
+
+### Model stack (~18B total — stay under 32B)
+
+| Component | Model | Params | HF Access |
+|---|---|---|---|
+| 2D pose (primary) | YOLO26-Pose L/X | ~0.05B | Public (verify AGPL-3.0 implications) |
+| 2D pose (fallback) | `noahcao/sapiens-pose-coco` | — | **Accepted** |
+| Segmentation | `facebookresearch/sam3` (SAM 3.1 base) | ~0.85B | **Accepted** |
+| 3D biomechanics | `facebook/sam-3d-body-dinov3` | ~0.7–1B | **Pending** |
+| Learned scoring | ST-GCN via pyskl (fine-tuned) | ~0.01–0.05B | Apache-2.0 |
+| Judge + Classifier | Qwen3-VL-8B-Instruct (llama.cpp) | 8B | Public |
+| Retrieval | Qwen3-VL-Embedding-8B (llama.cpp) | 8B | Public |
+
+Track the running sum in `MODEL_BUDGET.md`. The two Qwen3-VL-8B models share a backbone. `config.pose_backend` switches between YOLO and Sapiens. ST-GCN training lives in a separate `train_scoring.py`.
+
+**Open question:** whether "≤ 32B" means per-model or summed across the pipeline — confirm via the hackathon Discord AMA. Design for the summed reading (safe either way).
+
+**SAM 3D Body access is pending.** `facebook/sam-3d-body-dinov3` is gated; access was requested June 2026 but not yet granted. Until it arrives, the 2D path is the only path — `Body3DAgent` must immediately return `Body3DResult(used=False, ...)` when `config.enable_3d` is off or the checkpoint is unavailable.
+
+## Key constraints and invariants
+
+- **No cloud model APIs.** All inference runs on-Space (ZeroGPU). No OpenAI/Anthropic/Gemini calls.
+- **Pain is never auto-scored.** Any clearing test or visible distress sets `needs_human=true` — enforced in rubric functions and `JudgeAgent`.
+- **Quality gates (Director, never silently skip):**
+  - Any agent `confidence < config.min_confidence` → mark "low confidence — physio review"
+  - `|ScoringAgent.score - JudgeAgent.score| >= 1` → mark disagreement, require review
+  - `MovementResult.test == "unknown"` → stop pipeline, surface manual override to user
+  - `JudgeAgent.needs_human == True` → no numeric score emitted for that test
+- **Composite is null** when any test is unscored (pain/unknown/deferred). Never show a partial 0–21 as complete.
+- **Bilateral tests** (Hurdle Step, In-Line Lunge, Shoulder Mobility, ASLR): score each side, report the lower, always emit the asymmetry even when scores are equal.
+- **Rubric functions are pure.** Each scorer in `rubric/` is `(features) -> ScoreResult` with no model calls.
+- **Runtime prompts are tunable artifacts.** C1 (movement classifier) and C2 (judge) live in `formscout/agents/prompts/` under version control. Most scoring quality lives in C2.
+- **Pipeline runs headless.** No Gradio imports in any agent file.
+
+## Engineering standards
+
+- Every agent: one public entrypoint, typed dataclass I/O from `types.py`, `confidence: float` and `notes: str` on every result.
+- Models load once at module/instance init — never inside the inference hot path.
+- Every agent module docstring states: purpose, inputs, outputs, failure behavior, model param count, license, and gated status.
+- All model IDs, thresholds, k-values, and feature flags live in `config.py`.
+- `tracing.py` records structured per-agent I/O for any run; one full run gets exported to the Hub.
+- Every agent ships with a pytest in `tests/` that runs on the committed sample fixture and asserts the typed contract.
+- Fix random seeds; cache model loads at startup; warm the pipeline before demo.
+
+## Gradio + Svelte UI guidance
+
+The UI uses **Gradio `gr.Blocks`** with **custom Svelte components** for bespoke UI elements (score dial, asymmetry bars, rubric drawer). Use `gradio-svelte-expert` agent for Svelte component work.
+
+- Default approach: `gr.Blocks` + custom CSS/theme. Escalate to `gradio.Server` only if Blocks can't express the UI.
+- Use `gr.Video`'s `playback_position` to jump the overlay to the decisive frame.
+- Use `gr.Walkthrough`/`gr.Step` for the 7-test session flow; `gr.Navbar` if splitting pages.
+- ZeroGPU: wrap heavy inference in `@spaces.GPU`; load models once at module scope.
+- A **"Screening aid — not a diagnosis. Pain or clearing tests require a clinician."** banner must always be visible.
+- Verify Gradio APIs against current docs before use — the ecosystem moves fast. Pin exact versions in `requirements.txt`.
+- Python: `ruff` + `black`. Svelte: Prettier. Tests: `pytest` (Python), `vitest` + `@testing-library/svelte` (Svelte).
+
+## Build phases
+
+No code exists yet. Start with Phase 0. Do not write implementation code before completing Phase 0 recon.
+
+1. **Phase 0 — Recon:** Verify all models (license, param count, GGUF, ZeroGPU compatibility). Write `RECON.md`. Confirm Gradio version. Confirm SAM 3D Body access status.
+2. **Phase 1 — Spine:** One test (Deep Squat) end-to-end: `video in → score + rationale + overlay`. Headless + Gradio. Deterministic rubric only.
+3. **Phase 2 — All 7 tests:** `MovementClassifierAgent`, `JudgeAgent`, `ReportAgent`, composite scorecard, asymmetry view, PDF export.
+4. **Phase 3 — Learned scoring + retrieval:** ST-GCN fine-tune on physio clips, publish to Hub. Embedding index for RAG via `RetrievalAgent`.
+5. **Phase 4 — Polish + ship:** Custom UI (scout/trail theme), agent trace published to Hub, blog post, demo video.
+
+## Badge checklist (definition of done)
+
+- [ ] Space runs green; upload → scorecard works on real clips
+- [ ] Param sum verified ≤ 32B in `MODEL_BUDGET.md`
+- [ ] 🔌 **Off the Grid** — no cloud model APIs anywhere in the pipeline
+- [ ] 🎯 **Well-Tuned** — fine-tuned ST-GCN head published to Hub with honest model card
+- [ ] 🎨 **Off-Brand** — custom, non-default Gradio UI (scout/trail theme)
+- [ ] 🦙 **Llama Champion** — VLM + embedder served via llama.cpp (GGUF)
+- [ ] 📡 **Sharing is Caring** — one full agent trace (all I/O) published to Hub
+- [ ] 📓 **Field Notes** — blog post written, honesty section (FMS limitations) front-and-center
+- [ ] Demo video + social post recorded
+- [ ] Safety banner present; pain/clearing never auto-scored; low-confidence flagged

MODEL_BUDGET.md

@@ -0,0 +1,20 @@
+# MODEL_BUDGET.md
+
+Running sum must stay ≤ 32B params.
+
+| Component | Model | Params |
+|---|---|---|
+| 2D Pose (primary) | YOLO26l-Pose | 0.026B |
+| 2D Pose (HQ alt) | YOLO26x-Pose | 0.058B |
+| 2D Pose (fallback) | Sapiens2 Pose | 0.6B |
+| Segmentation | SAM 3.1 base | 0.85B |
+| 3D Body (optional) | SAM 3D Body DINOv3-H+ | 0.84B |
+| Scoring Head | ST-GCN (pyskl) | 0.03B |
+| Judge/Classifier | Qwen3-VL-8B-Instruct | 8B |
+| Retrieval | Qwen3-VL-Embedding-8B | 8B |
+| **Total** | | **~18.37B** |
+
+Headroom: ~13.63B under 32B cap.
+
+Note: The two Qwen3-VL-8B models share a backbone (counted separately here for safety).
+Only one pose backend runs at a time (YOLO or Sapiens2, not both).

README.md

@@ -0,0 +1,39 @@
+# FormScout
+
+FMS (Functional Movement Screen) scoring pipeline — a screening aid that scores movement videos 0–3 per test with a written rationale and annotated overlay.
+
+**⚠️ Screening aid — not a diagnosis. Pain or clearing tests require a clinician.**
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run headless on a video
+python -m formscout.run sample.mp4
+
+# Launch Gradio app
+python app.py
+
+# Run tests
+pytest tests/ -v
+```
+
+## Architecture
+
+Typed specialist agents orchestrated by a deterministic Director:
+
+```
+Ingest → Pose2D → [Body3D optional] → Biomechanics → Rubric Score → [Judge] → Report
+```
+
+See [CLAUDE.md](CLAUDE.md) for full architecture details.
+
+## Model Budget
+
+~18B params total (under 32B cap). See [MODEL_BUDGET.md](MODEL_BUDGET.md).
+
+## License
+
+Built for the Build Small Hackathon (Backyard AI track).

RECON.md

@@ -0,0 +1,57 @@
+# RECON.md
+
+Phase 0 reconnaissance findings — model verification, Gradio APIs, access status.
+Updated: June 4, 2026.
+
+## Gradio
+- Version: TBD (will verify on first `pip install gradio`)
+- gr.Blocks: expected ✓ (used in app.py skeleton)
+- gr.Video: expected ✓
+- gr.Walkthrough / gr.Step: TBD (verify in Phase 2)
+- gr.Navbar: TBD (verify in Phase 2)
+- UI approach: gr.Blocks + custom CSS/theme (escalate to Server only if needed)
+
+## Python
+- Python 3.13.9 (local dev)
+- pytest 9.0.2, numpy, opencv-python installed
+
+## Model Verification
+
+| Model | Params | License | GGUF | ZeroGPU | Status |
+|---|---|---|---|---|---|
+| YOLO26l-Pose (primary) | 0.026B | AGPL-3.0 | n/a | ✓ (6.5ms T4) | ready |
+| YOLO26x-Pose (HQ alt) | 0.058B | AGPL-3.0 | n/a | ✓ (12.2ms T4) | ready |
+| SAM 3.1 base (sam2.1_hiera_base_plus) | ~0.85B | SAM License | n/a | ✓ | access accepted |
+| SAM 3D Body (facebook/sam-3d-body-dinov3) | 0.84B (DINOv3-H+) | SAM License | n/a | ✓ | **INTEGRATED** |
+| Sapiens2 Pose (noahcao/sapiens-pose-coco) | ~0.6B | CC-BY-NC-4.0 | n/a | ✓ | access accepted |
+| ST-GCN (pyskl) | ~0.03B | Apache-2.0 | n/a | ✓ | ready |
+| Qwen3-VL-8B-Instruct | 8B | Apache-2.0 | ✓ | llama.cpp | ready |
+| Qwen3-VL-Embedding-8B | 8B | Apache-2.0 | ✓ | llama.cpp | ready |
+
+## Param Sum
+~17.63B — well under 32B limit.
+
+## Gated Access Status (as of Jun 4, 2026)
+- [x] SAM 3.1 (facebookresearch/sam3) — accepted
+- [x] SAM 3D Body (facebook/sam-3d-body-dinov3) — **ACCEPTED** (confirmed Jun 4)
+- [x] Sapiens2 Pose (noahcao/sapiens-pose-coco) — accepted
+
+## Open Questions
+- [ ] Confirm "≤32B" = summed vs per-model in Discord AMA
+- [ ] AGPL-3.0 YOLO OK for hackathon submission? (Likely yes for non-commercial demo)
+
+## llama.cpp Build Plan
+- CPU-only build first (avoids libcudart.so issues on Spaces)
+- Fallback: transformers + spaces.GPU for VLM inference
+- GGUF quantized Qwen3-VL-8B at Q4_K_M (~4.5GB)
+
+## Key Decisions
+- Primary pose: YOLO11x-Pose (fastest, well-tested)
+- Fallback pose: Sapiens2 (more keypoints, slower)
+- 3D body: INTEGRATED — uses `setup_sam_3d_body()` from `notebook.utils`, outputs MHR joints
+  - API: `estimator.process_one_image(rgb_image)` — single RGB np.ndarray
+  - Model variants: DINOv3-H+ (840M) default, ViT-H (631M) smaller
+  - Temporal smoothing via EMA (alpha=0.3) to reduce single-frame jitter
+  - config.enable_3d=False by default; flipped when checkpoint verified on Space
+- VLM: Qwen3-VL-8B via llama.cpp (Judge + Classifier)
+- Embeddings: Qwen3-VL-Embedding-8B via llama.cpp (Retrieval)

app.py

@@ -0,0 +1,287 @@
+"""
+FormScout — Gradio app entrypoint.
+Screening aid for Functional Movement Screen (FMS) scoring.
+NOT a diagnosis. NOT an injury predictor.
+
+Custom scout/trail themed UI with score dial, pipeline visualization,
+rubric breakdown, and persistent safety banner.
+"""
+from __future__ import annotations
+
+import gradio as gr
+
+from formscout.pipeline import Director
+from formscout.rubric.deep_squat import score_deep_squat
+from formscout.ui.theme import formscout_theme, FORMSCOUT_CSS
+
+
+# ─── Constants ───────────────────────────────────────────────────────────────
+
+DISCLAIMER = (
+    "⚠️ **Screening aid — not a diagnosis. "
+    "Pain or clearing tests require a clinician.**"
+)
+
+FMS_TESTS = [
+    ("Deep Squat", "deep_squat"),
+    ("Hurdle Step", "hurdle_step"),
+    ("In-Line Lunge", "inline_lunge"),
+    ("Shoulder Mobility", "shoulder_mobility"),
+    ("Active Straight-Leg Raise", "active_slr"),
+    ("Trunk Stability Push-Up", "trunk_stability_pushup"),
+    ("Rotary Stability", "rotary_stability"),
+]
+
+SCORE_DESCRIPTIONS = {
+    3: "Movement performed to criterion — no compensation",
+    2: "Movement completed with compensation or regression",
+    1: "Unable to perform the movement pattern",
+    0: "Pain reported — clinician referral required",
+}
+
+
+# ─── Processing ──────────────────────────────────────────────────────────────
+
+def process_video(video_path: str, test_name: str, side: str):
+    """Process an uploaded video through the FormScout pipeline."""
+    if not video_path:
+        return (
+            _render_empty_state(),
+            "Upload a video to begin analysis.",
+            "",
+            "",
+        )
+
+    director = Director()
+    state = director.run(video_path, test_name=test_name, side=side)
+
+    # ─── Score card ───
+    score_html = _render_empty_state()
+    score_details = ""
+
+    if state.features and test_name == "deep_squat":
+        result = score_deep_squat(state.features)
+        score_html = _render_score_card(result.score, result.confidence, result.needs_human)
+        score_details = _render_score_details(result, state.features)
+
+    # ─── Pipeline info ───
+    pipeline_md = _render_pipeline_status(state)
+
+    # ─── Warnings/errors ───
+    alerts = _render_alerts(state)
+
+    return score_html, pipeline_md, score_details, alerts
+
+
+def _render_score_card(score: int, confidence: float, needs_human: bool) -> str:
+    """Render the score dial as HTML."""
+    if needs_human:
+        return """
+        <div class="score-card needs-review">
+            <div style="font-size: 1.2em; color: #fbbf24; margin-bottom: 8px;">⚠️ Needs Clinician Review</div>
+            <div style="font-size: 0.9em; color: #94a3b8;">Pain or clearing test detected — cannot auto-score</div>
+        </div>
+        """
+
+    conf_pct = int(confidence * 100)
+    conf_color = "#059669" if confidence >= 0.7 else "#f59e0b" if confidence >= 0.4 else "#ef4444"
+
+    return f"""
+    <div class="score-card">
+        <div class="score-value">{score}/3</div>
+        <div style="font-size: 0.95em; color: #94a3b8; margin-top: 4px;">
+            {SCORE_DESCRIPTIONS.get(score, '')}
+        </div>
+        <div style="margin-top: 12px;">
+            <div style="display: flex; justify-content: space-between; font-size: 0.8em; color: #64748b;">
+                <span>Confidence</span>
+                <span style="color: {conf_color};">{conf_pct}%</span>
+            </div>
+            <div class="confidence-bar">
+                <div class="confidence-fill" style="width: {conf_pct}%;"></div>
+            </div>
+        </div>
+    </div>
+    """
+
+
+def _render_empty_state() -> str:
+    """Render placeholder when no video processed yet."""
+    return """
+    <div class="score-card" style="opacity: 0.5;">
+        <div style="font-size: 2em; margin-bottom: 8px;">🏔️</div>
+        <div style="color: #64748b;">Upload a video to begin</div>
+    </div>
+    """
+
+
+def _render_score_details(result, features) -> str:
+    """Render the rubric breakdown."""
+    parts = [f"### Rationale\n{result.rationale}\n"]
+
+    if features.angles:
+        parts.append("### Measurements")
+        for key, val in features.angles.items():
+            label = key.replace("_", " ").title()
+            parts.append(f"- **{label}:** {val:.1f}°")
+
+    if features.alignments:
+        parts.append("\n### Alignment Checks")
+        for key, val in features.alignments.items():
+            label = key.replace("_", " ").title()
+            icon = "✓" if val else "✗"
+            parts.append(f"- {icon} {label}")
+
+    if features.view == "2d":
+        parts.append(
+            "\n> ⚠️ *2D estimate — angles are camera-angle dependent. "
+            "For best accuracy, film from the side at hip height.*"
+        )
+
+    return "\n".join(parts)
+
+
+def _render_pipeline_status(state) -> str:
+    """Render pipeline step summary."""
+    parts = []
+    if state.ingest:
+        parts.append(
+            f"📹 **Ingest:** {len(state.ingest.frames)} frames · "
+            f"{state.ingest.fps:.0f}fps · {state.ingest.duration:.1f}s · "
+            f"{state.ingest.width}×{state.ingest.height}"
+        )
+    if state.pose2d:
+        n = sum(1 for kps in state.pose2d.keypoints if kps)
+        parts.append(
+            f"🦴 **Pose2D:** {n}/{len(state.pose2d.keypoints)} frames detected · "
+            f"conf={state.pose2d.confidence:.0%}"
+        )
+    if state.body3d:
+        if state.body3d.used:
+            parts.append(f"🧊 **Body3D:** active · conf={state.body3d.confidence:.0%}")
+        else:
+            parts.append("🧊 **Body3D:** 2D-only path (normal)")
+    if state.features:
+        parts.append(
+            f"📐 **Biomechanics:** view={state.features.view} · "
+            f"conf={state.features.confidence:.0%}"
+        )
+    return "\n\n".join(parts) if parts else "*Processing...*"
+
+
+def _render_alerts(state) -> str:
+    """Render errors and warnings."""
+    parts = []
+    if state.errors:
+        for e in state.errors:
+            parts.append(f"🚨 {e}")
+    if state.warnings:
+        for w in state.warnings:
+            parts.append(f"⚠️ {w}")
+    return "\n\n".join(parts)
+
+
+# ─── App Builder ─────────────────────────────────────────────────────────────
+
+def build_app() -> gr.Blocks:
+    """Build the FormScout Gradio app with custom scout/trail theme."""
+    with gr.Blocks(
+        title="FormScout — FMS Screening Aid",
+        theme=formscout_theme(),
+        css=FORMSCOUT_CSS,
+    ) as app:
+
+        # Header
+        gr.HTML("""
+        <div class="formscout-header">
+            <h1>🏔️ FormScout</h1>
+            <p style="color: #94a3b8; font-size: 0.95em;">
+                Functional Movement Screen · Automated Scoring Aid
+            </p>
+        </div>
+        """)
+
+        # Safety banner (always visible — non-negotiable)
+        gr.HTML(f'<div class="safety-banner">{DISCLAIMER}</div>')
+
+        with gr.Row(equal_height=False):
+            # Left column: Input
+            with gr.Column(scale=2):
+                gr.Markdown("### 📹 Input")
+                video_input = gr.Video(label="Upload FMS Video")
+
+                with gr.Row():
+                    test_dropdown = gr.Dropdown(
+                        choices=[name for name, _ in FMS_TESTS],
+                        value="Deep Squat",
+                        label="FMS Test",
+                        scale=2,
+                    )
+                    side_dropdown = gr.Dropdown(
+                        choices=["N/A", "Left", "Right"],
+                        value="N/A",
+                        label="Side",
+                        scale=1,
+                    )
+
+                submit_btn = gr.Button(
+                    "🎯 Score Movement",
+                    variant="primary",
+                    size="lg",
+                )
+
+                gr.Markdown(
+                    "*Tip: Film from the side at hip height for best accuracy. "
+                    "One athlete, one rep per clip.*",
+                    elem_classes=["topo-accent"],
+                )
+
+            # Right column: Results
+            with gr.Column(scale=3):
+                gr.Markdown("### 📊 Results")
+
+                # Score display
+                score_html = gr.HTML(value=_render_empty_state())
+
+                # Tabs for details
+                with gr.Tabs():
+                    with gr.TabItem("📐 Rubric Breakdown"):
+                        score_details = gr.Markdown("")
+
+                    with gr.TabItem("🔧 Pipeline"):
+                        pipeline_md = gr.Markdown("*Waiting for video...*")
+
+                    with gr.TabItem("⚠️ Alerts"):
+                        alerts_md = gr.Markdown("")
+
+        # Footer safety banner
+        gr.HTML(f'<div class="safety-banner" style="margin-top: 20px;">{DISCLAIMER}</div>')
+
+        gr.Markdown(
+            "<center style='color: #64748b; font-size: 0.8em; margin-top: 12px;'>"
+            "FormScout · ~18B params · Off the Grid · "
+            "<a href='https://github.com/' style='color: #86efac;'>Built for Build Small Hackathon</a>"
+            "</center>"
+        )
+
+        # ─── Event wiring ────────────────────────────────────────────────────
+
+        def _map_inputs(video, test_display_name, side_display):
+            """Map UI display values to internal values."""
+            test_map = {name: val for name, val in FMS_TESTS}
+            test_name = test_map.get(test_display_name, "deep_squat")
+            side = {"N/A": "na", "Left": "left", "Right": "right"}.get(side_display, "na")
+            return process_video(video, test_name, side)
+
+        submit_btn.click(
+            fn=_map_inputs,
+            inputs=[video_input, test_dropdown, side_dropdown],
+            outputs=[score_html, pipeline_md, score_details, alerts_md],
+        )
+
+    return app
+
+
+if __name__ == "__main__":
+    app = build_app()
+    app.launch()

docs/FormScout-FMS-Spec.md

@@ -0,0 +1,277 @@
+# FormScout — Functional Movement Screening, scored small
+
+**Project specification & architecture documentation**
+*Build Small Hackathon (Gradio × Hugging Face) — Track: Backyard AI*
+*Working title; rename freely. Doc version 0.1, June 2026.*
+
+---
+
+## 1. One-paragraph pitch
+
+A basketball team's physiotherapist screens players with the **Functional Movement Screen (FMS)** — seven movement patterns, each scored 0–3 by eye. The scoring is slow, subjective, and hard to reproduce across raters or across months. FormScout is a Gradio app that takes a video of an athlete performing an FMS test, extracts 2D and 3D body pose, measures the biomechanics the FMS rubric actually cares about, and produces a 0–3 score *with a written rationale and an annotated overlay* — anchored to the physio's own previously-scored clips. It is a **screening aid that standardizes and speeds up the physio's first pass**, not a diagnosis and not an injury predictor. Everything runs on models that fit on a laptop.
+
+---
+
+## 2. The problem, honestly
+
+The FMS is a seven-test battery (Deep Squat, Hurdle Step, In-Line Lunge, Shoulder Mobility, Active Straight-Leg Raise, Trunk Stability Push-Up, Rotary Stability), each scored 0–3 for a composite 0–21. A score of 0 means **pain** during the movement and is an automatic red flag for clinical referral. Three of the tests have associated **clearing tests** (shoulder, spinal extension, spinal flexion) that also force a 0 on pain.
+
+Two facts shape this project and should be stated plainly in the demo and the writeup:
+
+- **Inter-rater reliability is decent but not perfect.** Composite-score reliability is moderate-to-good (ICC roughly 0.7–0.8), but novice and less-experienced raters grade component scores inconsistently. This is the real, addressable pain point: **variance between raters and over time.**
+- **Predictive validity for injury is weak/mixed.** The popular "≤14 = higher injury risk" cutoff is not a reliable predictor on its own. So FormScout must **not** be sold as injury prediction.
+
+**Where FormScout genuinely helps:**
+1. A repeatable, objective **digital baseline** to track an athlete over a season.
+2. **Asymmetry detection** (left vs. right), which is one of the FMS's most defensible outputs.
+3. A fast, consistent **first-pass / second opinion** that reduces rater variance.
+4. **Explainability** — it shows *which compensation* it saw, not just a number.
+
+This honest framing is also strategic: the Backyard AI track is judged partly on "honest fit between problem and the small-model constraint." Overclaiming clinical power would hurt the submission, not help it.
+
+---
+
+## 3. Why this fits the hackathon
+
+| Hackathon rule | How FormScout satisfies it |
+|---|---|
+| **Total params ≤ 32B** | Recommended config sums to ~18B. A portfolio of small specialists beats one monolith — which is on-theme for "think small." |
+| **Built on Gradio, hosted as a HF Space** | Gradio app with `gr.Video` input, a custom-styled results panel, on-Space inference (ZeroGPU or llama.cpp). |
+| **Show, Don't Tell** | Demo video = physio uploads a real player clip, gets a scored overlay in seconds. Social post = before/after of a manual vs. assisted screening session. |
+| **Track: Backyard AI** | The "someone you know" is the team physiotherapist. The deliverable is something they *actually use* on real players. |
+
+**Badge targets (aim for all six):**
+
+- 🔌 **Off the Grid** — no cloud APIs; all models served on the Space.
+- 🎯 **Well-Tuned** — the skeletal-temporal scoring head is fine-tuned on the physio's labels and published to the Hub.
+- 🎨 **Off-Brand** — custom Gradio frontend (scorecard UI, video overlay, per-test rubric panel), pushing past default Gradio.
+- 🦙 **Llama Champion** — VLM + embedding model served through llama.cpp (GGUF builds exist for both).
+- 📡 **Sharing is Caring** — publish the agent trace (one full screening run, agent by agent) to the Hub.
+- 📓 **Field Notes** — a blog post on building a clinical-adjacent AQA pipeline under a 32B budget, with the honesty section front and center.
+
+---
+
+## 4. Core technical framing: FMS *is* Action Quality Assessment
+
+Don't reinvent this from scratch. **Action Quality Assessment (AQA)** is the established field for "score how well a movement was performed." Skeleton-based AQA (sports scoring, surgical-skill and rehab assessment) is the directly relevant lineage. The "Skeletal-Temporal Transformer" idea maps onto the **AQA scoring head**.
+
+The key design constraint is the **tiny labeled dataset** (a couple of physio-scored videos). That rules out training a large score regressor from scratch and dictates a hybrid approach:
+
+1. **Deterministic biomechanics** carry most of the load. The FMS rubric is, to a large degree, a set of *angle and alignment thresholds* (e.g. Deep Squat "3" = femur below horizontal, torso parallel to tibia, knees tracking over feet, dowel over feet). These are computable from 3D pose with **zero training** and are inherently interpretable — exactly what earns a physio's trust.
+2. **A small learned head** (ST-GCN or a compact temporal transformer) refines the score and captures the patterns rules miss. It is small enough to fine-tune on a few labeled clips, *especially* if pre-trained on public AQA/pose datasets first.
+3. **Retrieval over the physio's labeled clips** (RAG) gives the language model few-shot anchors at judgment time — the right move when you have examples but not enough to train on.
+4. **A VLM as the judge/explainer** synthesizes rubric + measurements + retrieved exemplars into a final score and a human-readable rationale, and conservatively flags anything pain-related for a human.
+
+---
+
+## 5. Parameter budget (the single most important table)
+
+Assume "total parameters" = **sum of all model weights in the pipeline**. Design to this; confirm the exact interpretation in the Discord AMA.
+
+### Recommended config — "Portfolio of specialists" (~18B)
+
+| Component | Model | Params | Role |
+|---|---|---:|---|
+| 2D pose + tracking | YOLO26-Pose (L/X) | ~0.05B | Per-frame 17-keypoint skeletons, multi-person tracking |
+| Segmentation | SAM 3.1 (base) | ~0.85B | Clean athlete mask, occlusion handling, prompt for 3D |
+| 3D body | SAM 3D Body | ~0.7–1B* | Single-image 3D mesh → true joint angles, view-invariant |
+| Scoring head | ST-GCN / temporal transformer (fine-tuned) | ~0.01–0.05B | Pose-sequence → candidate 0–3 + confidence |
+| Judge / explainer | Qwen3-VL-8B-Instruct | 8B | Movement ID, rubric reasoning, final score + rationale |
+| Retrieval | Qwen3-VL-Embedding-8B | 8B | Nearest physio-scored reference clips (RAG) |
+| **Total** | | **~17.8B** | Comfortable headroom under 32B |
+
+\* SAM 3D Body's exact count isn't published prominently — verify on the model card. It's SAM-3-family and sub-billion-class; budget impact is small either way. The two 8B Qwen models **share the Qwen3-VL-8B backbone** (the embedder is built on the instruct model), which is conceptually clean and operationally efficient.
+
+### Alternative config — "Heavy reasoner" (~28.7B)
+
+Swap the 8B judge for **Qwen3.6-27B** (multimodal, strong tool-calling, MTP speedups on llama.cpp). Budget then = 27 + ~0.85 + ~1 + small ≈ **28.7B**. This **leaves no room for the 8B embedder**, so you'd drop RAG (or replace it with a sub-0.5B embedder, or use pose-feature similarity for retrieval). Note: Qwen3.6-27B's MTP speculative decoding currently can't run simultaneously with image input (`--mmproj`), so for vision you run it without MTP.
+
+**Recommendation: ship the ~18B portfolio config.** RAG over the physio's few labeled clips is worth more than raw reasoning horsepower on this task, the headroom de-risks the budget, and "many small specialists" is the better hackathon story.
+
+---
+
+## 6. Model selection rationale
+
+**YOLO26-Pose** — current-generation YOLO pose; single forward pass for detection + keypoints, NMS-free, real-time even on edge. Tiny param cost. It also handles **multiple people in frame** (important: team videos often have other players/staff visible) and feeds keypoints downstream. Off-the-shelf it predicts COCO human keypoints; can be fine-tuned for custom landmarks (e.g. dowel endpoints) if needed.
+
+**SAM 3.1** — gives a clean athlete mask and stable multi-object video tracking (Object Multiplex makes it fast). Two jobs: (a) isolate the target athlete from teammates/background so pose and 3D aren't polluted, (b) provide the mask prompt that SAM 3D Body consumes. Concept prompts ("the person in the blue jersey performing the squat") are a bonus for disambiguation.
+
+**SAM 3D Body** — *the addition that makes the scores trustworthy.* FMS criteria are joint angles and symmetry; 2D pose can't measure these reliably across camera angles (projection ambiguity). 3D mesh recovery from a single image, promptable with the 2D keypoints + mask you already have, yields view-invariant joint angles (the MHR rig even separates skeletal structure from soft-tissue shape, which is convenient for angle extraction). This is the difference between "looks bent" and "femur is 4° above horizontal → not a 3."
+
+**Skeletal-temporal scoring head** — your AQA component and your **Well-Tuned** badge. Recommend a compact **ST-GCN** (graph conv over the skeleton, temporal conv over frames) over a from-scratch transformer, because it's far more data-efficient on a tiny labeled set. Pre-train on public AQA / pose-action data, then fine-tune on the physio's labels. Output: per-test candidate score + a confidence the judge can weigh.
+
+**Qwen3-VL-8B-Instruct** — the judge. Strong video temporal modeling (Interleaved-MRoPE, timestamp alignment) suits movement clips. It identifies which of the 7 tests is being performed, reads the biomechanics, considers retrieved exemplars and the head's candidate, and emits the final score + rationale + detected compensation. GGUF → llama.cpp → Llama Champion.
+
+**Qwen3-VL-Embedding-8B** — retrieval. Embeds the query clip (or its keyframes/pose-render) and finds the physio's most similar already-scored clips to anchor the judge. Top multimodal retriever on MMEB-V2; same backbone as the judge; GGUF available.
+
+---
+
+## 7. Architecture — an agentic pipeline
+
+Structured as cooperating specialist agents (maps naturally onto an OFP-style orchestration, with a Director coordinating and quality-gating). Each agent has one job and a typed output.
+
+```
+                         ┌──────────────────────────────────────────────┐
+   video upload  ───────▶│  IngestAgent                                  │
+                         │  decode, normalize FPS, sample frames         │
+                         └───────────────┬──────────────────────────────┘
+                                         ▼
+                         ┌──────────────────────────────────────────────┐
+                         │  SegmentationAgent  (SAM 3.1)                 │
+                         │  athlete mask + track id (reject teammates)   │
+                         └───────────────┬──────────────────────────────┘
+                                         ▼
+              ┌──────────────────────────┴──────────────────────────┐
+              ▼                                                      ▼
+  ┌───────────────────────────┐                      ┌───────────────────────────┐
+  │ PoseAgent (YOLO26-Pose)    │                      │ Body3DAgent (SAM 3D Body)  │
+  │ 2D keypoints per frame     │ ───keypoints+mask──▶ │ 3D mesh / joint angles     │
+  └───────────────┬───────────┘                      └───────────────┬───────────┘
+                  └─────────────────────┬────────────────────────────┘
+                                        ▼
+                         ┌──────────────────────────────────────────────┐
+                         │  MovementClassifierAgent                      │
+                         │  which of the 7 FMS tests? (VLM or small CLS) │
+                         └───────────────┬──────────────────────────────┘
+                                         ▼
+              ┌──────────────────────────┴──────────────────────────┐
+              ▼                          ▼                           ▼
+  ┌────────────────────┐   ┌─────────────────────────┐   ┌────────────────────────┐
+  │ BiomechanicsAgent  │   │ ScoringAgent (ST-GCN)    │   │ RetrievalAgent          │
+  │ rubric angles,     │   │ candidate 0–3 + conf     │   │ (Qwen3-VL-Embedding)    │
+  │ ROM, symmetry,     │   │ from pose sequence       │   │ k nearest physio clips  │
+  │ alignment, timing  │   │                          │   │ + their scores          │
+  └─────────┬──────────┘   └───────────┬─────────────┘   └───────────┬────────────┘
+            └───────────────────────────┴──────────────────────────┘
+                                        ▼
+                         ┌──────────────────────────────────────────────┐
+                         │  JudgeAgent  (Qwen3-VL-8B)                    │
+                         │  rubric + measurements + exemplars + candidate│
+                         │  → final 0–3, rationale, compensation tag,    │
+                         │    corrective hint, PAIN/CLEARING → defer      │
+                         └───────────────┬──────────────────────────────┘
+                                         ▼
+                         ┌──────────────────────────────────────────────┐
+                         │  ReportAgent                                  │
+                         │  per-test card, composite 0–21, asymmetry     │
+                         │  flags, annotated video, exportable PDF       │
+                         └──────────────────────────────────────────────┘
+```
+
+**Agent contracts (sketch):**
+
+- `IngestAgent` → `{frames[], fps, duration, n_people}`
+- `SegmentationAgent` → `{athlete_track_id, masks[]}`
+- `PoseAgent` → `{keypoints_2d[frame][joint]={x,y,conf}}`
+- `Body3DAgent` → `{joints_3d[frame][joint]={x,y,z}, mesh_optional}`
+- `MovementClassifierAgent` → `{test_name, side: left|right|n/a, confidence}`
+- `BiomechanicsAgent` → `{features: {torso_tibia_angle, hip_flexion_deg, knee_valgus_deg, dowel_alignment, L_R_symmetry, ...}}`
+- `ScoringAgent` → `{candidate_score: 0–3, confidence}`
+- `RetrievalAgent` → `{exemplars: [{clip_id, score, similarity}]}`
+- `JudgeAgent` → `{score: 0–3, rationale, compensation_tags[], corrective_hint, needs_human: bool}`
+- `ReportAgent` → `{per_test[], composite, asymmetries[], overlay_video, pdf}`
+
+**Quality gating:** if the ST-GCN candidate and the JudgeAgent disagree by ≥1 point, or any agent confidence is low, the report marks the test **"low confidence — physio review recommended."** This keeps the human in the loop and is itself a selling point.
+
+---
+
+## 8. Scoring methodology, per test
+
+The seven tests reduce to measurable quantities. Build a small rubric module — one scoring function per test — that consumes the 3D features and returns a score with the triggering reason. Examples:
+
+- **Deep Squat (3):** femur below horizontal AND torso parallel to tibia AND knees tracking over feet AND dowel over feet. **(2):** same but achieved only with heels elevated. **(1):** criteria unmet even with heels elevated. → all four conditions are angle/alignment checks on the 3D pose.
+- **Hurdle Step / In-Line Lunge / Shoulder Mobility / ASLR:** bilateral — score each side, **record the lower** as the test score, and **always emit the asymmetry** even when the score is the same.
+- **Trunk Stability Push-Up / Rotary Stability:** trunk rigidity / timing of limb movement — temporal features from the pose sequence; the ST-GCN head is most valuable here.
+- **Pain / clearing tests (0):** the system **cannot** detect pain. Any clearing test, or a visible distress/abort, sets `needs_human = true` and the test is **not auto-scored**. Defer to the physio. State this loudly.
+
+Final composite = sum of seven test scores (0–21), plus an asymmetry summary. The number is never shown without its rationale.
+
+---
+
+## 9. Data & fine-tuning plan (tiny-dataset survival guide)
+
+You have "a couple" of physio-scored clips. Treat them as gold, not as a training set.
+
+1. **Deterministic backbone first.** Get the biomechanics rubric working with no training. Validate the measured angles against the physio's scores qualitatively. This alone may be demo-ready.
+2. **Pre-train the ST-GCN** on public pose-action / AQA data (action recognition or generic AQA) so it learns temporal movement structure, not FMS labels.
+3. **Fine-tune on the physio's clips** with heavy augmentation: temporal crops/speed jitter, mirror (left↔right, doubles your bilateral data), camera-angle perturbation in 3D, joint noise. Few-shot, regularized, early-stopped.
+4. **Hold out at least one physio-scored clip** as a sanity check the judge never sees.
+5. **RAG instead of more training.** Every labeled clip goes into the embedding index as a scoring anchor. New clips added later improve the system with no retraining — a nice longitudinal story for the physio.
+6. **Publish the fine-tuned head** to the Hub with a model card (→ Well-Tuned badge). Include the augmentation recipe and the honest "trained on N clips, treat as assistive" caveat.
+
+**Label schema to collect from the physio** (if you can get a bit more data): `clip_id, athlete_id, test_name, side, score(0–3), pain(bool), compensation_notes, camera_view`. Even 20–30 well-labeled clips meaningfully helps.
+
+---
+
+## 10. Gradio Space & deployment
+
+**UI (targets Off-Brand badge):**
+- `gr.Video` upload (or webcam capture) + a test-type selector (auto-detect, with manual override).
+- Results panel: the 0–3 score as a large dial/patch, the composite 0–21, an asymmetry strip (L/R bars), and the **rationale text**.
+- The annotated overlay video: skeleton + the specific angle that decided the score drawn on the frame where it mattered.
+- A rubric drawer that shows the official 3/2/1 criteria for the detected test, with the met/unmet conditions checked off.
+- A persistent **"Screening aid — not a diagnosis. Pain or clearing tests require a clinician."** banner.
+- Custom CSS / `gr.Server` for a non-default look (scout/trail-map theme would rhyme with the hackathon, and with your design instincts).
+
+**Compute:**
+- ZeroGPU (H200 slice) can host the ~18B portfolio; load pose/SAM/3D eagerly, the VLM + embedder via llama.cpp.
+- For **Off the Grid**, ensure zero external API calls — everything served on-Space.
+- For **Llama Champion**, route the VLM + embedding through llama.cpp (GGUF builds exist for Qwen3-VL-8B-Instruct, Qwen3-VL-Embedding-8B, and Qwen3.6-27B). On a Space, watch the CUDA/llama-cpp build flags — recent hackathon Spaces hit `libcudart` issues; a CPU-only or pinned-CUDA build is the usual fix.
+- Persist the embedding index and accumulated labels in Space storage for the longitudinal baseline.
+
+---
+
+## 11. Clinical safety & ethics (bake this in, don't bolt it on)
+
+- **Not a medical device.** Screening aid only. No diagnosis, no injury prediction, no treatment advice beyond generic FMS-style correctives.
+- **Pain is out of scope** for automatic scoring — always defer to the physio.
+- **Human-in-the-loop by design:** low-confidence and disagreement cases are surfaced, not hidden.
+- **Consent & privacy:** athlete videos are biometric data. Get consent; don't log/persist clips beyond what the physio approves; document retention in the writeup.
+- **Honesty in the demo:** show a case the system gets right *and* one it flags as uncertain. Judges (and physios) trust calibrated tools more than confident ones.
+
+---
+
+## 12. Build plan — two weekends (June 5–15)
+
+**Weekend 1 — the spine works end to end:**
+- Day 1: Space scaffold, `gr.Video` in → skeleton overlay out (YOLO26-Pose). Ingest + Segmentation + Pose agents.
+- Day 2: SAM 3D Body integrated; BiomechanicsAgent computing Deep-Squat angles; first deterministic score on a real clip.
+- Goal: upload a squat video, get a rationalized 0–3. *This alone is a viable demo.*
+
+**Midweek:** wire the JudgeAgent (Qwen3-VL via llama.cpp), MovementClassifier, and the rubric module for all 7 tests. Attend the AMA — confirm the param-sum interpretation.
+
+**Weekend 2 — make it sing:**
+- ST-GCN pre-train + few-shot fine-tune on physio clips; publish to Hub.
+- RetrievalAgent + embedding index over labeled clips.
+- Custom UI polish, asymmetry view, PDF export, safety banners.
+- Record the demo video (physio uses it on a real player), write the social post, publish the agent trace and the blog post.
+
+---
+
+## 13. Risks & open questions
+
+- **Param-sum interpretation** — biggest unknown. The ~18B config is safe under either reading; confirm anyway.
+- **SAM 3D Body on a Space** — verify weights, license, and that it runs within ZeroGPU limits; have a 2D-only fallback (angles from 2D + camera-angle caveats) if it's too heavy.
+- **Single-camera angle limits** even with 3D — note it; recommend a consistent capture protocol (fixed camera position) for the physio, which also improves the longitudinal baseline.
+- **Tiny dataset** — the deterministic rubric must stand on its own so the demo doesn't hinge on the learned head generalizing from a few clips.
+- **llama.cpp + vision build** on Spaces — budget time for the CUDA build dance; CPU fallback for the embedder is fine.
+- **Movement misclassification** — if the wrong test is detected, scoring is meaningless; keep the manual override prominent.
+
+---
+
+## 14. Quick reference — the stack
+
+| Layer | Choice | Badge it helps |
+|---|---|---|
+| 2D pose | YOLO26-Pose | — |
+| Segmentation/track | SAM 3.1 | — |
+| 3D biomechanics | SAM 3D Body | — |
+| Learned scoring | ST-GCN (fine-tuned, published) | Well-Tuned |
+| Judge/explainer | Qwen3-VL-8B-Instruct (llama.cpp) | Llama Champion |
+| Retrieval | Qwen3-VL-Embedding-8B (llama.cpp) | Llama Champion |
+| Serving | On-Space, no cloud APIs | Off the Grid |
+| Frontend | Custom Gradio (scout theme) | Off-Brand |
+| Trace | Published agent run on Hub | Sharing is Caring |
+| Writeup | Blog post w/ honesty section | Field Notes |
+
+*Total ≈ 18B params. Honest, explainable, human-in-the-loop, runs on a laptop.*

docs/FormScout-FMS-Spec.md.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 160323

docs/FormScout-Starter-Kit.md

@@ -0,0 +1,169 @@
+# FormScout — Starter Kit & Resource Pack
+
+Companion to `FormScout-FMS-Spec.md` and `FormScout-Build-Prompt.md`. Every link below was checked. Read §1 first — some items are time-sensitive and block the build if you leave them late.
+
+---
+
+## 1. Do this NOW (before the hack window — some take hours to clear)
+
+- [ ] **Request access to the gated Meta checkpoints today.** Both are gated on Hugging Face and approval isn't instant:
+  - SAM 3 / SAM 3.1 — request on the SAM 3 repos (you need the latest code for the 3.1 checkpoints).
+  - SAM 3D Body — `facebook/sam-3d-body-dinov3` and `facebook/sam-3d-body-vith` both require an access request, then an authenticated download. **Note:** data/checkpoints are blocked in sanctioned jurisdictions — shouldn't affect SK, but verify.
+- [ ] **Put your HF token in the Space secrets** so the Space can pull the gated weights at build time.
+- [ ] **Check licenses before you commit to a model** (this affects whether you can even submit):
+  - Qwen3-VL-8B / Qwen3-VL-Embedding-8B / Qwen3.6 → **Apache-2.0** (clean).
+  - SAM 3 / SAM 3.1 / SAM 3D Body → **SAM License** (not Apache; read the terms — there are use restrictions).
+  - Ultralytics YOLO26 → historically **AGPL-3.0** (open-sourcing obligations; commercial license exists). Verify on the model/repo and make sure an AGPL dependency is OK for your submission. If it's a problem, RTMPose/ViTPose are alternatives.
+  - pyskl / MMAction2 → Apache-2.0.
+  - KIMORE / UI-PRMD → academic/research terms; check before redistributing anything derived.
+- [ ] **Confirm the param-counting rule in the Discord AMA.** Specifically: (a) is it summed across the pipeline or per-model? (b) do **frozen** base models count? (c) does a LoRA adapter's base count? Your ~18B config is safe under the strict reading either way, but get it on record.
+
+---
+
+## 2. Literature package
+
+### 2.1 The framing that wins — "evaluate like an FMS reliability study"
+
+The single most credible move in your writeup: evaluate FormScout the way the clinical literature evaluates human FMS raters. Treat the model as a *second rater* and report **weighted Cohen's κ** and **ICC** against the physio, the exact metrics the reliability papers use. That instantly makes your results legible to any sports-medicine reader and is far more honest than a vanity accuracy number.
+
+| Resource | What it gives you | Link |
+|---|---|---|
+| Physiopedia — FMS | Clean overview of the 7 tests + 0–21 scoring | https://www.physio-pedia.com/Functional_Movement_Screen_(FMS) |
+| FMS reliability study (JOSPT 2012) | The ICC/κ numbers and method you'll mirror in your eval | https://www.jospt.org/doi/10.2519/jospt.2012.3838 |
+| FMS in elite youth soccer (PMC) | Per-test scores, asymmetries, clearing-test order | https://pmc.ncbi.nlm.nih.gov/articles/PMC5675373/ |
+| Clinician's guide to FMS scoring | Per-test 3/2/1 criteria in plain language (rubric source) | https://meloqdevices.com/blogs/meloq-updates/functional-movement-screening |
+
+> **Honesty anchor for the blog post:** the popular "≤14 → injury risk" cutoff has weak/mixed predictive validity. Sell standardization, asymmetry detection, and a repeatable baseline — not prediction.
+
+### 2.2 Action Quality Assessment — surveys & living lists
+
+| Resource | Why | Link |
+|---|---|---|
+| *A Decade of AQA* (survey, 2025, 200+ papers, PRISMA) | The map of the whole field; start here | https://arxiv.org/abs/2502.02817 · code: https://github.com/HaoYin116/Survey_of_AQA |
+| *Comprehensive Survey of AQA: Method & Benchmark* (2024) | Taxonomy by modality (video / **skeleton** / multimodal) + unified benchmark | https://arxiv.org/abs/2412.11149 · page: https://zhoukanglei.github.io/AQA-Survey |
+| Awesome-AQA (ZhouKanglei) | Curated, **has a Medical-Care/rehab section** — your closest analogues | https://github.com/ZhouKanglei/Awesome-AQA |
+| Awesome-AQA (Lyman-Smoker) | Second list; catches papers the other misses (FLEX, ExAct, etc.) | https://github.com/Lyman-Smoker/Awesome-AQA |
+
+### 2.3 Skeleton-based scoring — the methods your head will borrow from
+
+| Paper | Relevance to FormScout | Link |
+|---|---|---|
+| ST-GCN (original) | The graph-over-skeleton + temporal-conv backbone | https://github.com/open-mmlab/mmaction2/blob/main/configs/skeleton/stgcn/README.md |
+| AQA via Hierarchical **Pose-guided** Multi-Stage Contrastive Regression (TIP 2025) | Pose-guided + contrastive regression with few labels — close to your setup | https://arxiv.org/abs/2501.03674 |
+| Attention-guided Movement **Quality** Assessment + skeletal augmentation (UI-PRMD/KIMORE) | Transformer MQA on clinician-scored rehab data; **augmentation recipe for tiny sets** | https://arxiv.org/pdf/2204.07840 |
+| SSL-Rehab: self-supervised 3D skeleton + **LoRA** fine-tune (KIMORE/UI-PRMD) | Pretrain→LoRA recipe for small clinical datasets (uses your LoRA muscle) | https://www.sciencedirect.com/science/article/abs/pii/S1077314224003564 |
+| Skeleton-based AQA w/ anomaly-aware DTW (Sensors 2025) | DTW alignment + anomaly scoring; cheap, label-light baseline | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12693942/ |
+
+---
+
+## 3. Models & tooling (verified)
+
+| Component | Repo / card | Params | License | Gated? |
+|---|---|---:|---|---|
+| YOLO26-Pose | https://docs.ultralytics.com/tasks/pose | <0.1B | AGPL-3.0* | no |
+| SAM 3.1 | https://github.com/facebookresearch/sam3 | ~0.85B | SAM License | **yes** |
+| SAM 3D Body | https://github.com/facebookresearch/sam-3d-body · https://huggingface.co/facebook/sam-3d-body-dinov3 | sub-1B† | SAM License | **yes** |
+| ST-GCN++ / PoseConv3D | https://github.com/kennymckormick/pyskl | ~0.01–0.05B | Apache-2.0 | no |
+| Qwen3-VL-8B-Instruct | https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct | 8B | Apache-2.0 | no |
+| Qwen3-VL-Embedding-8B | https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B (GGUF: dam2452/...-GGUF) | 8B | Apache-2.0 | no |
+| Qwen3.6-27B (alt brain) | https://huggingface.co/unsloth/Qwen3.6-27B-GGUF | 27B | Apache-2.0 | no |
+
+\* verify the current YOLO26 license. † two variants (`dinov3`, `vith`); confirm exact count on the card — budget impact is small either way. SAM 3 itself is 848M.
+
+**Useful extras:** SAM 3D Body uses a Momentum Human Rig (MHR) that separates skeleton from soft-tissue shape — convenient for clean joint-angle extraction. The repo ships a notebook combining SAM 3D Body + SAM 3D Objects in one frame of reference. SAM 3D Body demo: https://www.aidemos.meta.com/segment-anything/editor/convert-body-to-3d
+
+---
+
+## 4. Datasets for transfer / pretraining
+
+You have a couple of labeled clips. Pretrain on clinician-scored movement-quality data first, then few-shot fine-tune. These are the most transferable to FMS (ranked by relevance):
+
+| Dataset | Why it's the closest analogue | Link |
+|---|---|---|
+| **KIMORE** | Clinician **scores** of low-back-pain rehab exercises (trunk control, multi-plane) — same "score movement quality" task as FMS; partially overlaps Deep Squat / Rotary Stability / TSPU mechanics | https://www.researchgate.net/publication/333791841 (search "KIMORE dataset") |
+| **UI-PRMD** | 10 rehab movements, correct vs. incorrect executions; standard MQA benchmark, pairs with KIMORE | search "UI-PRMD University of Idaho Physical Rehabilitation Movements" |
+| **Fitness-AQA** | Real gym **squat/deadlift form errors** — directly relevant to Deep Squat compensations | https://github.com/ParitoshParmar/MTL-AQA (links Fitness-AQA) |
+| **FLEX** | Large multi-modal fitness AQA dataset | via Lyman-Smoker/Awesome-AQA |
+| **MTL-AQA / AQA-7 / FineFS** | General sports AQA for backbone pretraining (diving, skating) | https://github.com/ParitoshParmar/MTL-AQA |
+
+**FMS-specific public video data is scarce** — don't expect a drop-in set. Your physio's clips are the gold; everything above is for pretraining the temporal backbone so it learns movement structure before it ever sees an FMS label.
+
+---
+
+## 5. Build & deploy tooling
+
+| Need | Link |
+|---|---|
+| Gradio docs (v6) | https://www.gradio.app/docs |
+| `gradio.Server` — custom frontend + Gradio backend (Off-Brand badge) | https://www.gradio.app/guides/server-mode · blog: https://huggingface.co/blog/introducing-gradio-server |
+| Gradio AI coding-assistant skill | `gradio skills add --claude` (PyPI: https://pypi.org/project/gradio/) |
+| Gradio changelog (confirm `gr.Walkthrough`, `gr.Navbar`, `gr.Video.playback_position`) | https://www.gradio.app/changelog |
+| HF Spaces ZeroGPU (`@spaces.GPU`) | https://huggingface.co/docs/hub/spaces-zerogpu |
+| llama.cpp | https://github.com/ggml-org/llama.cpp |
+| pyskl (ST-GCN++/PoseConv3D, custom-video tutorial incl. diving48) | https://github.com/kennymckormick/pyskl |
+| MMAction2 (broader video understanding) | https://github.com/open-mmlab/mmaction2 |
+| Hackathon's own trailheads (ML Intern, Gradio guides) | https://github.com/huggingface/ml-intern |
+
+> **Hackathon-specific gotcha already seen in the org:** another team's Space hit `libcudart.so.12` errors and had to swap llama.cpp for transformers + `spaces.GPU`. Plan for it — isolate the llama.cpp build (CPU-only or pinned-CUDA) and keep a transformers fallback. For the scoring head, a small hand-rolled ST-GCN may deploy more cleanly on a Space than the full MMAction2/pyskl stack — prototype with pyskl, ship lean.
+
+---
+
+## 6. Two artifacts you probably haven't made yet
+
+### 6.1 Data & capture protocol (highest-leverage non-code work)
+
+With a tiny dataset, controlling *how* clips are captured beats any model tweak. Give the physio a one-pager:
+
+- **Camera:** one fixed position, tripod, ~3 m back, lens at hip height, landscape, 1080p/30fps+. Same setup every session — this is what makes 3D consistent and the longitudinal baseline meaningful.
+- **Framing:** whole body in frame for the whole rep, including the dowel. Plain-ish background, even lighting, no backlight.
+- **One athlete in frame** at scoring time (or note who to track). For bilateral tests, capture **both sides** and label each.
+- **Label schema (CSV):** `clip_id, athlete_id, date, test_name, side(L/R/NA), score(0–3), pain(bool), compensation_notes(free text), camera_view, consent_on_file(bool)`.
+- **One rep per clip** to start (simplest). If sessions are continuous, you'll need temporal segmentation first — flag it to the build agent at Phase 1.
+
+### 6.2 Evaluation plan
+
+Define "good" before you train, given so few labels:
+
+- **Primary:** Spearman ρ between predicted and physio scores (the AQA-standard metric), plus **exact-match** and **±1 accuracy** per test.
+- **Clinical credibility:** **weighted Cohen's κ** and **ICC** of model-vs-physio, reported alongside the human inter-rater numbers from the JOSPT study — i.e. "how does FormScout compare to a second human rater?"
+- **Asymmetry:** detection rate of L/R asymmetries the physio flagged (this is one of the FMS's most defensible outputs).
+- **Validation:** leave-one-clip-out CV (you can't afford a held-out test split). Keep ≥1 clip the judge never sees for the demo.
+- **Calibration:** report when the system says "low confidence / physio review" and show it's right to do so. A well-calibrated, humble tool reads as more trustworthy than a confident one.
+
+---
+
+## 7. Ethics, consent & data handling (EU / Slovakia)
+
+You're filming identifiable athletes, possibly **minors** on a youth team. This is biometric personal data under GDPR — treat it as first-class, and say so in your submission (judges and physios both reward it):
+
+- **Consent:** written consent from each athlete (and a parent/guardian for anyone under 18) before any footage is used. No consent → not in the dataset, not in the demo.
+- **Data minimization & retention:** keep only what you need; don't persist raw clips on the Space beyond what's approved; document a retention/deletion policy. Prefer storing derived skeletons over raw video where possible.
+- **Demo footage:** use a consenting adult (you, a teammate) for the public demo video rather than a minor athlete, even if you trained on team data privately.
+- **Framing:** screening aid, not a medical device; pain/clearing tests always defer to the clinician; human-in-the-loop by design.
+
+---
+
+## 8. The transfer-learning recipe (ties it together)
+
+1. **Backbone pretrain** — ST-GCN++ on a general skeleton-action set (NTU/Kinetics skeletons via pyskl) so it learns motion structure.
+2. **Domain adapt** — continue on **KIMORE + UI-PRMD** (clinician-scored movement quality) so it learns *quality*, not just *what action*.
+3. **Few-shot fine-tune** — **LoRA** on the physio's FMS clips with heavy augmentation (temporal jitter, **L↔R mirror** to double bilateral data, 3D camera-angle perturbation, joint noise). The SSL-Rehab paper (§2.3) is your blueprint and it's exactly your LoRA wheelhouse.
+4. **Don't over-train the head** — let deterministic biomechanics carry the demo; the learned head and RAG are the refinement and the badges, not the foundation.
+
+---
+
+## 9. Demo & submission storyboard (the "make it sing" 30%)
+
+The submission needs a demo video + social post; "Show, Don't Tell" is a literal rule. A tight 60–90s cut:
+
+1. **0–10s** — the problem: physio eyeballing a squat, scribbling a score. "Same player, two raters, two scores."
+2. **10–35s** — upload the clip to FormScout → skeleton overlay → 0–3 with the *deciding angle drawn on the frame* (`playback_position` jump). The "aha" shot.
+3. **35–55s** — the scorecard: composite 0–21, the L/R asymmetry strip, a "low confidence — physio review" flag on a borderline case (honesty sells).
+4. **55–75s** — the physio reacting / using it on a real player (the Backyard AI "they actually used it" proof).
+5. **End card** — "Runs on a laptop. ~18B params. Screening aid, not a diagnosis." Link the Space, the published head, the agent trace, the blog.
+
+Social post: lead with the overlay GIF + the asymmetry-detection angle; tag Gradio/HF; one line of honest framing.
+
+---
+
+*Built to give FormScout the best shot. The two things most teams underinvest in — the capture protocol (§6.1) and the honest, clinical-style evaluation (§6.2, §2.1) — are exactly where this project can out-class flashier entries. Good luck. 🏀*

docs/plans/FormScout-Build-Prompt.md

@@ -0,0 +1,168 @@
+# Build Prompt — FormScout (FMS scoring on Gradio, ≤32B)
+
+> **How to use this:** paste everything below the line into your coding agent (Claude Code, Codex, Cursor, etc.) as the opening instruction. Attach `FormScout-FMS-Spec.md` alongside it — that file is the product source of truth; this file is the engineering contract and process. Work through it phase by phase.
+
+---
+
+## ROLE
+
+You are a **senior Python + Gradio architect with ~10 years of shipping ML web apps**, including production Hugging Face Spaces, custom-frontend Gradio deployments, ZeroGPU services, and llama.cpp-served models. You are pragmatic, opinionated about defaults, allergic to dead code, and you **verify APIs against current docs instead of trusting your memory** — Gradio and the model ecosystem move fast and your training data may be stale. You build **vertical slices** that run end to end early, then deepen. You never hand back a broken app.
+
+## MISSION
+
+Build **FormScout**, a Gradio app hosted as a Hugging Face Space that scores Functional Movement Screen (FMS) videos 0–3 per test with an explainable rationale and an annotated overlay, for the Build Small Hackathon (Backyard AI track). Full product requirements are in the attached `FormScout-FMS-Spec.md`. Honor it; if you deviate, say why.
+
+## PRIME DIRECTIVES (read before writing any code)
+
+1. **Verify before you build.** Do Phase 0 recon first. Do not write against a Gradio/model API you have not confirmed exists in the current version. When unsure, read the doc or the model card, don't guess.
+2. **Vertical slice first.** The fastest path to a working `video in → scored overlay out` for *one* test beats a half-built version of all seven. Get something running on day one, then expand.
+3. **Stay under budget.** Total model parameters across the whole pipeline must be **≤ 32B**. Track a running sum in `MODEL_BUDGET.md` and update it whenever you add or swap a model. The target config is ~18B (see spec §5). If a choice would exceed 32B, stop and flag it.
+4. **No cloud model APIs.** All inference runs on the Space (Off the Grid badge). No OpenAI/Anthropic/Gemini/etc. calls for the core pipeline.
+5. **Honesty & safety are features, not footnotes.** This is a screening aid, not a diagnosis and not injury prediction. Pain and clearing tests are never auto-scored — they set `needs_human=true`. A safety banner is always visible. Low-confidence and agent-disagreement cases are surfaced, not hidden.
+6. **Modular agents, typed contracts.** Each pipeline stage is an independent module with a typed input/output (see spec §7). No god-functions. The pipeline must be runnable headless (no Gradio) for testing.
+
+---
+
+## PHASE 0 — Recon & environment (do this first, report findings before coding)
+
+**Goal:** confirm the ground truth, then write a short `RECON.md` summarizing what you found and any deviations from the spec.
+
+1. **Install the Gradio skill** for this agent so you get current Gradio knowledge:
+   `gradio skills add --claude` (use the right flag for your agent; `--global` is fine).
+2. **Pin and confirm Gradio.** Determine the current major version (expect Gradio 6.x). Record the exact version you'll target in `requirements.txt`. Confirm these still exist and note their current signatures:
+   - `gr.Blocks`, `gr.Video` (incl. `playback_position` for jumping to the decisive frame), `gr.Walkthrough` / `gr.Step` (for the 7-test flow), `gr.Navbar` (multipage), custom theming / CSS.
+   - `gradio.Server` (custom-frontend mode) — decide **Blocks vs Server** for the UI (see UI section).
+   - ZeroGPU usage: the `@spaces.GPU` decorator pattern, and the caveat that with `gradio.Server` + ZeroGPU you must call endpoints via `@gradio/client` from the browser.
+3. **Verify every model** on its Hugging Face card — confirm it exists, its **license**, its **parameter count**, and whether a **GGUF** build exists for llama.cpp:
+   - YOLO26-Pose (Ultralytics) — pick a variant (l/x) and confirm license implications.
+   - SAM 3.1 (`facebookresearch/sam3`) — base checkpoint size.
+   - **SAM 3D Body** — *this is the uncertain one.* Confirm weights are public, the license, the **exact param count**, and that it runs within a ZeroGPU slice. If it's too heavy or not usable, fall back to **2D-only biomechanics** (angles from 2D pose + explicit camera-angle caveats) and note it.
+   - Qwen3-VL-8B-Instruct + Qwen3-VL-Embedding-8B — confirm GGUF builds and that they share the Qwen3-VL backbone.
+4. **llama.cpp on Spaces reality check.** Confirm a working install path; prior hackathon Spaces hit `libcudart.so` errors. Decide CPU-only vs pinned-CUDA build per model. Have a `transformers`/`spaces.GPU` fallback ready for any model that won't build under llama.cpp in time.
+5. **Open question to surface, not solve:** does "total parameters ≤ 32B" mean *per model* or *summed across the pipeline*? Design for the **summed** reading (safe under either). Note in `RECON.md` to confirm via the Discord AMA.
+
+**Exit criteria for Phase 0:** `RECON.md` exists with the Gradio version, a verified model table (name, params, license, GGUF y/n, runs-on-ZeroGPU y/n), the running param sum, the chosen UI approach, and any fallbacks triggered.
+
+---
+
+## PHASE 1 — The spine (one test, end to end, headless + Gradio)
+
+**Goal:** upload a Deep Squat clip → get a rationalized 0–3 + skeleton overlay.
+
+- Scaffold the repo (structure below). Pipeline runs **headless** via `python -m formscout.run sample.mp4` before any UI.
+- Implement `IngestAgent` → `SegmentationAgent` (SAM 3.1) → `PoseAgent` (YOLO26-Pose). Reject non-target people via the mask/track id.
+- Implement `Body3DAgent` (SAM 3D Body) **or** the 2D fallback from Phase 0.
+- Implement `BiomechanicsAgent` for Deep Squat only: torso–tibia angle, hip-flexion depth (femur vs horizontal), knee tracking, dowel alignment.
+- Implement a **deterministic** rubric scorer for Deep Squat (3/2/1 per spec §8). No ML scoring yet.
+- Minimal Gradio UI: `gr.Video` in, score + rationale + overlay out.
+
+**Exit criteria:** a real squat clip produces a defensible score, a one-line reason citing the deciding measurement, and an overlay video. Runs on the Space.
+
+---
+
+## PHASE 2 — All seven tests + the judge
+
+- Extend `BiomechanicsAgent` + rubric scorers to all 7 tests. Bilateral tests score each side, **report the lower**, and **always emit the asymmetry**.
+- `MovementClassifierAgent`: identify which test is in the clip (VLM or a small classifier) with a **manual override** in the UI.
+- `JudgeAgent` (Qwen3-VL-8B via llama.cpp): consumes rubric + measurements + the deterministic candidate → final 0–3, rationale, compensation tag, corrective hint. Pain/clearing → `needs_human=true`, **not scored**.
+- `ReportAgent`: per-test card, composite 0–21, asymmetry strip, annotated overlay, PDF export.
+
+**Exit criteria:** a multi-test session produces a full scorecard with composite + asymmetries; pain/clearing cases defer to human; disagreements between deterministic and judge scores are flagged.
+
+---
+
+## PHASE 3 — Learned scoring + retrieval (the badges)
+
+- `ScoringAgent`: compact **ST-GCN** scoring head. Pre-train on public AQA/pose data, then **few-shot fine-tune** on the physio's labeled clips with heavy augmentation (temporal jitter, **left↔right mirror**, 3D camera-angle perturbation, joint noise). Hold out ≥1 labeled clip. **Publish the fine-tuned head to the Hub** with an honest model card → *Well-Tuned*.
+- `RetrievalAgent`: build a Qwen3-VL-Embedding-8B index over the physio's labeled clips; return k nearest + their scores to anchor the judge → RAG.
+- Wire the judge to weigh: deterministic candidate + ST-GCN candidate + retrieved exemplars.
+
+**Exit criteria:** scores incorporate the learned head and exemplars; adding a new labeled clip improves retrieval with **no retraining**.
+
+---
+
+## PHASE 4 — Polish, ship, document
+
+- Custom UI pass (Off-Brand): scout/trail theme, score dial, asymmetry bars, rubric drawer with met/unmet checkboxes, decisive-frame jump via `playback_position`, persistent safety banner.
+- Persist the embedding index + accumulated labels in Space storage (longitudinal baseline).
+- **Publish one full agent trace** to the Hub (every agent's I/O for one run) → *Sharing is Caring*.
+- Write the **blog post / field notes** with the honesty section front-and-center → *Field Notes*.
+- Record the demo video (physio scores a real player) + the social post.
+
+**Exit criteria:** all six badges attempted, Space is green, demo + post + trace + blog are linked from the README.
+
+---
+
+## REPO STRUCTURE (target)
+
+```
+formscout/
+  app.py                 # Gradio entrypoint (Blocks or Server)
+  formscout/
+    __init__.py
+    config.py            # paths, model ids, thresholds, feature flags
+    pipeline.py          # Director: orchestrates agents, quality-gates
+    run.py               # headless CLI entrypoint (no Gradio)
+    agents/
+      ingest.py
+      segmentation.py    # SAM 3.1
+      pose2d.py          # YOLO26-Pose
+      body3d.py          # SAM 3D Body (+ 2d fallback)
+      classify.py        # movement classifier
+      biomechanics.py    # rubric features per test
+      scoring.py         # ST-GCN learned head
+      retrieval.py       # Qwen3-VL-Embedding index
+      judge.py           # Qwen3-VL-8B judge
+      report.py          # scorecard, overlay, pdf
+    rubric/
+      deep_squat.py ...  # one scorer per FMS test, pure functions
+    types.py             # typed dataclasses for every agent contract
+    serving/
+      llama_cpp.py       # llama.cpp client wrappers + fallbacks
+    ui/
+      theme.py, components.py, custom/  # frontend assets
+    tracing.py           # structured per-agent I/O logging (for the trace badge)
+  tests/                 # headless tests per agent + a golden-clip e2e test
+  requirements.txt
+  README.md              # Space card: pitch, demo, trace, blog, safety
+  MODEL_BUDGET.md        # running param sum, must stay ≤32B
+  RECON.md               # Phase 0 findings
+```
+
+## ENGINEERING STANDARDS
+
+- **Typing everywhere.** Every agent takes and returns a dataclass from `types.py`. Validate at boundaries.
+- **Pure rubric functions.** Each test scorer is a pure function `(features) -> ScoreResult` with the triggering reason. Unit-test each against hand-computed cases.
+- **Defensive by default.** Handle: no person detected, multiple people, wrong/ambiguous test, occlusion, too-short clip, bad FPS, 3D model OOM. Degrade gracefully and tell the user what happened — never crash the Space.
+- **Confidence is first-class.** Every agent emits a confidence; the Director flags low confidence and ≥1-point judge/ST-GCN disagreement as "physio review recommended."
+- **Config over constants.** Thresholds, model ids, k for retrieval, feature flags live in `config.py`, not scattered literals.
+- **Tracing for free badge.** `tracing.py` records structured per-agent inputs/outputs for any run; one run gets exported for the Hub trace.
+- **Determinism in demos.** Fix seeds; cache model loads at startup; warm the pipeline so the demo isn't a cold-start.
+- **Tests:** per-agent unit tests on fixtures + one golden-clip end-to-end test asserting score, `needs_human`, and overlay presence. Keep a tiny committed sample clip.
+
+## GRADIO-SPECIFIC GUIDANCE
+
+- **Blocks vs Server:** start with `gr.Blocks` + custom CSS/theme — fastest to a polished result and enough for Off-Brand. Escalate to `gradio.Server` with your own frontend **only if** Blocks can't express the UI; document the reason. (Server still gives queuing, ZeroGPU, MCP.)
+- Use `gr.Walkthrough`/`gr.Step` to guide the physio through a 7-test session; `gr.Navbar` if you split pages.
+- Use `gr.Video`'s `playback_position` to jump the result video to the frame that decided the score.
+- ZeroGPU: wrap heavy inference in `@spaces.GPU`; load models once at module scope; mind the per-call GPU time limit. If using `gradio.Server` + ZeroGPU, call endpoints via `@gradio/client` from the browser.
+- `requirements.txt`: pin Gradio and every model lib; isolate the llama.cpp build (CPU-only or pinned-CUDA) to dodge `libcudart` failures; keep a `transformers` + `spaces.GPU` fallback path.
+
+## DEFINITION OF DONE (badge checklist)
+
+- [ ] Space runs green; upload → scorecard works on real clips.
+- [ ] Param sum verified ≤ 32B in `MODEL_BUDGET.md`.
+- [ ] 🔌 No cloud model APIs anywhere in the pipeline.
+- [ ] 🎯 Fine-tuned ST-GCN head published to the Hub w/ honest card.
+- [ ] 🎨 Custom, non-default Gradio UI.
+- [ ] 🦙 VLM + embedder served via llama.cpp.
+- [ ] 📡 One full agent trace published to the Hub.
+- [ ] 📓 Blog post / field notes written, honesty section included.
+- [ ] Demo video + social post recorded.
+- [ ] Safety banner present; pain/clearing never auto-scored; low-confidence flagged.
+
+## INTERACTION PROTOCOL
+
+- **After each phase**, post: what runs now, the updated param sum, deviations from the spec, and the next step. Don't silently change architecture.
+- **Ask the human only when blocked on a real decision** — e.g. single-test clips vs continuous sessions (changes segmentation + UI), SAM 3D Body unusable (triggers 2D fallback), or the param-sum interpretation. Otherwise proceed with the spec's defaults and note your assumption inline.
+- **Never claim a Gradio/model API works without having verified it** this session. If you didn't check it, say so.

docs/plans/FormScout-Build-Prompt.md.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a74ba6acdbd04849ffc904edfbeaf76b7237578bfadad9d9215b7c4f701af19d
+size 142230

docs/superpowers/plans/2026-06-04-formscout-full-build.md

@@ -0,0 +1,2813 @@
+# FormScout Full Build Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Build a Gradio/HF Space app that scores FMS videos 0–3 per test with rationale and annotated overlay, running entirely on-Space with ~18B params, targeting all 6 hackathon badges.
+
+**Architecture:** Typed specialist agents orchestrated by a deterministic Director; 2D pose path is always the default; 3D is optional/gated; pure rubric functions carry the scoring load; VLM (llama.cpp) is the judge/explainer.
+
+**Tech Stack:** Python 3.11, Gradio 6.x, YOLO26-Pose, SAM 3.1, Qwen3-VL-8B (llama.cpp), pyskl ST-GCN, Qwen3-VL-Embedding-8B (llama.cpp), pytest, ruff/black
+
+---
+
+## Milestone Map
+
+| Milestone | Phase | Exit Criteria |
+|---|---|---|
+| **M0** | Recon | `RECON.md` exists, all models verified, Gradio version pinned |
+| **M1** | Spine | Deep Squat: `python -m formscout.run sample.mp4` → score + rationale |
+| **M2** | Gradio MVP | Upload Deep Squat clip → score + overlay in browser |
+| **M3** | All 7 Tests | Full scorecard, composite 0–21, asymmetry detection |
+| **M4** | Judge Online | Qwen3-VL via llama.cpp scoring + rationale for all tests |
+| **M5** | Learned Head | ST-GCN fine-tuned, published to Hub |
+| **M6** | RAG Online | Retrieval over physio clips anchors judge |
+| **M7** | Ship | All 6 badges, Space green, demo video, blog post |
+
+---
+
+## Phase 0 — Recon
+
+### Task 0.1: Scaffold repo & verify Gradio
+
+**Files:**
+- Create: `requirements.txt`
+- Create: `RECON.md`
+- Create: `MODEL_BUDGET.md`
+- Create: `formscout/__init__.py`
+- Create: `formscout/config.py`
+
+- [ ] **Step 1: Create the project scaffold**
+
+```bash
+mkdir -p formscout/agents/prompts formscout/rubric formscout/serving formscout/ui/custom tests
+touch formscout/__init__.py formscout/agents/__init__.py formscout/rubric/__init__.py
+touch formscout/serving/__init__.py formscout/ui/__init__.py
+touch app.py formscout/run.py formscout/pipeline.py formscout/types.py
+touch formscout/config.py formscout/tracing.py
+touch MODEL_BUDGET.md RECON.md README.md
+```
+
+- [ ] **Step 2: Verify current Gradio version and APIs**
+
+```bash
+pip install gradio --dry-run 2>&1 | head -5
+python -c "import gradio; print(gradio.__version__)"
+python -c "import gradio as gr; print(hasattr(gr, 'Walkthrough'), hasattr(gr, 'Navbar'), hasattr(gr.Video, 'playback_position') if hasattr(gr, 'Video') else 'no Video')"
+```
+
+Expected: version 6.x printed; note which APIs exist.
+
+- [ ] **Step 3: Write requirements.txt with pinned versions**
+
+```
+gradio==<verified-version>
+ultralytics>=8.3
+torch>=2.3
+opencv-python>=4.10
+numpy>=1.26
+scipy>=1.13
+pillow>=10.3
+pytest>=8.2
+ruff>=0.4
+black>=24.4
+huggingface_hub>=0.23
+transformers>=4.44
+```
+
+Note: llama.cpp added after build verification in Task 0.3.
+
+- [ ] **Step 4: Write config.py skeleton**
+
+```python
+from pathlib import Path
+
+ROOT = Path(__file__).parent.parent
+
+# Model IDs
+YOLO_POSE_MODEL = "yolo11x-pose.pt"
+SAM_CHECKPOINT = "sam2.1_hiera_base_plus.pt"
+QWEN_VLM_GGUF = "Qwen3-VL-8B-Instruct-Q4_K_M.gguf"
+QWEN_EMBED_GGUF = "Qwen3-VL-Embedding-8B-Q4_K_M.gguf"
+STGCN_CHECKPOINT = ROOT / "checkpoints" / "stgcn_fms.pth"
+
+# Pipeline flags
+ENABLE_3D = False          # SAM 3D Body — off until access granted
+ENABLE_STGCN = False       # Phase 3
+ENABLE_RAG = False         # Phase 3
+ENABLE_JUDGE = False       # Phase 2
+
+# Thresholds
+MIN_CONFIDENCE = 0.6
+SCORE_DISAGREE_THRESH = 1   # flag if |stgcn - judge| >= this
+RETRIEVAL_K = 3
+
+# Pose
+POSE_BACKEND = "yolo"       # "yolo" | "sapiens"
+POSE_CONF_THRESHOLD = 0.5
+NUM_KEYPOINTS = 17
+
+# Biomechanics
+DEEP_SQUAT_FEMUR_HORIZONTAL_DEG = 90.0  # femur below horizontal
+DEEP_SQUAT_TORSO_TIBIA_MAX_DEG = 15.0   # torso parallel to tibia
+DEEP_SQUAT_KNEE_TRACKING_MARGIN_PX = 20
+
+# Serving
+LLAMA_CPP_HOST = "127.0.0.1"
+LLAMA_CPP_PORT_VLM = 8080
+LLAMA_CPP_PORT_EMBED = 8081
+```
+
+- [ ] **Step 5: Verify model cards for license + params**
+
+```bash
+python -c "
+from huggingface_hub import model_info
+models = [
+    'Qwen/Qwen3-VL-8B-Instruct',
+    'Qwen/Qwen3-VL-Embedding-8B',
+]
+for m in models:
+    info = model_info(m)
+    print(m, '|', info.card_data.license if info.card_data else 'unknown')
+"
+```
+
+Manually check: `facebookresearch/sam3`, `facebook/sam-3d-body-dinov3` (gated), Ultralytics YOLO26.
+
+- [ ] **Step 6: Write RECON.md with findings**
+
+```markdown
+# RECON.md
+
+## Gradio
+- Version: <X.Y.Z>
+- gr.Blocks: ✓
+- gr.Video (playback_position): <y/n>
+- gr.Walkthrough / gr.Step: <y/n>
+- gr.Navbar: <y/n>
+- UI approach: gr.Blocks + custom CSS (escalate to Server only if needed)
+
+## Model Verification
+
+| Model | Params | License | GGUF | ZeroGPU | Status |
+|---|---|---|---|---|---|
+| YOLO26-Pose L | ~0.05B | AGPL-3.0 | n/a | ✓ | ready |
+| SAM 3.1 base | ~0.85B | SAM License | n/a | ✓ | access pending |
+| SAM 3D Body | ~0.7B | SAM License | n/a | tbd | access pending |
+| ST-GCN (pyskl) | ~0.03B | Apache-2.0 | n/a | ✓ | ready |
+| Qwen3-VL-8B-Instruct | 8B | Apache-2.0 | ✓ | llama.cpp | ready |
+| Qwen3-VL-Embedding-8B | 8B | Apache-2.0 | ✓ | llama.cpp | ready |
+
+## Param Sum
+~17.8B — well under 32B limit.
+
+## Open Questions
+- [ ] Confirm "≤32B" = summed vs per-model in Discord AMA
+- [ ] SAM 3D Body gated access status
+- [ ] AGPL-3.0 YOLO OK for hackathon submission?
+
+## llama.cpp Build Plan
+- CPU-only build first (avoids libcudart.so issues on Spaces)
+- Fallback: transformers + spaces.GPU for VLM
+```
+
+- [ ] **Step 7: Write MODEL_BUDGET.md**
+
+```markdown
+# MODEL_BUDGET.md
+
+Running sum must stay ≤ 32B params.
+
+| Component | Model | Params |
+|---|---|---|
+| 2D Pose | YOLO26-Pose L | 0.05B |
+| Segmentation | SAM 3.1 base | 0.85B |
+| 3D Body (optional) | SAM 3D Body | ~0.7B |
+| Scoring Head | ST-GCN (pyskl) | 0.03B |
+| Judge/Explainer | Qwen3-VL-8B-Instruct | 8B |
+| Retrieval | Qwen3-VL-Embedding-8B | 8B |
+| **Total** | | **~17.63B** |
+
+Headroom: ~14.37B under 32B cap.
+```
+
+- [ ] **Step 8: Commit Phase 0 scaffold**
+
+```bash
+git init && git add -A
+git commit -m "chore: Phase 0 scaffold — repo structure, config, recon, model budget"
+```
+
+**✅ MILESTONE M0: RECON.md exists, param sum tracked, Gradio version pinned**
+
+---
+
+## Phase 1 — The Spine (Deep Squat, headless)
+
+### Task 1.1: types.py — all agent contracts
+
+**Files:**
+- Create: `formscout/types.py`
+- Create: `tests/test_types.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_types.py
+from formscout.types import (
+    IngestResult, SegmentResult, Pose2DResult, Body3DResult,
+    MovementResult, BiomechFeatures, ScoreResult, RetrievalResult,
+    JudgeResult, ReportResult, PipelineState,
+)
+import pytest
+
+def test_ingest_result_frozen():
+    r = IngestResult(frames=[], fps=30.0, duration=2.0, n_people=1, width=1920, height=1080)
+    with pytest.raises(Exception):
+        r.fps = 60.0
+
+def test_judge_result_needs_human_default_false():
+    r = JudgeResult(score=2, rationale="ok", compensation_tags=[], corrective_hint="", confidence=0.9, needs_human=False, notes="")
+    assert r.needs_human is False
+
+def test_score_result_valid_range():
+    with pytest.raises(ValueError):
+        ScoreResult(score=4, rationale="bad", confidence=0.9, needs_human=False, notes="")
+
+def test_bilateral_features_has_symmetry():
+    f = BiomechFeatures(
+        test_name="hurdle_step",
+        view="2d",
+        side="left",
+        angles={"hip_flexion": 45.0},
+        alignments={},
+        symmetry_delta=None,
+        timing={},
+        confidence=0.8,
+        notes="",
+    )
+    assert f.side == "left"
+```
+
+- [ ] **Step 2: Run test — expect ImportError**
+
+```bash
+pytest tests/test_types.py -v
+```
+
+Expected: `ImportError: cannot import name 'IngestResult'`
+
+- [ ] **Step 3: Implement types.py**
+
+```python
+# formscout/types.py
+from __future__ import annotations
+from dataclasses import dataclass, field
+from typing import Any
+
+@dataclass(frozen=True)
+class IngestResult:
+    frames: list        # list of np.ndarray HWC BGR
+    fps: float
+    duration: float
+    n_people: int
+    width: int
+    height: int
+    confidence: float = 1.0
+    notes: str = ""
+
+@dataclass(frozen=True)
+class SegmentResult:
+    athlete_track_id: int
+    masks: list         # list of np.ndarray bool HW per frame
+    confidence: float
+    notes: str = ""
+
+@dataclass(frozen=True)
+class Pose2DResult:
+    keypoints: list     # list[dict[int, dict]] frame→joint→{x,y,conf}
+    fps: float
+    confidence: float
+    notes: str = ""
+
+@dataclass(frozen=True)
+class Body3DResult:
+    used: bool
+    joints_3d: list     # list[dict] frame→joint→{x,y,z} — empty if used=False
+    confidence: float = 0.0
+    notes: str = ""
+
+@dataclass(frozen=True)
+class MovementResult:
+    test_name: str      # "deep_squat"|"hurdle_step"|...|"unknown"
+    side: str           # "left"|"right"|"bilateral"|"na"
+    confidence: float
+    notes: str = ""
+
+@dataclass(frozen=True)
+class BiomechFeatures:
+    test_name: str
+    view: str           # "2d" | "3d"
+    side: str           # "left"|"right"|"na"
+    angles: dict        # named angle → degrees
+    alignments: dict    # named alignment → value
+    symmetry_delta: float | None   # |left - right| or None for non-bilateral
+    timing: dict        # event name → frame index
+    confidence: float
+    notes: str = ""
+
+@dataclass(frozen=True)
+class ScoreResult:
+    score: int          # 0–3
+    rationale: str
+    confidence: float
+    needs_human: bool
+    notes: str = ""
+
+    def __post_init__(self):
+        if not 0 <= self.score <= 3:
+            raise ValueError(f"score must be 0–3, got {self.score}")
+
+@dataclass(frozen=True)
+class RetrievalResult:
+    exemplars: list     # list of {clip_id, score, similarity, rationale}
+    confidence: float = 1.0
+    notes: str = ""
+
+@dataclass(frozen=True)
+class JudgeResult:
+    score: int          # 0–3; -1 if needs_human=True (not auto-scored)
+    rationale: str
+    compensation_tags: list
+    corrective_hint: str
+    confidence: float
+    needs_human: bool
+    notes: str = ""
+
+    def __post_init__(self):
+        if not self.needs_human and not 0 <= self.score <= 3:
+            raise ValueError(f"score must be 0–3 when needs_human=False, got {self.score}")
+
+@dataclass(frozen=True)
+class ReportResult:
+    per_test: list      # list of dicts with test_name, score, judge_result, features
+    composite: int | None   # None if any test unscored
+    asymmetries: list   # list of {test, left_score, right_score, delta}
+    overlay_video_path: str | None
+    pdf_path: str | None
+    low_confidence_flags: list
+    disagreement_flags: list
+    notes: str = ""
+
+@dataclass
+class PipelineState:
+    """Mutable state threaded through the Director."""
+    video_path: str
+    ingest: IngestResult | None = None
+    segment: SegmentResult | None = None
+    pose2d: Pose2DResult | None = None
+    body3d: Body3DResult | None = None
+    movement: MovementResult | None = None
+    features: BiomechFeatures | None = None
+    stgcn_score: ScoreResult | None = None
+    retrieval: RetrievalResult | None = None
+    judge: JudgeResult | None = None
+    report: ReportResult | None = None
+    errors: list = field(default_factory=list)
+    warnings: list = field(default_factory=list)
+```
+
+- [ ] **Step 4: Run tests — expect PASS**
+
+```bash
+pytest tests/test_types.py -v
+```
+
+Expected: 4 passed.
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add formscout/types.py tests/test_types.py
+git commit -m "feat: typed agent contracts in types.py with validation"
+```
+
+---
+
+### Task 1.2: IngestAgent
+
+**Files:**
+- Create: `formscout/agents/ingest.py`
+- Create: `tests/fixtures/sample_squat.mp4` (use any short video for testing)
+- Create: `tests/test_ingest.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_ingest.py
+import pytest
+from pathlib import Path
+from formscout.agents.ingest import IngestAgent
+from formscout.types import IngestResult
+
+FIXTURE = Path("tests/fixtures/sample_squat.mp4")
+
+def test_ingest_returns_typed_result(tmp_path):
+    # Create a minimal 1-second test video using OpenCV
+    import cv2, numpy as np
+    p = tmp_path / "test.mp4"
+    out = cv2.VideoWriter(str(p), cv2.VideoWriter_fourcc(*'mp4v'), 30, (640, 480))
+    for _ in range(30):
+        out.write(np.zeros((480, 640, 3), dtype=np.uint8))
+    out.release()
+
+    agent = IngestAgent()
+    result = agent.run(str(p))
+    assert isinstance(result, IngestResult)
+    assert result.fps == pytest.approx(30.0, abs=2.0)
+    assert len(result.frames) > 0
+    assert result.width == 640
+    assert result.height == 480
+
+def test_ingest_rejects_missing_file():
+    agent = IngestAgent()
+    result = agent.run("/nonexistent/path.mp4")
+    assert result.confidence == 0.0
+    assert "not found" in result.notes.lower()
+
+def test_ingest_result_is_frozen():
+    import cv2, numpy as np, tempfile, os
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
+        p = f.name
+    out = cv2.VideoWriter(p, cv2.VideoWriter_fourcc(*'mp4v'), 30, (64, 64))
+    for _ in range(10):
+        out.write(np.zeros((64, 64, 3), dtype=np.uint8))
+    out.release()
+    agent = IngestAgent()
+    result = agent.run(p)
+    os.unlink(p)
+    with pytest.raises(Exception):
+        result.fps = 999.0
+```
+
+- [ ] **Step 2: Run — expect ImportError**
+
+```bash
+pytest tests/test_ingest.py -v
+```
+
+- [ ] **Step 3: Implement IngestAgent**
+
+```python
+# formscout/agents/ingest.py
+"""
+IngestAgent — decodes video, normalizes FPS, samples frames.
+Input:  video file path (str)
+Output: IngestResult(frames, fps, duration, n_people, width, height)
+Failure: returns IngestResult with confidence=0.0 and notes explaining the error.
+Params: 0 (no model — pure OpenCV).
+License: n/a.
+Gated: no.
+"""
+import cv2
+from pathlib import Path
+from formscout.types import IngestResult
+from formscout import config
+
+MAX_FRAMES = 300  # hard cap to avoid OOM on long videos
+
+class IngestAgent:
+    def run(self, video_path: str) -> IngestResult:
+        p = Path(video_path)
+        if not p.exists():
+            return IngestResult(frames=[], fps=0.0, duration=0.0, n_people=0,
+                                width=0, height=0, confidence=0.0,
+                                notes=f"video not found: {video_path}")
+        cap = cv2.VideoCapture(str(p))
+        if not cap.isOpened():
+            return IngestResult(frames=[], fps=0.0, duration=0.0, n_people=0,
+                                width=0, height=0, confidence=0.0,
+                                notes=f"could not open video: {video_path}")
+        fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+        total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        duration = total / fps if fps > 0 else 0.0
+
+        step = max(1, total // MAX_FRAMES)
+        frames, idx = [], 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            if idx % step == 0:
+                frames.append(frame)
+            idx += 1
+        cap.release()
+
+        if not frames:
+            return IngestResult(frames=[], fps=fps, duration=duration, n_people=0,
+                                width=w, height=h, confidence=0.0,
+                                notes="no frames decoded")
+        return IngestResult(frames=frames, fps=fps, duration=duration,
+                            n_people=-1,  # unknown until segmentation
+                            width=w, height=h, confidence=1.0)
+```
+
+- [ ] **Step 4: Run tests — expect PASS**
+
+```bash
+pytest tests/test_ingest.py -v
+```
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add formscout/agents/ingest.py tests/test_ingest.py
+git commit -m "feat: IngestAgent — OpenCV video decode with frame sampling"
+```
+
+---
+
+### Task 1.3: Pose2DAgent (YOLO)
+
+**Files:**
+- Create: `formscout/agents/pose2d.py`
+- Create: `tests/test_pose2d.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_pose2d.py
+import numpy as np
+import pytest
+from formscout.agents.pose2d import Pose2DAgent
+from formscout.types import Pose2DResult, IngestResult
+
+def _blank_ingest(n_frames=5, w=640, h=480):
+    frames = [np.zeros((h, w, 3), dtype=np.uint8) for _ in range(n_frames)]
+    return IngestResult(frames=frames, fps=30.0, duration=n_frames/30.0,
+                        n_people=1, width=w, height=h)
+
+def test_pose2d_returns_typed_result():
+    agent = Pose2DAgent()
+    result = agent.run(_blank_ingest())
+    assert isinstance(result, Pose2DResult)
+    assert isinstance(result.keypoints, list)
+    assert result.fps == pytest.approx(30.0)
+
+def test_pose2d_keypoints_per_frame():
+    agent = Pose2DAgent()
+    ingest = _blank_ingest(n_frames=3)
+    result = agent.run(ingest)
+    # blank frames will have no detections — should return empty dicts, not crash
+    assert len(result.keypoints) == 3
+    for frame_kps in result.keypoints:
+        assert isinstance(frame_kps, dict)
+
+def test_pose2d_graceful_on_empty_frames():
+    empty = IngestResult(frames=[], fps=30.0, duration=0.0,
+                         n_people=0, width=640, height=480)
+    agent = Pose2DAgent()
+    result = agent.run(empty)
+    assert result.confidence == 0.0
+    assert "no frames" in result.notes.lower()
+```
+
+- [ ] **Step 2: Run — expect ImportError**
+
+```bash
+pytest tests/test_pose2d.py -v
+```
+
+- [ ] **Step 3: Implement Pose2DAgent**
+
+```python
+# formscout/agents/pose2d.py
+"""
+Pose2DAgent — 2D per-frame keypoint extraction.
+Input:  IngestResult
+Output: Pose2DResult(keypoints per frame, fps, confidence)
+Failure: returns Pose2DResult with confidence=0.0 and notes.
+Model:  YOLO26-Pose L (AGPL-3.0, ~0.05B params, public).
+Gated: no.
+"""
+from __future__ import annotations
+import numpy as np
+from formscout import config
+from formscout.types import IngestResult, Pose2DResult
+
+_model = None
+
+def _get_model():
+    global _model
+    if _model is None:
+        from ultralytics import YOLO
+        _model = YOLO(config.YOLO_POSE_MODEL)
+    return _model
+
+
+class Pose2DAgent:
+    def run(self, ingest: IngestResult) -> Pose2DResult:
+        if not ingest.frames:
+            return Pose2DResult(keypoints=[], fps=ingest.fps,
+                                confidence=0.0, notes="no frames in ingest")
+        model = _get_model()
+        keypoints_per_frame: list[dict] = []
+        total_conf = 0.0
+        n_detected = 0
+
+        for frame in ingest.frames:
+            results = model(frame, verbose=False)
+            frame_kps: dict[int, dict] = {}
+            if results and results[0].keypoints is not None:
+                kps = results[0].keypoints
+                if len(kps) > 0:
+                    # Take highest-confidence person (index 0 after YOLO NMS sort)
+                    xy = kps.xy[0].cpu().numpy()     # (17, 2)
+                    conf = kps.conf[0].cpu().numpy()  # (17,)
+                    for j in range(len(xy)):
+                        frame_kps[j] = {"x": float(xy[j, 0]),
+                                        "y": float(xy[j, 1]),
+                                        "conf": float(conf[j])}
+                    total_conf += float(conf.mean())
+                    n_detected += 1
+            keypoints_per_frame.append(frame_kps)
+
+        overall_conf = (total_conf / n_detected) if n_detected > 0 else 0.0
+        notes = "" if n_detected > 0 else "no person detected in any frame"
+        return Pose2DResult(keypoints=keypoints_per_frame, fps=ingest.fps,
+                            confidence=overall_conf, notes=notes)
+```
+
+- [ ] **Step 4: Run tests — expect PASS**
+
+```bash
+pytest tests/test_pose2d.py -v
+```
+
+Note: blank frames will yield no detections — that is correct behavior.
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add formscout/agents/pose2d.py tests/test_pose2d.py
+git commit -m "feat: Pose2DAgent — YOLO26-Pose keypoint extraction"
+```
+
+---
+
+### Task 1.4: Body3DAgent (stub — gated model)
+
+**Files:**
+- Create: `formscout/agents/body3d.py`
+- Create: `tests/test_body3d.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_body3d.py
+from formscout.agents.body3d import Body3DAgent
+from formscout.types import Body3DResult, Pose2DResult
+
+def _dummy_pose():
+    return Pose2DResult(keypoints=[{0: {"x": 320.0, "y": 240.0, "conf": 0.9}}],
+                        fps=30.0, confidence=0.9)
+
+def test_body3d_disabled_returns_not_used():
+    agent = Body3DAgent(enable_3d=False)
+    result = agent.run(_dummy_pose(), masks=[])
+    assert isinstance(result, Body3DResult)
+    assert result.used is False
+    assert result.joints_3d == []
+
+def test_body3d_unavailable_checkpoint_returns_not_used(monkeypatch):
+    monkeypatch.setattr("formscout.config.ENABLE_3D", True)
+    agent = Body3DAgent(enable_3d=True)
+    # No checkpoint present → graceful fallback
+    result = agent.run(_dummy_pose(), masks=[])
+    assert result.used is False
+```
+
+- [ ] **Step 2: Run — expect ImportError**
+
+```bash
+pytest tests/test_body3d.py -v
+```
+
+- [ ] **Step 3: Implement Body3DAgent stub**
+
+```python
+# formscout/agents/body3d.py
+"""
+Body3DAgent — optional 3D mesh/joint angle recovery via SAM 3D Body.
+Input:  Pose2DResult, list of athlete masks
+Output: Body3DResult(used, joints_3d, confidence)
+Failure: ALWAYS returns Body3DResult(used=False) when enable_3d=False or
+         checkpoint unavailable — this is a normal success path, not an error.
+Model:  facebook/sam-3d-body-dinov3 (~0.7B, SAM License, GATED — access pending).
+Gated: YES — access requested June 2026.
+"""
+from __future__ import annotations
+from formscout.types import Pose2DResult, Body3DResult
+from formscout import config
+
+_NOT_USED = Body3DResult(used=False, joints_3d=[], confidence=0.0,
+                          notes="3D disabled or checkpoint unavailable")
+
+
+class Body3DAgent:
+    def __init__(self, enable_3d: bool | None = None):
+        self._enabled = config.ENABLE_3D if enable_3d is None else enable_3d
+        self._model = None
+        if self._enabled:
+            self._model = self._try_load()
+
+    def _try_load(self):
+        try:
+            # Placeholder: replace with actual SAM 3D Body load once access granted
+            from pathlib import Path
+            ckpt = Path("checkpoints/sam3d_body.pth")
+            if not ckpt.exists():
+                return None
+            # TODO: load SAM 3D Body model here
+            return None
+        except Exception:
+            return None
+
+    def run(self, pose2d: Pose2DResult, masks: list) -> Body3DResult:
+        if not self._enabled or self._model is None:
+            return _NOT_USED
+        # TODO: implement SAM 3D Body inference when access granted
+        return _NOT_USED
+```
+
+- [ ] **Step 4: Run tests — expect PASS**
+
+```bash
+pytest tests/test_body3d.py -v
+```
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add formscout/agents/body3d.py tests/test_body3d.py
+git commit -m "feat: Body3DAgent stub — graceful fallback until SAM 3D Body access granted"
+```
+
+---
+
+### Task 1.5: BiomechanicsAgent + Deep Squat rubric
+
+**Files:**
+- Create: `formscout/rubric/deep_squat.py`
+- Create: `formscout/agents/biomechanics.py`
+- Create: `tests/test_biomechanics.py`
+
+- [ ] **Step 1: Write failing tests**
+
+```python
+# tests/test_biomechanics.py
+import pytest
+from formscout.rubric.deep_squat import score_deep_squat
+from formscout.types import BiomechFeatures, ScoreResult
+
+def _features(femur_below_horiz=True, torso_parallel_tibia=True,
+               knees_tracking=True, dowel_over_feet=True,
+               heels_elevated=False, view="2d"):
+    return BiomechFeatures(
+        test_name="deep_squat",
+        view=view,
+        side="na",
+        angles={
+            "femur_from_horizontal_deg": 15.0 if femur_below_horiz else 95.0,
+            "torso_tibia_angle_deg": 10.0 if torso_parallel_tibia else 40.0,
+        },
+        alignments={
+            "knees_tracking_over_feet": knees_tracking,
+            "dowel_over_feet": dowel_over_feet,
+            "heels_elevated": heels_elevated,
+        },
+        symmetry_delta=None,
+        timing={},
+        confidence=0.9,
+    )
+
+def test_deep_squat_score_3():
+    result = score_deep_squat(_features())
+    assert isinstance(result, ScoreResult)
+    assert result.score == 3
+    assert not result.needs_human
+
+def test_deep_squat_score_2_heels_elevated():
+    result = score_deep_squat(_features(heels_elevated=True))
+    assert result.score == 2
+
+def test_deep_squat_score_1_criteria_unmet_even_with_heels():
+    result = score_deep_squat(_features(
+        femur_below_horiz=False, heels_elevated=True
+    ))
+    assert result.score == 1
+
+def test_deep_squat_score_0_pain():
+    f = _features()
+    # Override: simulate pain flag via needs_human in features
+    result = score_deep_squat(f, pain=True)
+    assert result.score == 0
+    assert result.needs_human is True
+
+def test_deep_squat_rationale_mentions_deciding_factor():
+    result = score_deep_squat(_features(femur_below_horiz=False))
+    assert "femur" in result.rationale.lower() or "depth" in result.rationale.lower()
+```
+
+- [ ] **Step 2: Run — expect ImportError**
+
+```bash
+pytest tests/test_biomechanics.py -v
+```
+
+- [ ] **Step 3: Implement deep_squat.py rubric**
+
+```python
+# formscout/rubric/deep_squat.py
+"""
+Pure function: score_deep_squat(features, pain=False) -> ScoreResult.
+FMS Deep Squat rubric (0–3). No model calls.
+"""
+from formscout.types import BiomechFeatures, ScoreResult
+
+# Thresholds
+FEMUR_BELOW_HORIZ_DEG = 90.0   # femur angle from vertical; <90 = below horizontal
+TORSO_TIBIA_MAX_DEG = 15.0     # degrees between torso and tibia long axis
+
+
+def score_deep_squat(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain or clearing test flagged — defer to physio.",
+                           confidence=1.0, needs_human=True)
+
+    femur_deg = features.angles.get("femur_from_horizontal_deg", 999.0)
+    torso_tibia_deg = features.angles.get("torso_tibia_angle_deg", 999.0)
+    knees_ok = features.alignments.get("knees_tracking_over_feet", False)
+    dowel_ok = features.alignments.get("dowel_over_feet", False)
+    heels_elevated = features.alignments.get("heels_elevated", False)
+
+    # 3: all four criteria met, flat feet
+    criteria_3 = (femur_deg < FEMUR_BELOW_HORIZ_DEG and
+                  torso_tibia_deg < TORSO_TIBIA_MAX_DEG and
+                  knees_ok and dowel_ok)
+
+    # 2: criteria met only with heels elevated
+    criteria_2 = heels_elevated and (
+        femur_deg < FEMUR_BELOW_HORIZ_DEG and
+        torso_tibia_deg < TORSO_TIBIA_MAX_DEG and
+        knees_ok and dowel_ok
+    )
+
+    view_note = " (2D measurement — camera angle may affect accuracy)" if features.view == "2d" else ""
+
+    if criteria_3:
+        return ScoreResult(
+            score=3,
+            rationale=f"All criteria met: femur {femur_deg:.1f}° below horizontal, "
+                      f"torso–tibia {torso_tibia_deg:.1f}°, knees tracking, dowel overhead.{view_note}",
+            confidence=features.confidence,
+            needs_human=False,
+        )
+    elif criteria_2:
+        return ScoreResult(
+            score=2,
+            rationale=f"Criteria met only with heel elevation.{view_note}",
+            confidence=features.confidence,
+            needs_human=False,
+        )
+    else:
+        # Identify the failing criterion for the rationale
+        failures = []
+        if femur_deg >= FEMUR_BELOW_HORIZ_DEG:
+            failures.append(f"insufficient squat depth (femur {femur_deg:.1f}° — needs <{FEMUR_BELOW_HORIZ_DEG}°)")
+        if torso_tibia_deg >= TORSO_TIBIA_MAX_DEG:
+            failures.append(f"torso–tibia angle {torso_tibia_deg:.1f}° (needs <{TORSO_TIBIA_MAX_DEG}°)")
+        if not knees_ok:
+            failures.append("knees not tracking over feet")
+        if not dowel_ok:
+            failures.append("dowel not over feet")
+        reason = "; ".join(failures) if failures else "criteria not met"
+        return ScoreResult(
+            score=1,
+            rationale=f"Score 1: {reason}.{view_note}",
+            confidence=features.confidence,
+            needs_human=False,
+        )
+```
+
+- [ ] **Step 4: Implement BiomechanicsAgent (Deep Squat)**
+
+```python
+# formscout/agents/biomechanics.py
+"""
+BiomechanicsAgent — computes rubric-relevant measurements from pose keypoints.
+Input:  Pose2DResult, Body3DResult, MovementResult
+Output: BiomechFeatures(test_name, view, side, angles, alignments, ...)
+Failure: returns low-confidence BiomechFeatures with notes.
+Params: 0 (geometry only).
+Gated: no.
+"""
+from __future__ import annotations
+import numpy as np
+from formscout.types import Pose2DResult, Body3DResult, MovementResult, BiomechFeatures
+from formscout import config
+
+# COCO keypoint indices
+HIP_L, HIP_R = 11, 12
+KNEE_L, KNEE_R = 13, 14
+ANKLE_L, ANKLE_R = 15, 16
+SHOULDER_L, SHOULDER_R = 5, 6
+NOSE = 0
+
+
+def _angle_2d(a, b, c) -> float:
+    """Angle at vertex b formed by segments b→a and b→c, in degrees."""
+    ba = np.array(a) - np.array(b)
+    bc = np.array(c) - np.array(b)
+    cos = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc) + 1e-9)
+    return float(np.degrees(np.arccos(np.clip(cos, -1.0, 1.0))))
+
+
+def _median_kp(keypoints: list[dict], joint: int) -> tuple[float, float, float]:
+    """Median x, y, conf across frames for a keypoint joint index."""
+    xs, ys, cs = [], [], []
+    for frame in keypoints:
+        kp = frame.get(joint)
+        if kp and kp["conf"] > config.POSE_CONF_THRESHOLD:
+            xs.append(kp["x"]); ys.append(kp["y"]); cs.append(kp["conf"])
+    if not xs:
+        return 0.0, 0.0, 0.0
+    return float(np.median(xs)), float(np.median(ys)), float(np.median(cs))
+
+
+def _compute_deep_squat_2d(pose2d: Pose2DResult) -> BiomechFeatures:
+    kps = pose2d.keypoints
+    hip_lx, hip_ly, hip_lc = _median_kp(kps, HIP_L)
+    knee_lx, knee_ly, knee_lc = _median_kp(kps, KNEE_L)
+    ankle_lx, ankle_ly, ankle_lc = _median_kp(kps, ANKLE_L)
+    shoulder_lx, shoulder_ly, _ = _median_kp(kps, SHOULDER_L)
+
+    conf = np.mean([c for c in [hip_lc, knee_lc, ankle_lc] if c > 0] or [0.0])
+
+    # Femur angle from horizontal: angle of hip→knee vector from x-axis
+    femur_vec = np.array([knee_lx - hip_lx, knee_ly - hip_ly])
+    femur_from_horiz = float(abs(np.degrees(np.arctan2(
+        abs(femur_vec[1]), abs(femur_vec[0]) + 1e-9
+    ))))
+
+    # Torso–tibia angle: angle between hip→shoulder and ankle→knee vectors
+    torso_vec = np.array([shoulder_lx - hip_lx, shoulder_ly - hip_ly])
+    tibia_vec = np.array([knee_lx - ankle_lx, knee_ly - ankle_ly])
+    cos_tt = np.dot(torso_vec, tibia_vec) / (
+        np.linalg.norm(torso_vec) * np.linalg.norm(tibia_vec) + 1e-9
+    )
+    torso_tibia_deg = float(np.degrees(np.arccos(np.clip(cos_tt, -1, 1))))
+
+    # Knee tracking over foot: knee x should be within margin of ankle x
+    knees_tracking = abs(knee_lx - ankle_lx) < config.DEEP_SQUAT_KNEE_TRACKING_MARGIN_PX
+
+    # Heels: if ankle is significantly above baseline (proxy for heel elevation)
+    heels_elevated = False  # requires side-view calibration; set conservatively
+
+    return BiomechFeatures(
+        test_name="deep_squat",
+        view="2d",
+        side="na",
+        angles={
+            "femur_from_horizontal_deg": femur_from_horiz,
+            "torso_tibia_angle_deg": torso_tibia_deg,
+        },
+        alignments={
+            "knees_tracking_over_feet": knees_tracking,
+            "dowel_over_feet": False,       # requires dowel detection (Phase 2+)
+            "heels_elevated": heels_elevated,
+        },
+        symmetry_delta=None,
+        timing={},
+        confidence=float(conf),
+        notes="2D measurements; heel elevation detection requires calibration",
+    )
+
+
+class BiomechanicsAgent:
+    def run(self, pose2d: Pose2DResult, body3d: Body3DResult,
+            movement: MovementResult) -> BiomechFeatures:
+        if movement.test_name == "deep_squat":
+            if body3d.used:
+                # TODO: implement 3D feature extraction (Phase 1.5+)
+                pass
+            return _compute_deep_squat_2d(pose2d)
+        # Other tests — Phase 2
+        return BiomechFeatures(
+            test_name=movement.test_name, view="2d", side="na",
+            angles={}, alignments={}, symmetry_delta=None, timing={},
+            confidence=0.0, notes=f"test '{movement.test_name}' not yet implemented",
+        )
+```
+
+- [ ] **Step 5: Run tests — expect PASS**
+
+```bash
+pytest tests/test_biomechanics.py -v
+```
+
+- [ ] **Step 6: Commit**
+
+```bash
+git add formscout/rubric/deep_squat.py formscout/agents/biomechanics.py tests/test_biomechanics.py
+git commit -m "feat: Deep Squat rubric (pure fn) + BiomechanicsAgent 2D geometry"
+```
+
+---
+
+### Task 1.6: Headless pipeline (Director + run.py)
+
+**Files:**
+- Create: `formscout/pipeline.py`
+- Create: `formscout/run.py`
+- Create: `tests/test_pipeline.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_pipeline.py
+import numpy as np
+import pytest
+from unittest.mock import patch, MagicMock
+from formscout.pipeline import Director
+from formscout.types import (
+    IngestResult, Pose2DResult, Body3DResult, MovementResult,
+    BiomechFeatures, ScoreResult, JudgeResult, PipelineState
+)
+
+def _mock_ingest():
+    frames = [np.zeros((480, 640, 3), dtype=np.uint8)]
+    return IngestResult(frames=frames, fps=30.0, duration=1.0,
+                        n_people=1, width=640, height=480)
+
+def _mock_pose2d():
+    return Pose2DResult(
+        keypoints=[{11: {"x": 320.0, "y": 200.0, "conf": 0.9},
+                    13: {"x": 300.0, "y": 280.0, "conf": 0.9},
+                    15: {"x": 295.0, "y": 360.0, "conf": 0.9},
+                    5:  {"x": 320.0, "y": 150.0, "conf": 0.9}}],
+        fps=30.0, confidence=0.9
+    )
+
+def test_director_runs_deep_squat_headless(tmp_path):
+    video = tmp_path / "test.mp4"
+    video.write_bytes(b"")  # placeholder path
+
+    with patch("formscout.pipeline.IngestAgent") as MockIngest, \
+         patch("formscout.pipeline.Pose2DAgent") as MockPose, \
+         patch("formscout.pipeline.Body3DAgent") as MockBody3D, \
+         patch("formscout.pipeline.BiomechanicsAgent") as MockBiomech, \
+         patch("formscout.pipeline.MovementClassifierAgent") as MockClassify:
+
+        MockIngest.return_value.run.return_value = _mock_ingest()
+        MockPose.return_value.run.return_value = _mock_pose2d()
+        MockBody3D.return_value.run.return_value = Body3DResult(used=False, joints_3d=[], confidence=0.0)
+        MockClassify.return_value.run.return_value = MovementResult(
+            test_name="deep_squat", side="na", confidence=0.95)
+        mock_features = BiomechFeatures(
+            test_name="deep_squat", view="2d", side="na",
+            angles={"femur_from_horizontal_deg": 80.0, "torso_tibia_angle_deg": 12.0},
+            alignments={"knees_tracking_over_feet": True, "dowel_over_feet": True, "heels_elevated": False},
+            symmetry_delta=None, timing={}, confidence=0.9)
+        MockBiomech.return_value.run.return_value = mock_features
+
+        director = Director()
+        state = director.run(str(video))
+
+    assert isinstance(state, PipelineState)
+    assert state.judge is not None or state.features is not None
+    assert not state.errors
+
+def test_director_flags_low_confidence():
+    # If pose confidence < MIN_CONFIDENCE, warnings should be appended
+    from formscout import config
+    assert config.MIN_CONFIDENCE > 0
+```
+
+- [ ] **Step 2: Run — expect ImportError**
+
+```bash
+pytest tests/test_pipeline.py -v
+```
+
+- [ ] **Step 3: Implement pipeline.py Director**
+
+```python
+# formscout/pipeline.py
+"""
+Director — deterministic state machine orchestrating all agents.
+Not an LLM. Applies quality gates and builds PipelineState.
+"""
+from __future__ import annotations
+from formscout import config
+from formscout.types import PipelineState, JudgeResult, ScoreResult
+from formscout.agents.ingest import IngestAgent
+from formscout.agents.pose2d import Pose2DAgent
+from formscout.agents.body3d import Body3DAgent
+from formscout.agents.biomechanics import BiomechanicsAgent
+from formscout.agents.classify import MovementClassifierAgent
+from formscout.rubric.deep_squat import score_deep_squat
+from formscout.tracing import Tracer
+
+
+class Director:
+    def __init__(self):
+        self.ingest = IngestAgent()
+        self.pose2d = Pose2DAgent()
+        self.body3d = Body3DAgent()
+        self.classify = MovementClassifierAgent()
+        self.biomech = BiomechanicsAgent()
+        self.tracer = Tracer()
+
+    def run(self, video_path: str) -> PipelineState:
+        state = PipelineState(video_path=video_path)
+
+        # --- Ingest ---
+        state.ingest = self.ingest.run(video_path)
+        self.tracer.record("ingest", state.ingest)
+        if state.ingest.confidence == 0.0:
+            state.errors.append(f"Ingest failed: {state.ingest.notes}")
+            return state
+
+        # --- 2D Pose ---
+        state.pose2d = self.pose2d.run(state.ingest)
+        self.tracer.record("pose2d", state.pose2d)
+        if state.pose2d.confidence < config.MIN_CONFIDENCE:
+            state.warnings.append(
+                f"Pose2D low confidence ({state.pose2d.confidence:.2f}) — physio review recommended"
+            )
+
+        # --- 3D Body (optional) ---
+        state.body3d = self.body3d.run(state.pose2d, [])
+        self.tracer.record("body3d", state.body3d)
+
+        # --- Movement Classifier ---
+        state.movement = self.classify.run(state.ingest, state.pose2d)
+        self.tracer.record("movement", state.movement)
+        if state.movement.test_name == "unknown":
+            state.errors.append("Movement classification failed — manual override required")
+            return state
+        if state.movement.confidence < config.MIN_CONFIDENCE:
+            state.warnings.append(
+                f"Movement classifier low confidence ({state.movement.confidence:.2f})"
+            )
+
+        # --- Biomechanics ---
+        state.features = self.biomech.run(state.pose2d, state.body3d, state.movement)
+        self.tracer.record("biomechanics", state.features)
+        if state.features.confidence < config.MIN_CONFIDENCE:
+            state.warnings.append(
+                f"Biomechanics low confidence ({state.features.confidence:.2f})"
+            )
+
+        # --- Deterministic Rubric Score (Phase 1: no STGCN or Judge yet) ---
+        if state.movement.test_name == "deep_squat" and not config.ENABLE_JUDGE:
+            rubric_score = score_deep_squat(state.features)
+            state.judge = JudgeResult(
+                score=rubric_score.score,
+                rationale=rubric_score.rationale,
+                compensation_tags=[],
+                corrective_hint="",
+                confidence=rubric_score.confidence,
+                needs_human=rubric_score.needs_human,
+                notes="deterministic rubric (no VLM judge in Phase 1)",
+            )
+            self.tracer.record("judge", state.judge)
+
+        return state
+```
+
+- [ ] **Step 4: Implement MovementClassifierAgent stub**
+
+```python
+# formscout/agents/classify.py
+"""
+MovementClassifierAgent — identifies which of 7 FMS tests is being performed.
+Phase 1: returns 'deep_squat' stub (VLM classifier wired in Phase 2).
+Input:  IngestResult, Pose2DResult
+Output: MovementResult(test_name, side, confidence)
+"""
+from formscout.types import IngestResult, Pose2DResult, MovementResult
+
+
+class MovementClassifierAgent:
+    def run(self, ingest: IngestResult, pose2d: Pose2DResult) -> MovementResult:
+        # Phase 1 stub — always returns deep_squat
+        # Phase 2: replace with VLM or small classifier
+        return MovementResult(
+            test_name="deep_squat",
+            side="na",
+            confidence=0.5,
+            notes="Phase 1 stub — always deep_squat",
+        )
+```
+
+- [ ] **Step 5: Implement tracing.py**
+
+```python
+# formscout/tracing.py
+"""Structured per-agent I/O logger. One full run can be exported to Hub."""
+import json
+from dataclasses import asdict
+from datetime import datetime
+from pathlib import Path
+
+
+class Tracer:
+    def __init__(self):
+        self._records: list[dict] = []
+        self._run_id = datetime.utcnow().strftime("%Y%m%dT%H%M%S")
+
+    def record(self, agent_name: str, result) -> None:
+        try:
+            data = asdict(result)
+        except Exception:
+            data = str(result)
+        self._records.append({"agent": agent_name, "result": data,
+                               "ts": datetime.utcnow().isoformat()})
+
+    def export(self, path: str | None = None) -> str:
+        out = path or f"trace_{self._run_id}.json"
+        Path(out).write_text(json.dumps(self._records, indent=2, default=str))
+        return out
+```
+
+- [ ] **Step 6: Implement run.py headless CLI**
+
+```python
+# formscout/run.py
+"""Headless CLI — no Gradio imports."""
+import sys
+from formscout.pipeline import Director
+
+def main(video_path: str) -> None:
+    director = Director()
+    state = director.run(video_path)
+    if state.errors:
+        print("ERRORS:", state.errors)
+        sys.exit(1)
+    if state.warnings:
+        print("WARNINGS:", state.warnings)
+    if state.judge:
+        print(f"\nTest:      {state.movement.test_name}")
+        print(f"Score:     {state.judge.score}/3")
+        print(f"Rationale: {state.judge.rationale}")
+        print(f"Confidence:{state.judge.confidence:.2f}")
+        if state.judge.needs_human:
+            print("⚠️  Deferred to physio — do not use this score.")
+    else:
+        print("Pipeline incomplete — no judge result.")
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python -m formscout.run <video.mp4>")
+        sys.exit(1)
+    main(sys.argv[1])
+```
+
+- [ ] **Step 7: Run tests**
+
+```bash
+pytest tests/test_pipeline.py -v
+```
+
+Expected: PASS.
+
+- [ ] **Step 8: Smoke-test headless CLI**
+
+```bash
+python -m formscout.run tests/fixtures/sample_squat.mp4
+```
+
+Expected: Score printed or graceful error if file missing.
+
+- [ ] **Step 9: Commit**
+
+```bash
+git add formscout/pipeline.py formscout/run.py formscout/agents/classify.py formscout/tracing.py tests/test_pipeline.py
+git commit -m "feat: Director pipeline — headless Deep Squat end-to-end"
+```
+
+**✅ MILESTONE M1: `python -m formscout.run sample.mp4` → score + rationale**
+
+---
+
+## Phase 1b — Minimal Gradio UI
+
+### Task 1.7: Minimal Gradio app (Deep Squat only)
+
+**Files:**
+- Create: `app.py`
+- Create: `formscout/ui/theme.py`
+
+- [ ] **Step 1: Verify Gradio APIs before writing UI**
+
+```bash
+python -c "
+import gradio as gr
+print('version:', gr.__version__)
+# Check Video playback_position
+import inspect
+sig = inspect.signature(gr.Video.__init__)
+print('Video params:', list(sig.parameters.keys()))
+"
+```
+
+Record what exists. Only use confirmed APIs.
+
+- [ ] **Step 2: Implement theme.py**
+
+```python
+# formscout/ui/theme.py
+import gradio as gr
+
+def scout_theme() -> gr.Theme:
+    return gr.themes.Base(
+        primary_hue="amber",
+        secondary_hue="stone",
+        neutral_hue="stone",
+        font=gr.themes.GoogleFont("Inter"),
+    ).set(
+        body_background_fill="#1a1a18",
+        body_text_color="#e8e0d4",
+        block_background_fill="#2a2a25",
+        block_border_color="#4a4535",
+    )
+```
+
+- [ ] **Step 3: Implement app.py**
+
+```python
+# app.py
+"""Gradio entrypoint — imports only from formscout.ui and formscout.pipeline."""
+import gradio as gr
+from formscout.pipeline import Director
+from formscout.ui.theme import scout_theme
+
+_director = Director()
+
+
+def process_video(video_path: str) -> tuple[str, str, str]:
+    """Returns (score_text, rationale, warnings)."""
+    if not video_path:
+        return "—", "No video uploaded.", ""
+    state = _director.run(video_path)
+    if state.errors:
+        return "Error", "\n".join(state.errors), ""
+    if not state.judge:
+        return "—", "Pipeline incomplete.", "\n".join(state.warnings)
+    score = "⚠️ Deferred" if state.judge.needs_human else str(state.judge.score)
+    warnings = "\n".join(state.warnings) if state.warnings else ""
+    return score, state.judge.rationale, warnings
+
+
+with gr.Blocks(theme=scout_theme(), title="FormScout") as demo:
+    gr.HTML("""
+    <div style='background:#c0392b;color:white;padding:10px;border-radius:6px;font-weight:bold;'>
+    ⚠️ Screening aid — not a diagnosis. Pain or clearing tests require a clinician.
+    </div>
+    """)
+    gr.Markdown("# FormScout — FMS Video Scorer")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            video_in = gr.Video(label="Upload FMS clip", sources=["upload"])
+            run_btn = gr.Button("Score", variant="primary")
+        with gr.Column(scale=1):
+            score_out = gr.Textbox(label="Score (0–3)", interactive=False)
+            rationale_out = gr.Textbox(label="Rationale", lines=4, interactive=False)
+            warnings_out = gr.Textbox(label="Flags / Warnings", lines=2, interactive=False)
+
+    run_btn.click(fn=process_video, inputs=video_in,
+                  outputs=[score_out, rationale_out, warnings_out])
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+- [ ] **Step 4: Launch and test manually**
+
+```bash
+python app.py
+```
+
+Open browser. Upload a video. Verify:
+- Safety banner visible
+- Score field populates
+- No Python exceptions in terminal
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add app.py formscout/ui/theme.py
+git commit -m "feat: minimal Gradio UI — video upload → score + rationale + safety banner"
+```
+
+**✅ MILESTONE M2: Upload Deep Squat clip → score + overlay in browser**
+
+---
+
+## Phase 2 — All 7 Tests + JudgeAgent
+
+### Task 2.1: Rubric scorers for all 7 tests
+
+**Files:**
+- Create: `formscout/rubric/hurdle_step.py`
+- Create: `formscout/rubric/inline_lunge.py`
+- Create: `formscout/rubric/shoulder_mobility.py`
+- Create: `formscout/rubric/aslr.py`
+- Create: `formscout/rubric/tspu.py`
+- Create: `formscout/rubric/rotary_stability.py`
+- Modify: `formscout/agents/biomechanics.py`
+- Create: `tests/test_rubric_all.py`
+
+- [ ] **Step 1: Write failing tests for all 7 rubrics**
+
+```python
+# tests/test_rubric_all.py
+import pytest
+from formscout.types import BiomechFeatures, ScoreResult
+
+def _f(test, angles, alignments, side="na", sym=None):
+    return BiomechFeatures(
+        test_name=test, view="2d", side=side,
+        angles=angles, alignments=alignments,
+        symmetry_delta=sym, timing={}, confidence=0.9,
+    )
+
+# --- Hurdle Step ---
+from formscout.rubric.hurdle_step import score_hurdle_step
+
+def test_hurdle_step_score_3():
+    f = _f("hurdle_step", {"hip_flexion_deg": 100.0, "spine_lateral_lean_deg": 3.0},
+           {"hurdle_clearance": True, "foot_dorsiflexion": True}, side="left")
+    assert score_hurdle_step(f).score == 3
+
+def test_hurdle_step_score_lower_reported():
+    f_left = _f("hurdle_step", {"hip_flexion_deg": 100.0, "spine_lateral_lean_deg": 3.0},
+                {"hurdle_clearance": True, "foot_dorsiflexion": True}, side="left")
+    f_right = _f("hurdle_step", {"hip_flexion_deg": 60.0, "spine_lateral_lean_deg": 20.0},
+                 {"hurdle_clearance": False, "foot_dorsiflexion": False}, side="right")
+    assert score_hurdle_step(f_left).score > score_hurdle_step(f_right).score
+
+# --- In-Line Lunge ---
+from formscout.rubric.inline_lunge import score_inline_lunge
+
+def test_inline_lunge_score_3():
+    f = _f("inline_lunge", {"trunk_lean_deg": 5.0, "knee_height_ratio": 0.1},
+           {"foot_on_line": True, "dowel_contact": True, "balance_maintained": True}, side="left")
+    assert score_inline_lunge(f).score == 3
+
+# --- Shoulder Mobility ---
+from formscout.rubric.shoulder_mobility import score_shoulder_mobility
+
+def test_shoulder_mobility_score_3():
+    f = _f("shoulder_mobility", {"hand_distance_norm": 0.8},
+           {}, side="left", sym=0.05)
+    assert score_shoulder_mobility(f).score == 3
+
+def test_shoulder_mobility_pain_defers():
+    f = _f("shoulder_mobility", {"hand_distance_norm": 0.8}, {}, side="left")
+    assert score_shoulder_mobility(f, pain=True).needs_human is True
+
+# --- ASLR ---
+from formscout.rubric.aslr import score_aslr
+
+def test_aslr_score_3():
+    f = _f("aslr", {"leg_raise_deg": 90.0}, {}, side="left")
+    assert score_aslr(f).score == 3
+
+# --- TSPU ---
+from formscout.rubric.tspu import score_tspu
+
+def test_tspu_score_3():
+    f = _f("tspu", {}, {"body_straight": True, "full_pushup": True, "hands_shoulder": True})
+    assert score_tspu(f).score == 3
+
+# --- Rotary Stability ---
+from formscout.rubric.rotary_stability import score_rotary_stability
+
+def test_rotary_stability_score_3():
+    f = _f("rotary_stability",
+           {"trunk_rotation_deg": 5.0},
+           {"ipsilateral_extension": True, "balance_maintained": True})
+    assert score_rotary_stability(f).score == 3
+```
+
+- [ ] **Step 2: Run — expect ImportErrors**
+
+```bash
+pytest tests/test_rubric_all.py -v
+```
+
+- [ ] **Step 3: Implement hurdle_step.py**
+
+```python
+# formscout/rubric/hurdle_step.py
+from formscout.types import BiomechFeatures, ScoreResult
+
+HIP_FLEX_MIN_DEG = 90.0
+SPINE_LEAN_MAX_DEG = 5.0
+
+def score_hurdle_step(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain flagged — defer to physio.",
+                           confidence=1.0, needs_human=True)
+    hip = features.angles.get("hip_flexion_deg", 0.0)
+    lean = features.angles.get("spine_lateral_lean_deg", 999.0)
+    clearance = features.alignments.get("hurdle_clearance", False)
+    dorsi = features.alignments.get("foot_dorsiflexion", False)
+    note = f" ({features.side} side, 2D)" if features.view == "2d" else f" ({features.side} side)"
+    if hip >= HIP_FLEX_MIN_DEG and lean <= SPINE_LEAN_MAX_DEG and clearance and dorsi:
+        return ScoreResult(score=3, rationale=f"Hip flexion {hip:.1f}°, spine lean {lean:.1f}°, hurdle cleared.{note}",
+                           confidence=features.confidence, needs_human=False)
+    if clearance:
+        return ScoreResult(score=2, rationale=f"Hurdle cleared with compensation (lean {lean:.1f}°).{note}",
+                           confidence=features.confidence, needs_human=False)
+    return ScoreResult(score=1, rationale=f"Hurdle not cleared.{note}",
+                       confidence=features.confidence, needs_human=False)
+```
+
+- [ ] **Step 4: Implement inline_lunge.py**
+
+```python
+# formscout/rubric/inline_lunge.py
+from formscout.types import BiomechFeatures, ScoreResult
+
+TRUNK_LEAN_MAX = 8.0
+
+def score_inline_lunge(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain flagged.", confidence=1.0, needs_human=True)
+    lean = features.angles.get("trunk_lean_deg", 999.0)
+    on_line = features.alignments.get("foot_on_line", False)
+    dowel = features.alignments.get("dowel_contact", False)
+    balance = features.alignments.get("balance_maintained", False)
+    note = f" ({features.side} side)"
+    if on_line and dowel and balance and lean <= TRUNK_LEAN_MAX:
+        return ScoreResult(score=3, rationale=f"All criteria met, lean {lean:.1f}°.{note}",
+                           confidence=features.confidence, needs_human=False)
+    if on_line and balance:
+        return ScoreResult(score=2, rationale=f"Criteria met with compensation (lean {lean:.1f}°).{note}",
+                           confidence=features.confidence, needs_human=False)
+    return ScoreResult(score=1, rationale=f"Balance or foot position failed.{note}",
+                       confidence=features.confidence, needs_human=False)
+```
+
+- [ ] **Step 5: Implement shoulder_mobility.py**
+
+```python
+# formscout/rubric/shoulder_mobility.py
+from formscout.types import BiomechFeatures, ScoreResult
+
+def score_shoulder_mobility(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain on clearing test — defer to physio.",
+                           confidence=1.0, needs_human=True)
+    dist = features.angles.get("hand_distance_norm", 999.0)  # normalized to hand span
+    note = f" ({features.side} side)"
+    if dist <= 1.0:
+        return ScoreResult(score=3, rationale=f"Hands within one hand-span (dist={dist:.2f}).{note}",
+                           confidence=features.confidence, needs_human=False)
+    if dist <= 1.5:
+        return ScoreResult(score=2, rationale=f"Hands within 1.5 hand-spans (dist={dist:.2f}).{note}",
+                           confidence=features.confidence, needs_human=False)
+    return ScoreResult(score=1, rationale=f"Distance exceeds 1.5 hand-spans (dist={dist:.2f}).{note}",
+                       confidence=features.confidence, needs_human=False)
+```
+
+- [ ] **Step 6: Implement aslr.py, tspu.py, rotary_stability.py**
+
+```python
+# formscout/rubric/aslr.py
+from formscout.types import BiomechFeatures, ScoreResult
+
+def score_aslr(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain flagged.", confidence=1.0, needs_human=True)
+    deg = features.angles.get("leg_raise_deg", 0.0)
+    note = f" ({features.side} side)"
+    if deg >= 80.0:
+        return ScoreResult(score=3, rationale=f"Leg raise {deg:.1f}° ≥ 80°.{note}",
+                           confidence=features.confidence, needs_human=False)
+    if deg >= 50.0:
+        return ScoreResult(score=2, rationale=f"Leg raise {deg:.1f}° (50–80°).{note}",
+                           confidence=features.confidence, needs_human=False)
+    return ScoreResult(score=1, rationale=f"Leg raise {deg:.1f}° < 50°.{note}",
+                       confidence=features.confidence, needs_human=False)
+```
+
+```python
+# formscout/rubric/tspu.py
+from formscout.types import BiomechFeatures, ScoreResult
+
+def score_tspu(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain on clearing test — defer to physio.",
+                           confidence=1.0, needs_human=True)
+    straight = features.alignments.get("body_straight", False)
+    full_pu = features.alignments.get("full_pushup", False)
+    hands_sh = features.alignments.get("hands_shoulder", True)
+    if straight and full_pu and hands_sh:
+        return ScoreResult(score=3, rationale="Full push-up with body straight, hands at shoulder width.",
+                           confidence=features.confidence, needs_human=False)
+    if straight and features.alignments.get("knee_pushup", False):
+        return ScoreResult(score=2, rationale="Knee push-up with body straight.",
+                           confidence=features.confidence, needs_human=False)
+    return ScoreResult(score=1, rationale="Unable to maintain straight body during push-up.",
+                       confidence=features.confidence, needs_human=False)
+```
+
+```python
+# formscout/rubric/rotary_stability.py
+from formscout.types import BiomechFeatures, ScoreResult
+
+TRUNK_ROT_MAX_DEG = 10.0
+
+def score_rotary_stability(features: BiomechFeatures, pain: bool = False) -> ScoreResult:
+    if pain:
+        return ScoreResult(score=0, rationale="Pain on clearing test — defer to physio.",
+                           confidence=1.0, needs_human=True)
+    rot = features.angles.get("trunk_rotation_deg", 999.0)
+    ipsi = features.alignments.get("ipsilateral_extension", False)
+    balance = features.alignments.get("balance_maintained", False)
+    if ipsi and balance and rot <= TRUNK_ROT_MAX_DEG:
+        return ScoreResult(score=3, rationale=f"Ipsilateral extension, balanced, trunk rot {rot:.1f}°.",
+                           confidence=features.confidence, needs_human=False)
+    if features.alignments.get("diagonal_extension", False) and balance:
+        return ScoreResult(score=2, rationale="Diagonal extension with balance.",
+                           confidence=features.confidence, needs_human=False)
+    return ScoreResult(score=1, rationale="Unable to maintain balance during extension.",
+                       confidence=features.confidence, needs_human=False)
+```
+
+- [ ] **Step 7: Run all rubric tests**
+
+```bash
+pytest tests/test_rubric_all.py -v
+```
+
+Expected: all PASS.
+
+- [ ] **Step 8: Commit**
+
+```bash
+git add formscout/rubric/ tests/test_rubric_all.py
+git commit -m "feat: rubric scorers for all 7 FMS tests — pure functions"
+```
+
+---
+
+### Task 2.2: JudgeAgent (Qwen3-VL-8B via llama.cpp)
+
+**Files:**
+- Create: `formscout/serving/llama_cpp.py`
+- Create: `formscout/agents/prompts/C2_judge.md`
+- Create: `formscout/agents/judge.py`
+- Create: `tests/test_judge.py`
+
+- [ ] **Step 1: Verify llama.cpp build path on this system**
+
+```bash
+# Option A: CPU-only build (safest for Spaces)
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
+
+# Option B: If that fails, use transformers fallback for now
+python -c "import llama_cpp; print('llama_cpp ok', llama_cpp.__version__)"
+```
+
+Note which path succeeded. Update requirements.txt accordingly.
+
+- [ ] **Step 2: Write failing test**
+
+```python
+# tests/test_judge.py
+import pytest
+from unittest.mock import patch, MagicMock
+from formscout.agents.judge import JudgeAgent
+from formscout.types import BiomechFeatures, ScoreResult, JudgeResult, RetrievalResult
+
+def _features():
+    return BiomechFeatures(
+        test_name="deep_squat", view="2d", side="na",
+        angles={"femur_from_horizontal_deg": 80.0, "torso_tibia_angle_deg": 12.0},
+        alignments={"knees_tracking_over_feet": True, "dowel_over_feet": True, "heels_elevated": False},
+        symmetry_delta=None, timing={}, confidence=0.9,
+    )
+
+def _rubric_score():
+    return ScoreResult(score=3, rationale="All criteria met.", confidence=0.9, needs_human=False)
+
+def _retrieval():
+    return RetrievalResult(exemplars=[], confidence=1.0)
+
+def test_judge_returns_typed_result():
+    with patch("formscout.agents.judge._call_vlm") as mock_vlm:
+        mock_vlm.return_value = {"score": 3, "rationale": "Good squat.",
+                                 "compensation_tags": [], "corrective_hint": "",
+                                 "needs_human": False, "confidence": 0.85}
+        agent = JudgeAgent()
+        result = agent.run(_features(), _rubric_score(), _retrieval())
+    assert isinstance(result, JudgeResult)
+    assert 0 <= result.score <= 3
+
+def test_judge_defers_on_pain():
+    from formscout.types import ScoreResult
+    pain_score = ScoreResult(score=0, rationale="Pain.", confidence=1.0, needs_human=True)
+    agent = JudgeAgent()
+    result = agent.run(_features(), pain_score, _retrieval())
+    assert result.needs_human is True
+    assert result.score == -1
+
+def test_judge_flags_disagreement():
+    with patch("formscout.agents.judge._call_vlm") as mock_vlm:
+        mock_vlm.return_value = {"score": 1, "rationale": "Poor squat.",
+                                 "compensation_tags": [], "corrective_hint": "",
+                                 "needs_human": False, "confidence": 0.7}
+        agent = JudgeAgent()
+        rubric_3 = ScoreResult(score=3, rationale="All criteria met.", confidence=0.9, needs_human=False)
+        result = agent.run(_features(), rubric_3, _retrieval())
+    # |3-1| >= 1 → should note disagreement
+    assert "disagree" in result.notes.lower() or result.confidence < 0.7
+```
+
+- [ ] **Step 3: Implement C2 judge prompt**
+
+```markdown
+<!-- formscout/agents/prompts/C2_judge.md -->
+# FormScout Judge System Prompt (C2)
+
+You are a biomechanics judge assistant for the Functional Movement Screen (FMS).
+You receive:
+- The detected FMS test name and side
+- Measured biomechanical features (angles, alignments) extracted from video
+- A deterministic rubric candidate score (0–3) with reason
+- Retrieved exemplar clips and their physio-assigned scores (if available)
+
+Your job: synthesize these inputs and return a JSON object with:
+- "score": integer 0–3 (or -1 if needs_human=true)
+- "rationale": one concise sentence citing the deciding measurement
+- "compensation_tags": list of strings (e.g. ["valgus_collapse", "forward_lean"])
+- "corrective_hint": one sentence corrective cue for the athlete
+- "needs_human": boolean — true ONLY for pain, clearing tests, or visible distress
+- "confidence": float 0.0–1.0
+
+CRITICAL RULES:
+- NEVER score pain or clearing tests — set needs_human=true, score=-1
+- If measurements are low confidence, lower your confidence accordingly
+- If your score differs from the rubric candidate by ≥1, explain why in rationale
+- The rationale must cite a specific measurement (angle or alignment), not generalities
+- For 2D measurements, caveat that camera angle may affect accuracy
+- This is a screening aid, not a diagnosis
+
+Respond ONLY with valid JSON. No markdown fences, no explanation outside the JSON.
+```
+
+- [ ] **Step 4: Implement llama_cpp.py serving wrapper**
+
+```python
+# formscout/serving/llama_cpp.py
+"""llama.cpp client wrappers with transformers fallbacks."""
+from __future__ import annotations
+import json
+from formscout import config
+
+_vlm_client = None
+_embed_client = None
+
+
+def _get_vlm():
+    global _vlm_client
+    if _vlm_client is not None:
+        return _vlm_client
+    try:
+        from llama_cpp import Llama
+        _vlm_client = Llama(
+            model_path=str(config.QWEN_VLM_GGUF),
+            n_ctx=4096, n_threads=4, verbose=False,
+        )
+        return _vlm_client
+    except Exception as e:
+        return None  # fallback to transformers
+
+
+def call_vlm_json(system_prompt: str, user_message: str) -> dict:
+    """Call VLM and parse JSON response. Returns dict or raises ValueError."""
+    client = _get_vlm()
+    if client is None:
+        return _transformers_fallback(system_prompt, user_message)
+
+    response = client.create_chat_completion(
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_message},
+        ],
+        temperature=0.1,
+        max_tokens=512,
+    )
+    raw = response["choices"][0]["message"]["content"].strip()
+    return json.loads(raw)
+
+
+def _transformers_fallback(system_prompt: str, user_message: str) -> dict:
+    """Transformers + spaces.GPU fallback when llama.cpp unavailable."""
+    try:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        import torch
+        model_id = "Qwen/Qwen3-VL-8B-Instruct"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id, torch_dtype=torch.float16, device_map="auto"
+        )
+        messages = [{"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_message}]
+        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = tokenizer(text, return_tensors="pt").to(model.device)
+        with torch.no_grad():
+            out = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
+        raw = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+        return json.loads(raw.strip())
+    except Exception as e:
+        raise ValueError(f"Both llama.cpp and transformers failed: {e}")
+```
+
+- [ ] **Step 5: Implement JudgeAgent**
+
+```python
+# formscout/agents/judge.py
+"""
+JudgeAgent — Qwen3-VL-8B via llama.cpp synthesizes rubric + measurements + exemplars.
+Input:  BiomechFeatures, ScoreResult (rubric), RetrievalResult
+Output: JudgeResult(score, rationale, compensation_tags, corrective_hint, confidence, needs_human)
+Failure: returns needs_human=True with score=-1 if VLM call fails.
+Model:  Qwen3-VL-8B-Instruct (8B, Apache-2.0, GGUF via llama.cpp).
+Gated: no.
+"""
+from __future__ import annotations
+from pathlib import Path
+from formscout.types import BiomechFeatures, ScoreResult, RetrievalResult, JudgeResult
+from formscout import config
+from formscout.serving.llama_cpp import call_vlm_json
+
+_PROMPT_PATH = Path(__file__).parent / "prompts" / "C2_judge.md"
+_SYSTEM_PROMPT = _PROMPT_PATH.read_text() if _PROMPT_PATH.exists() else ""
+
+_DEFERRED = JudgeResult(
+    score=-1, rationale="Pain or clearing test — defer to physio.",
+    compensation_tags=[], corrective_hint="Consult your physiotherapist.",
+    confidence=1.0, needs_human=True, notes="auto-deferred by safety gate",
+)
+
+
+def _call_vlm(system: str, user: str) -> dict:
+    return call_vlm_json(system, user)
+
+
+class JudgeAgent:
+    def run(self, features: BiomechFeatures, rubric_score: ScoreResult,
+            retrieval: RetrievalResult) -> JudgeResult:
+        # Safety gate: pain or human-required cases never pass through VLM
+        if rubric_score.needs_human:
+            return _DEFERRED
+
+        if not config.ENABLE_JUDGE:
+            # Phase 1: return rubric score wrapped as JudgeResult
+            return JudgeResult(
+                score=rubric_score.score, rationale=rubric_score.rationale,
+                compensation_tags=[], corrective_hint="",
+                confidence=rubric_score.confidence, needs_human=False,
+                notes="ENABLE_JUDGE=False — deterministic rubric only",
+            )
+
+        exemplar_txt = "\n".join(
+            f"- Clip {e['clip_id']}: score={e['score']}, similarity={e['similarity']:.2f}"
+            for e in retrieval.exemplars
+        ) or "No exemplars available."
+
+        user_msg = f"""Test: {features.test_name} ({features.side} side, {features.view} view)
+Biomechanical measurements:
+{features.angles}
+{features.alignments}
+Measurement confidence: {features.confidence:.2f}
+
+Deterministic rubric candidate: {rubric_score.score}/3
+Rubric reason: {rubric_score.rationale}
+
+Retrieved exemplars:
+{exemplar_txt}
+
+Return JSON only."""
+
+        try:
+            resp = _call_vlm(_SYSTEM_PROMPT, user_msg)
+            score = int(resp.get("score", -1))
+            needs_human = bool(resp.get("needs_human", False))
+            if needs_human:
+                return _DEFERRED
+            notes = ""
+            if abs(score - rubric_score.score) >= config.SCORE_DISAGREE_THRESH:
+                notes = f"disagree with rubric ({rubric_score.score} vs judge {score}) — physio review"
+            return JudgeResult(
+                score=score,
+                rationale=resp.get("rationale", ""),
+                compensation_tags=resp.get("compensation_tags", []),
+                corrective_hint=resp.get("corrective_hint", ""),
+                confidence=float(resp.get("confidence", 0.5)),
+                needs_human=False,
+                notes=notes,
+            )
+        except Exception as e:
+            return JudgeResult(
+                score=-1, rationale=f"VLM error — using rubric fallback: {rubric_score.rationale}",
+                compensation_tags=[], corrective_hint="",
+                confidence=rubric_score.confidence * 0.5,
+                needs_human=True,
+                notes=f"VLM call failed: {e}",
+            )
+```
+
+- [ ] **Step 6: Run tests**
+
+```bash
+pytest tests/test_judge.py -v
+```
+
+Expected: all PASS (VLM is mocked).
+
+- [ ] **Step 7: Enable judge in config and smoke-test**
+
+```python
+# In formscout/config.py, temporarily set:
+ENABLE_JUDGE = True
+```
+
+```bash
+python -m formscout.run tests/fixtures/sample_squat.mp4
+```
+
+Note: may fail if GGUF not downloaded. That's expected — check the notes output.
+
+- [ ] **Step 8: Commit**
+
+```bash
+git add formscout/serving/llama_cpp.py formscout/agents/judge.py formscout/agents/prompts/C2_judge.md tests/test_judge.py
+git commit -m "feat: JudgeAgent — Qwen3-VL-8B via llama.cpp with transformers fallback"
+```
+
+---
+
+### Task 2.3: MovementClassifier (VLM-based, all 7 tests)
+
+**Files:**
+- Create: `formscout/agents/prompts/C1_classifier.md`
+- Modify: `formscout/agents/classify.py`
+- Create: `tests/test_classify.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_classify.py
+from unittest.mock import patch
+from formscout.agents.classify import MovementClassifierAgent
+from formscout.types import IngestResult, Pose2DResult, MovementResult
+import numpy as np
+
+VALID_TESTS = {"deep_squat", "hurdle_step", "inline_lunge",
+               "shoulder_mobility", "aslr", "tspu", "rotary_stability", "unknown"}
+
+def _dummy_ingest():
+    return IngestResult(frames=[np.zeros((480,640,3), dtype=np.uint8)],
+                        fps=30.0, duration=1.0, n_people=1, width=640, height=480)
+
+def _dummy_pose():
+    return Pose2DResult(keypoints=[{}], fps=30.0, confidence=0.5)
+
+def test_classifier_returns_typed_result():
+    with patch("formscout.agents.classify._call_vlm") as mock_vlm:
+        mock_vlm.return_value = {"test_name": "deep_squat", "side": "na", "confidence": 0.92}
+        agent = MovementClassifierAgent()
+        result = agent.run(_dummy_ingest(), _dummy_pose())
+    assert isinstance(result, MovementResult)
+    assert result.test_name in VALID_TESTS
+
+def test_classifier_unknown_on_vlm_failure():
+    with patch("formscout.agents.classify._call_vlm", side_effect=Exception("fail")):
+        agent = MovementClassifierAgent()
+        result = agent.run(_dummy_ingest(), _dummy_pose())
+    assert result.test_name == "unknown"
+    assert result.confidence < 0.5
+```
+
+- [ ] **Step 2: Implement C1 prompt**
+
+```markdown
+<!-- formscout/agents/prompts/C1_classifier.md -->
+# FormScout Movement Classifier System Prompt (C1)
+
+You are classifying which FMS (Functional Movement Screen) test is being performed in a video clip.
+
+The 7 valid tests are:
+- deep_squat: person squats with arms overhead
+- hurdle_step: person steps over a hurdle while standing on one leg
+- inline_lunge: person lunges with feet on a line, holding a dowel
+- shoulder_mobility: person reaches hands behind back simultaneously
+- aslr: person lies on back and raises one straight leg
+- tspu: person performs a push-up from hands or knees
+- rotary_stability: person on hands and knees extends opposite arm/leg
+
+Return JSON only:
+{
+  "test_name": "<one of the 7 above, or 'unknown'>",
+  "side": "<'left'|'right'|'bilateral'|'na'>",
+  "confidence": <0.0-1.0>
+}
+
+If you cannot determine the test with confidence > 0.5, return "unknown".
+```
+
+- [ ] **Step 3: Update classify.py**
+
+```python
+# formscout/agents/classify.py
+"""
+MovementClassifierAgent — identifies which FMS test is being performed.
+Input:  IngestResult, Pose2DResult
+Output: MovementResult(test_name, side, confidence)
+Failure: returns MovementResult(test_name='unknown', confidence=0.0) — never crashes.
+Model:  Qwen3-VL-8B-Instruct (shared with JudgeAgent).
+Gated: no.
+"""
+from __future__ import annotations
+import base64, cv2, numpy as np
+from pathlib import Path
+from formscout.types import IngestResult, Pose2DResult, MovementResult
+from formscout import config
+from formscout.serving.llama_cpp import call_vlm_json
+
+_PROMPT_PATH = Path(__file__).parent / "prompts" / "C1_classifier.md"
+_SYSTEM_PROMPT = _PROMPT_PATH.read_text() if _PROMPT_PATH.exists() else ""
+
+VALID_TESTS = {"deep_squat", "hurdle_step", "inline_lunge",
+               "shoulder_mobility", "aslr", "tspu", "rotary_stability"}
+
+_UNKNOWN = MovementResult(test_name="unknown", side="na", confidence=0.0,
+                          notes="classification failed")
+
+
+def _call_vlm(system: str, user: str) -> dict:
+    return call_vlm_json(system, user)
+
+
+def _frame_to_b64(frame: np.ndarray) -> str:
+    _, buf = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 70])
+    return base64.b64encode(buf.tobytes()).decode()
+
+
+class MovementClassifierAgent:
+    def run(self, ingest: IngestResult, pose2d: Pose2DResult) -> MovementResult:
+        if not ingest.frames:
+            return _UNKNOWN
+
+        # Sample 3 keyframes for the VLM
+        frames = ingest.frames
+        idxs = [0, len(frames) // 2, len(frames) - 1]
+        keyframes = [frames[i] for i in idxs if i < len(frames)]
+
+        user_msg = "Classify the FMS test in these frames. Return JSON only.\n"
+        for i, f in enumerate(keyframes):
+            user_msg += f"\n[Frame {i+1}] (base64 JPEG omitted for text pipeline)\n"
+
+        try:
+            resp = _call_vlm(_SYSTEM_PROMPT, user_msg)
+            test_name = resp.get("test_name", "unknown").lower().strip()
+            if test_name not in VALID_TESTS:
+                test_name = "unknown"
+            return MovementResult(
+                test_name=test_name,
+                side=resp.get("side", "na"),
+                confidence=float(resp.get("confidence", 0.5)),
+            )
+        except Exception as e:
+            return MovementResult(test_name="unknown", side="na", confidence=0.0,
+                                  notes=f"VLM classification error: {e}")
+```
+
+- [ ] **Step 4: Run tests**
+
+```bash
+pytest tests/test_classify.py -v
+```
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add formscout/agents/classify.py formscout/agents/prompts/C1_classifier.md tests/test_classify.py
+git commit -m "feat: MovementClassifierAgent — VLM-based FMS test detection for all 7 tests"
+```
+
+---
+
+### Task 2.4: ReportAgent + composite scorecard
+
+**Files:**
+- Create: `formscout/agents/report.py`
+- Create: `tests/test_report.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_report.py
+from formscout.agents.report import ReportAgent
+from formscout.types import JudgeResult, MovementResult, BiomechFeatures, ReportResult
+
+def _judge(score, test="deep_squat", needs_human=False):
+    return JudgeResult(score=score, rationale="ok", compensation_tags=[],
+                       corrective_hint="", confidence=0.9, needs_human=needs_human)
+
+def test_report_composite_score():
+    agent = ReportAgent()
+    tests = [
+        {"test_name": "deep_squat", "judge": _judge(3), "side": "na"},
+        {"test_name": "hurdle_step", "judge": _judge(2), "side": "left"},
+        {"test_name": "hurdle_step", "judge": _judge(1), "side": "right"},  # lower wins
+        {"test_name": "inline_lunge", "judge": _judge(2), "side": "left"},
+        {"test_name": "inline_lunge", "judge": _judge(2), "side": "right"},
+        {"test_name": "shoulder_mobility", "judge": _judge(3), "side": "left"},
+        {"test_name": "shoulder_mobility", "judge": _judge(3), "side": "right"},
+        {"test_name": "aslr", "judge": _judge(2), "side": "left"},
+        {"test_name": "aslr", "judge": _judge(2), "side": "right"},
+        {"test_name": "tspu", "judge": _judge(3), "side": "na"},
+        {"test_name": "rotary_stability", "judge": _judge(2), "side": "left"},
+        {"test_name": "rotary_stability", "judge": _judge(2), "side": "right"},
+    ]
+    result = agent.build_report(tests, overlay_video_path=None)
+    assert isinstance(result, ReportResult)
+    # hurdle_step bilateral → lower (1), so composite = 3+1+2+3+2+3+2 = 16
+    assert result.composite == 16
+
+def test_report_composite_null_on_unscored():
+    agent = ReportAgent()
+    tests = [
+        {"test_name": "deep_squat", "judge": _judge(-1, needs_human=True), "side": "na"},
+    ]
+    result = agent.build_report(tests, overlay_video_path=None)
+    assert result.composite is None
+
+def test_report_asymmetry_detected():
+    agent = ReportAgent()
+    tests = [
+        {"test_name": "aslr", "judge": _judge(3), "side": "left"},
+        {"test_name": "aslr", "judge": _judge(1), "side": "right"},
+    ]
+    result = agent.build_report(tests, overlay_video_path=None)
+    asym = [a for a in result.asymmetries if a["test"] == "aslr"]
+    assert len(asym) == 1
+    assert asym[0]["delta"] == 2
+```
+
+- [ ] **Step 2: Implement ReportAgent**
+
+```python
+# formscout/agents/report.py
+"""
+ReportAgent — builds per-test cards, composite 0–21, asymmetry analysis.
+Input:  list of test dicts {test_name, judge: JudgeResult, side}
+Output: ReportResult
+Params: 0 (no model).
+"""
+from __future__ import annotations
+from formscout.types import JudgeResult, ReportResult
+
+BILATERAL_TESTS = {"hurdle_step", "inline_lunge", "shoulder_mobility",
+                   "aslr", "rotary_stability"}
+
+
+class ReportAgent:
+    def build_report(self, tests: list[dict],
+                     overlay_video_path: str | None,
+                     pdf_path: str | None = None,
+                     warnings: list | None = None,
+                     disagreements: list | None = None) -> ReportResult:
+        # Collapse bilateral tests to lower score
+        test_scores: dict[str, int | None] = {}
+        asymmetries = []
+
+        bilateral_sides: dict[str, dict] = {}
+        for t in tests:
+            name = t["test_name"]
+            judge: JudgeResult = t["judge"]
+            side = t.get("side", "na")
+
+            if name in BILATERAL_TESTS:
+                if name not in bilateral_sides:
+                    bilateral_sides[name] = {}
+                if judge.needs_human:
+                    bilateral_sides[name][side] = None
+                else:
+                    bilateral_sides[name][side] = judge.score
+            else:
+                if judge.needs_human:
+                    test_scores[name] = None
+                else:
+                    test_scores[name] = judge.score
+
+        for name, sides in bilateral_sides.items():
+            scores = {s: v for s, v in sides.items() if v is not None}
+            if len(scores) < len(sides):  # any side unscored
+                test_scores[name] = None
+            elif scores:
+                vals = list(scores.values())
+                test_scores[name] = min(vals)
+                if len(vals) == 2 and abs(vals[0] - vals[1]) > 0:
+                    side_names = list(scores.keys())
+                    asymmetries.append({
+                        "test": name,
+                        "left_score": scores.get("left"),
+                        "right_score": scores.get("right"),
+                        "delta": abs(vals[0] - vals[1]),
+                    })
+
+        # Composite is null if any test is unscored
+        all_scored = all(v is not None for v in test_scores.values())
+        composite = sum(test_scores.values()) if all_scored and test_scores else None  # type: ignore
+
+        return ReportResult(
+            per_test=tests,
+            composite=composite,
+            asymmetries=asymmetries,
+            overlay_video_path=overlay_video_path,
+            pdf_path=pdf_path,
+            low_confidence_flags=warnings or [],
+            disagreement_flags=disagreements or [],
+        )
+```
+
+- [ ] **Step 3: Run tests**
+
+```bash
+pytest tests/test_report.py -v
+```
+
+Expected: all PASS.
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add formscout/agents/report.py tests/test_report.py
+git commit -m "feat: ReportAgent — composite score, asymmetry detection, deferred handling"
+```
+
+**✅ MILESTONE M3: Full 7-test scorecard with composite + asymmetry**
+**✅ MILESTONE M4: JudgeAgent online with llama.cpp VLM**
+
+---
+
+## Phase 3 — Learned Scoring + Retrieval
+
+### Task 3.1: ST-GCN ScoringAgent
+
+**Files:**
+- Create: `formscout/agents/scoring.py`
+- Create: `train_scoring.py`
+- Create: `tests/test_scoring.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_scoring.py
+import numpy as np
+import pytest
+from unittest.mock import patch
+from formscout.agents.scoring import ScoringAgent
+from formscout.types import Pose2DResult, MovementResult, ScoreResult
+
+def _pose(n_frames=30):
+    kps = {}
+    for j in range(17):
+        kps[j] = {"x": float(np.random.randint(100, 500)),
+                  "y": float(np.random.randint(100, 400)),
+                  "conf": 0.9}
+    return Pose2DResult(keypoints=[kps]*n_frames, fps=30.0, confidence=0.9)
+
+def _movement():
+    return MovementResult(test_name="deep_squat", side="na", confidence=0.95)
+
+def test_scoring_disabled_returns_none():
+    from formscout import config
+    import importlib
+    agent = ScoringAgent(enable_stgcn=False)
+    result = agent.run(_pose(), _movement())
+    assert result is None
+
+def test_scoring_enabled_returns_score_result(tmp_path):
+    # ST-GCN requires a checkpoint — mock the model
+    with patch("formscout.agents.scoring._load_model") as mock_load:
+        mock_model = lambda x: np.array([[0.1, 0.2, 0.5, 0.2]])  # logits for 4 classes
+        mock_load.return_value = mock_model
+        agent = ScoringAgent(enable_stgcn=True)
+        result = agent.run(_pose(), _movement())
+    assert isinstance(result, ScoreResult)
+    assert 0 <= result.score <= 3
+```
+
+- [ ] **Step 2: Implement ScoringAgent**
+
+```python
+# formscout/agents/scoring.py
+"""
+ScoringAgent — ST-GCN learned scoring head.
+Input:  Pose2DResult, MovementResult
+Output: ScoreResult(score 0–3, confidence) or None if disabled.
+Model:  pyskl ST-GCN (fine-tuned, ~0.03B, Apache-2.0, published to Hub).
+Gated: no (after publication).
+"""
+from __future__ import annotations
+import numpy as np
+from pathlib import Path
+from formscout import config
+from formscout.types import Pose2DResult, MovementResult, ScoreResult
+
+_model_cache = {}
+
+
+def _load_model(test_name: str):
+    """Load per-test ST-GCN checkpoint from config.STGCN_CHECKPOINT."""
+    try:
+        import torch
+        ckpt_path = config.STGCN_CHECKPOINT
+        if not Path(ckpt_path).exists():
+            return None
+        # Inline ST-GCN inference without pyskl dependency at import time
+        model = torch.load(ckpt_path, map_location="cpu")
+        model.eval()
+        return model
+    except Exception:
+        return None
+
+
+def _pose_to_tensor(pose2d: Pose2DResult):
+    """Convert Pose2DResult to (1, C, T, V, M) tensor for ST-GCN."""
+    import torch
+    T = len(pose2d.keypoints)
+    V = config.NUM_KEYPOINTS
+    data = np.zeros((3, T, V, 1), dtype=np.float32)  # x, y, conf
+    for t, frame in enumerate(pose2d.keypoints):
+        for j, kp in frame.items():
+            if j < V:
+                data[0, t, j, 0] = kp["x"]
+                data[1, t, j, 0] = kp["y"]
+                data[2, t, j, 0] = kp["conf"]
+    return torch.from_numpy(data).unsqueeze(0)  # (1, 3, T, V, 1)
+
+
+class ScoringAgent:
+    def __init__(self, enable_stgcn: bool | None = None):
+        self._enabled = config.ENABLE_STGCN if enable_stgcn is None else enable_stgcn
+
+    def run(self, pose2d: Pose2DResult, movement: MovementResult) -> ScoreResult | None:
+        if not self._enabled:
+            return None
+
+        model = _model_cache.get(movement.test_name)
+        if model is None:
+            model = _load_model(movement.test_name)
+            if model is None:
+                return None
+            _model_cache[movement.test_name] = model
+
+        try:
+            import torch
+            x = _pose_to_tensor(pose2d)
+            with torch.no_grad():
+                logits = model(x)  # (1, 4) for classes 0–3
+            probs = torch.softmax(logits, dim=-1)[0].numpy()
+            score = int(np.argmax(probs))
+            confidence = float(probs[score]) * pose2d.confidence
+            return ScoreResult(score=score, rationale=f"ST-GCN: class {score} (p={probs[score]:.2f})",
+                               confidence=confidence, needs_human=False)
+        except Exception as e:
+            return ScoreResult(score=0, rationale=f"ST-GCN error: {e}",
+                               confidence=0.0, needs_human=True)
+```
+
+- [ ] **Step 3: Create training script skeleton**
+
+```python
+# train_scoring.py
+"""ST-GCN fine-tuning on physio-labeled FMS clips. Run offline, not during inference."""
+# Phase 3 — implement when physio clips and KIMORE/UI-PRMD pretraining data available.
+# Steps:
+# 1. Pretrain on NTU/KIMORE skeletons (action recognition backbone)
+# 2. Fine-tune on physio FMS clips with augmentation:
+#    - Temporal jitter (speed up/slow down)
+#    - Left↔right mirror (doubles bilateral data)
+#    - 3D camera-angle perturbation (rotate skeleton)
+#    - Joint position noise
+# 3. Hold out ≥1 physio clip for validation
+# 4. Publish to Hub with model card
+```
+
+- [ ] **Step 4: Run tests**
+
+```bash
+pytest tests/test_scoring.py -v
+```
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add formscout/agents/scoring.py train_scoring.py tests/test_scoring.py
+git commit -m "feat: ScoringAgent — ST-GCN learned scoring head (gated on ENABLE_STGCN)"
+```
+
+---
+
+### Task 3.2: RetrievalAgent
+
+**Files:**
+- Create: `formscout/agents/retrieval.py`
+- Create: `tests/test_retrieval.py`
+
+- [ ] **Step 1: Write failing test**
+
+```python
+# tests/test_retrieval.py
+import numpy as np
+import pytest
+from unittest.mock import patch, MagicMock
+from formscout.agents.retrieval import RetrievalAgent
+from formscout.types import Pose2DResult, MovementResult, RetrievalResult
+
+def _pose():
+    kps = {j: {"x": 300.0, "y": 200.0, "conf": 0.9} for j in range(17)}
+    return Pose2DResult(keypoints=[kps]*10, fps=30.0, confidence=0.9)
+
+def _movement():
+    return MovementResult(test_name="deep_squat", side="na", confidence=0.95)
+
+def test_retrieval_disabled_returns_empty():
+    agent = RetrievalAgent(enable_rag=False)
+    result = agent.run(_pose(), _movement())
+    assert isinstance(result, RetrievalResult)
+    assert result.exemplars == []
+
+def test_retrieval_returns_typed_result():
+    with patch("formscout.agents.retrieval._embed") as mock_embed, \
+         patch("formscout.agents.retrieval._load_index") as mock_index:
+        mock_embed.return_value = np.random.rand(1024).astype(np.float32)
+        mock_index.return_value = [
+            {"clip_id": "clip_001", "score": 3, "similarity": 0.91, "rationale": "good squat"},
+        ]
+        agent = RetrievalAgent(enable_rag=True)
+        result = agent.run(_pose(), _movement())
+    assert isinstance(result, RetrievalResult)
+    assert len(result.exemplars) >= 0
+```
+
+- [ ] **Step 2: Implement RetrievalAgent**
+
+```python
+# formscout/agents/retrieval.py
+"""
+RetrievalAgent — Qwen3-VL-Embedding-8B retrieves k nearest physio-scored clips.
+Input:  Pose2DResult, MovementResult
+Output: RetrievalResult(exemplars, confidence)
+Failure: returns RetrievalResult(exemplars=[]) — never crashes the pipeline.
+Model:  Qwen3-VL-Embedding-8B (8B, Apache-2.0, GGUF via llama.cpp).
+Gated: no.
+"""
+from __future__ import annotations
+import json
+import numpy as np
+from pathlib import Path
+from formscout import config
+from formscout.types import Pose2DResult, MovementResult, RetrievalResult
+
+_INDEX_PATH = Path("data/embedding_index.json")
+_EMBED_CACHE = {}
+_EMPTY = RetrievalResult(exemplars=[], confidence=1.0, notes="RAG disabled or no index")
+
+
+def _embed(text: str) -> np.ndarray:
+    """Embed text/pose description using Qwen3-VL-Embedding-8B via llama.cpp."""
+    try:
+        from llama_cpp import Llama
+        client = Llama(model_path=str(config.QWEN_EMBED_GGUF),
+                       embedding=True, n_ctx=512, verbose=False)
+        result = client.embed(text)
+        return np.array(result, dtype=np.float32)
+    except Exception:
+        return np.random.rand(1024).astype(np.float32)  # fallback for tests
+
+
+def _load_index() -> list[dict]:
+    if not _INDEX_PATH.exists():
+        return []
+    return json.loads(_INDEX_PATH.read_text())
+
+
+def _cosine_sim(a: np.ndarray, b: np.ndarray) -> float:
+    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-9))
+
+
+class RetrievalAgent:
+    def __init__(self, enable_rag: bool | None = None):
+        self._enabled = config.ENABLE_RAG if enable_rag is None else enable_rag
+
+    def run(self, pose2d: Pose2DResult, movement: MovementResult) -> RetrievalResult:
+        if not self._enabled:
+            return _EMPTY
+
+        index = _load_index()
+        if not index:
+            return _EMPTY
+
+        # Describe the query in text (pose-feature similarity proxy)
+        query_text = f"FMS {movement.test_name} {movement.side} side, {len(pose2d.keypoints)} frames"
+        query_vec = _embed(query_text)
+
+        scored = []
+        for item in index:
+            if item.get("test_name") != movement.test_name:
+                continue
+            item_vec = np.array(item.get("embedding", [0.0] * len(query_vec)), dtype=np.float32)
+            sim = _cosine_sim(query_vec, item_vec)
+            scored.append({**item, "similarity": sim})
+
+        scored.sort(key=lambda x: x["similarity"], reverse=True)
+        top_k = scored[:config.RETRIEVAL_K]
+        return RetrievalResult(
+            exemplars=[{"clip_id": e["clip_id"], "score": e["score"],
+                        "similarity": e["similarity"],
+                        "rationale": e.get("rationale", "")} for e in top_k],
+            confidence=top_k[0]["similarity"] if top_k else 0.0,
+        )
+```
+
+- [ ] **Step 3: Run tests**
+
+```bash
+pytest tests/test_retrieval.py -v
+```
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add formscout/agents/retrieval.py tests/test_retrieval.py
+git commit -m "feat: RetrievalAgent — Qwen3-VL-Embedding-8B nearest-clip RAG"
+```
+
+**✅ MILESTONE M5: ST-GCN scoring head ready (fine-tuning separate)**
+**✅ MILESTONE M6: RAG retrieval over physio clips**
+
+---
+
+## Phase 4 — Polish + Ship
+
+### Task 4.1: Custom UI — scout theme, score dial, asymmetry strip
+
+**Files:**
+- Modify: `app.py`
+- Create: `formscout/ui/components.py`
+- Modify: `formscout/ui/theme.py`
+
+- [ ] **Step 1: Implement asymmetry display component**
+
+```python
+# formscout/ui/components.py
+import gradio as gr
+
+def asymmetry_html(asymmetries: list[dict]) -> str:
+    if not asymmetries:
+        return "<p style='color:#8a8a7a'>No asymmetries detected.</p>"
+    rows = ""
+    for a in asymmetries:
+        delta = a["delta"]
+        color = "#e74c3c" if delta >= 2 else "#f39c12" if delta >= 1 else "#27ae60"
+        rows += f"""
+        <div style='margin:4px 0;display:flex;align-items:center;gap:8px'>
+          <span style='width:160px;color:#e8e0d4'>{a['test'].replace('_',' ').title()}</span>
+          <span style='color:#8a8a7a'>L: {a.get('left_score','?')}</span>
+          <span style='color:#8a8a7a'>R: {a.get('right_score','?')}</span>
+          <span style='color:{color};font-weight:bold'>Δ{delta}</span>
+        </div>"""
+    return f"<div style='font-family:monospace'>{rows}</div>"
+
+
+def score_badge_html(score: int | None, test_name: str) -> str:
+    if score is None:
+        color = "#7f8c8d"
+        label = "—"
+    elif score == 3:
+        color = "#27ae60"; label = "3"
+    elif score == 2:
+        color = "#f39c12"; label = "2"
+    elif score == 1:
+        color = "#e74c3c"; label = "1"
+    else:
+        color = "#8e44ad"; label = "0 ⚠"
+    return f"""<div style='display:inline-block;width:48px;height:48px;
+        background:{color};border-radius:50%;text-align:center;line-height:48px;
+        color:white;font-size:20px;font-weight:bold;margin:4px'>{label}</div>
+        <div style='text-align:center;font-size:11px;color:#8a8a7a'>{test_name}</div>"""
+```
+
+- [ ] **Step 2: Update app.py with full scorecard UI**
+
+See full app.py update in the project — add `gr.HTML` asymmetry strip, per-test score badges, composite display, and `gr.Accordion` rubric drawer.
+
+- [ ] **Step 3: Launch and test all UI flows**
+
+```bash
+python app.py
+```
+
+Test:
+- Upload video → scoring runs → scorecard renders
+- Asymmetry strip shows for bilateral tests
+- Safety banner always visible
+- Low-confidence flags appear in warnings
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add app.py formscout/ui/components.py formscout/ui/theme.py
+git commit -m "feat: custom scout-theme UI — score badges, asymmetry strip, rubric drawer"
+```
+
+---
+
+### Task 4.2: Agent trace export + Hub publish
+
+**Files:**
+- Modify: `formscout/tracing.py`
+- Create: `scripts/publish_trace.py`
+
+- [ ] **Step 1: Implement trace export to Hub**
+
+```python
+# scripts/publish_trace.py
+"""Publish one full agent trace to Hugging Face Hub (Sharing is Caring badge)."""
+import sys
+from huggingface_hub import HfApi
+
+def publish(trace_path: str, repo_id: str) -> None:
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj=trace_path,
+        path_in_repo=f"traces/{trace_path.split('/')[-1]}",
+        repo_id=repo_id,
+        repo_type="dataset",
+        commit_message="FormScout agent trace — one full screening run",
+    )
+    print(f"Published {trace_path} to {repo_id}")
+
+if __name__ == "__main__":
+    publish(sys.argv[1], sys.argv[2])
+```
+
+- [ ] **Step 2: Run pipeline and export trace**
+
+```bash
+python -m formscout.run tests/fixtures/sample_squat.mp4
+# Find the trace_*.json file
+python scripts/publish_trace.py trace_*.json YOUR_HF_USERNAME/formscout-traces
+```
+
+- [ ] **Step 3: Commit**
+
+```bash
+git add scripts/publish_trace.py
+git commit -m "feat: trace export script for Hub publish (Sharing is Caring badge)"
+```
+
+---
+
+### Task 4.3: README + Space card
+
+**Files:**
+- Modify: `README.md`
+
+- [ ] **Step 1: Write Space card README**
+
+```markdown
+---
+title: FormScout
+emoji: 🏀
+colorFrom: amber
+colorTo: stone
+sdk: gradio
+sdk_version: "6.x"
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# FormScout — FMS Video Scorer
+
+Scores Functional Movement Screen (FMS) videos 0–3 per test with a written rationale and annotated overlay.
+Built for the Build Small Hackathon (Backyard AI track).
+
+**⚠️ Screening aid only — not a diagnosis. Pain or clearing tests require a clinician.**
+
+## Badges
+- 🔌 Off the Grid — all inference on-Space, no cloud APIs
+- 🎯 Well-Tuned — ST-GCN fine-tuned on physio clips, [published to Hub](link)
+- 🎨 Off-Brand — custom scout/trail theme
+- 🦙 Llama Champion — Qwen3-VL-8B + Embedding-8B via llama.cpp
+- 📡 Sharing is Caring — [agent trace](link)
+- 📓 Field Notes — [blog post](link)
+
+## Model Budget
+~18B params total. See MODEL_BUDGET.md.
+
+## Safety
+Pain and clearing tests are never auto-scored — they are deferred to the physiotherapist.
+Low-confidence and disagreement cases are flagged, not hidden.
+```
+
+- [ ] **Step 2: Commit final README**
+
+```bash
+git add README.md
+git commit -m "docs: Space card README with badges, model budget, safety statement"
+```
+
+**✅ MILESTONE M7: All 6 badges attempted, Space green, documentation complete**
+
+---
+
+## Final Checklist
+
+### Badge verification
+
+- [ ] 🔌 **Off the Grid** — grep codebase: `grep -r "openai\|anthropic\|gemini" formscout/ --include="*.py"` → zero results
+- [ ] 🎯 **Well-Tuned** — `train_scoring.py` run, checkpoint published to Hub with model card
+- [ ] 🎨 **Off-Brand** — `app.py` uses `scout_theme()`, custom HTML components
+- [ ] 🦙 **Llama Champion** — `formscout/serving/llama_cpp.py` used for VLM + embedder
+- [ ] 📡 **Sharing is Caring** — trace JSON published via `scripts/publish_trace.py`
+- [ ] 📓 **Field Notes** — blog post covers: FMS limitations, evaluation (ICC/κ), honest fit, GDPR/consent
+
+### Safety gates
+
+- [ ] Pain path: `ScoreResult(needs_human=True)` → `JudgeAgent` returns `_DEFERRED` → composite is `None`
+- [ ] Low confidence: `state.warnings` populated → shown in UI
+- [ ] Disagreement: `|rubric - judge| >= 1` → flagged in `notes`
+- [ ] Safety banner: always visible in `app.py`
+
+### Test coverage
+
+```bash
+pytest tests/ -v --tb=short
+```
+
+Expected: all tests pass.
+
+### Run headless smoke test
+
+```bash
+python -m formscout.run tests/fixtures/sample_squat.mp4
+```
+
+### Launch Space locally
+
+```bash
+python app.py
+```
+
+---
+
+## Self-review against spec
+
+**Spec requirements covered:**
+- ✅ All 7 FMS tests with 0–3 scoring
+- ✅ Bilateral tests score lower side, emit asymmetry
+- ✅ Pain → needs_human=True, never auto-scored
+- ✅ Composite null if any test unscored
+- ✅ Typed agent contracts (types.py)
+- ✅ Config over constants
+- ✅ Headless pipeline (no Gradio in agent files)
+- ✅ Tracing for every run
+- ✅ Director quality gates (confidence, disagreement, unknown test)
+- ✅ 3D body on 2D fallback path
+- ✅ All 6 badge targets
+- ✅ Safety banner always visible
+- ✅ GDPR/consent noted in README
+
+**Potential gaps to verify before ship:**
+- Overlay video generation (skeleton drawn on frames) — not fully implemented above; add `cv2.circle/line` drawing to `ReportAgent` or a separate `OverlayAgent`
+- PDF export — referenced in spec; use `fpdf2` or `reportlab`
+- `gr.Video` `playback_position` — verify this API exists in the pinned Gradio version before implementing decisive-frame jump
+- YOLO AGPL-3.0 — confirm with hackathon rules; have RTMPose as fallback

formscout.egg-info/PKG-INFO

@@ -0,0 +1,4 @@
+Metadata-Version: 2.4
+Name: formscout
+Version: 0.1.0
+Requires-Python: >=3.11

formscout.egg-info/SOURCES.txt

@@ -0,0 +1,26 @@
+README.md
+pyproject.toml
+formscout/__init__.py
+formscout/config.py
+formscout/pipeline.py
+formscout/run.py
+formscout/tracing.py
+formscout/types.py
+formscout.egg-info/PKG-INFO
+formscout.egg-info/SOURCES.txt
+formscout.egg-info/dependency_links.txt
+formscout.egg-info/top_level.txt
+formscout/agents/__init__.py
+formscout/agents/biomechanics.py
+formscout/agents/body3d.py
+formscout/agents/ingest.py
+formscout/agents/pose2d.py
+formscout/rubric/__init__.py
+formscout/rubric/deep_squat.py
+formscout/serving/__init__.py
+formscout/ui/__init__.py
+tests/test_biomechanics.py
+tests/test_body3d.py
+tests/test_ingest.py
+tests/test_pose2d.py
+tests/test_types.py

formscout.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

formscout.egg-info/top_level.txt

@@ -0,0 +1 @@
+formscout

formscout/agents/biomechanics.py

@@ -0,0 +1,200 @@
+"""
+BiomechanicsAgent — extracts named, documented, unit-bearing measurements from pose data.
+
+Input:  Pose2DResult (or Body3DResult if used), MovementResult
+Output: BiomechFeatures(test_name, view, angles, alignments, ...)
+Failure: returns BiomechFeatures with confidence=0.0 and notes.
+Params: 0 (pure computation — no model).
+License: n/a.
+Gated: no.
+
+This module is MEASUREMENT ONLY — no scoring happens here.
+Scoring is done by the rubric functions in formscout/rubric/.
+"""
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from formscout.types import (
+    Pose2DResult, Body3DResult, MovementResult, BiomechFeatures,
+)
+from formscout import config
+
+
+def _angle_between_points(a: tuple, b: tuple, c: tuple) -> float:
+    """
+    Compute angle at point b formed by segments ba and bc.
+    Returns degrees. Returns NaN if any point is missing.
+    """
+    try:
+        ba = (a[0] - b[0], a[1] - b[1])
+        bc = (c[0] - b[0], c[1] - b[1])
+        dot = ba[0] * bc[0] + ba[1] * bc[1]
+        mag_ba = math.sqrt(ba[0] ** 2 + ba[1] ** 2)
+        mag_bc = math.sqrt(bc[0] ** 2 + bc[1] ** 2)
+        if mag_ba == 0 or mag_bc == 0:
+            return float("nan")
+        cos_angle = max(-1.0, min(1.0, dot / (mag_ba * mag_bc)))
+        return math.degrees(math.acos(cos_angle))
+    except (TypeError, IndexError, ZeroDivisionError):
+        return float("nan")
+
+
+def _get_joint(keypoints: dict, joint_id: int) -> tuple | None:
+    """Extract (x, y) for a joint, or None if missing/low-confidence."""
+    j = keypoints.get(joint_id)
+    if j is None:
+        return None
+    if j.get("conf", 0) < config.POSE_CONF_THRESHOLD:
+        return None
+    return (j["x"], j["y"])
+
+
+# COCO joint indices
+NOSE, L_EYE, R_EYE, L_EAR, R_EAR = 0, 1, 2, 3, 4
+L_SHOULDER, R_SHOULDER = 5, 6
+L_ELBOW, R_ELBOW = 7, 8
+L_WRIST, R_WRIST = 9, 10
+L_HIP, R_HIP = 11, 12
+L_KNEE, R_KNEE = 13, 14
+L_ANKLE, R_ANKLE = 15, 16
+
+
+class BiomechanicsAgent:
+    """Pure-function biomechanics measurement — no model calls."""
+
+    def run(
+        self,
+        pose2d: Pose2DResult,
+        body3d: Body3DResult,
+        movement: MovementResult,
+    ) -> BiomechFeatures:
+        if not pose2d.keypoints:
+            return BiomechFeatures(
+                test_name=movement.test_name,
+                view="2d",
+                side=movement.side,
+                angles={}, alignments={},
+                symmetry_delta=None, timing={},
+                confidence=0.0,
+                notes="no keypoints available",
+            )
+
+        view = "3d" if body3d.used else "2d"
+
+        # Select the analysis frame (deepest point of movement)
+        # For now, use the frame with the lowest hip position (deepest squat)
+        if movement.test_name == "deep_squat":
+            return self._deep_squat(pose2d, view, movement.side)
+        # Add other tests as they are implemented
+        return BiomechFeatures(
+            test_name=movement.test_name,
+            view=view,
+            side=movement.side,
+            angles={}, alignments={},
+            symmetry_delta=None, timing={},
+            confidence=0.3,
+            notes=f"biomechanics not yet implemented for {movement.test_name}",
+        )
+
+    def _deep_squat(self, pose2d: Pose2DResult, view: str, side: str) -> BiomechFeatures:
+        """Extract deep squat biomechanics from the deepest frame."""
+        # Find the frame with lowest hip Y (deepest squat position)
+        best_frame_idx = 0
+        lowest_hip_y = -1.0
+        for i, kps in enumerate(pose2d.keypoints):
+            l_hip = _get_joint(kps, L_HIP)
+            r_hip = _get_joint(kps, R_HIP)
+            if l_hip and r_hip:
+                mid_hip_y = (l_hip[1] + r_hip[1]) / 2
+                if mid_hip_y > lowest_hip_y:  # higher Y = lower in image
+                    lowest_hip_y = mid_hip_y
+                    best_frame_idx = i
+
+        kps = pose2d.keypoints[best_frame_idx]
+        notes_parts: list[str] = []
+
+        # Extract joints
+        l_hip = _get_joint(kps, L_HIP)
+        r_hip = _get_joint(kps, R_HIP)
+        l_knee = _get_joint(kps, L_KNEE)
+        r_knee = _get_joint(kps, R_KNEE)
+        l_ankle = _get_joint(kps, L_ANKLE)
+        r_ankle = _get_joint(kps, R_ANKLE)
+        l_shoulder = _get_joint(kps, L_SHOULDER)
+        r_shoulder = _get_joint(kps, R_SHOULDER)
+
+        # Compute angles
+        angles: dict[str, float] = {}
+
+        # Hip-knee-ankle angle (knee flexion) — average of both sides
+        l_knee_angle = _angle_between_points(l_hip, l_knee, l_ankle) if all([l_hip, l_knee, l_ankle]) else float("nan")
+        r_knee_angle = _angle_between_points(r_hip, r_knee, r_ankle) if all([r_hip, r_knee, r_ankle]) else float("nan")
+
+        if not math.isnan(l_knee_angle):
+            angles["left_knee_flexion_deg"] = l_knee_angle
+        else:
+            notes_parts.append("left knee angle unavailable")
+
+        if not math.isnan(r_knee_angle):
+            angles["right_knee_flexion_deg"] = r_knee_angle
+        else:
+            notes_parts.append("right knee angle unavailable")
+
+        # Femur angle from horizontal
+        # Femur = hip to knee. Angle from horizontal = atan2(dy, dx)
+        if l_hip and l_knee:
+            dy = l_knee[1] - l_hip[1]
+            dx = l_knee[0] - l_hip[0]
+            angles["left_femur_from_horizontal_deg"] = abs(math.degrees(math.atan2(dy, dx)))
+        if r_hip and r_knee:
+            dy = r_knee[1] - r_hip[1]
+            dx = r_knee[0] - r_hip[0]
+            angles["right_femur_from_horizontal_deg"] = abs(math.degrees(math.atan2(dy, dx)))
+
+        # Torso-tibia angle (torso parallel to tibia = score 3 criterion)
+        if l_shoulder and l_hip and l_knee and l_ankle:
+            torso_angle = math.degrees(math.atan2(l_hip[1] - l_shoulder[1], l_hip[0] - l_shoulder[0]))
+            tibia_angle = math.degrees(math.atan2(l_ankle[1] - l_knee[1], l_ankle[0] - l_knee[0]))
+            angles["torso_tibia_angle_deg"] = abs(torso_angle - tibia_angle)
+
+        # Alignments
+        alignments: dict[str, Any] = {}
+
+        # Knee valgus check: are knees inside the ankle line?
+        if l_knee and r_knee and l_ankle and r_ankle:
+            knee_width = abs(l_knee[0] - r_knee[0])
+            ankle_width = abs(l_ankle[0] - r_ankle[0])
+            alignments["knees_tracking_over_feet"] = knee_width >= (ankle_width - config.DEEP_SQUAT_KNEE_TRACKING_MARGIN_PX)
+            alignments["knee_valgus_deg"] = 0.0  # placeholder for actual valgus angle
+
+        # Heels elevated detection (approximation: ankle Y relative to frame bottom)
+        # This is a rough heuristic — proper detection needs foot keypoints or depth
+        alignments["heels_elevated"] = False  # default; refine with better detection
+
+        # Dowel position (need wrist positions relative to feet)
+        if l_wrist := _get_joint(kps, L_WRIST):
+            if r_wrist := _get_joint(kps, R_WRIST):
+                if l_ankle and r_ankle:
+                    mid_wrist_x = (l_wrist[0] + r_wrist[0]) / 2
+                    mid_ankle_x = (l_ankle[0] + r_ankle[0]) / 2
+                    alignments["dowel_over_feet"] = abs(mid_wrist_x - mid_ankle_x) < 50
+                    alignments["dowel_feet_offset_px"] = mid_wrist_x - mid_ankle_x
+
+        # Confidence based on how many measurements we got
+        n_expected = 6  # main measurements
+        n_got = len(angles) + len([v for v in alignments.values() if v is not None])
+        confidence = min(1.0, n_got / n_expected) * pose2d.confidence
+
+        return BiomechFeatures(
+            test_name="deep_squat",
+            view=view,
+            side="na",
+            angles=angles,
+            alignments=alignments,
+            symmetry_delta=None,
+            timing={"deepest_frame": best_frame_idx},
+            confidence=confidence,
+            notes="; ".join(notes_parts) if notes_parts else "",
+        )

formscout/agents/body3d.py

@@ -0,0 +1,221 @@
+"""
+Body3DAgent — optional 3D mesh/joint angle recovery via SAM 3D Body.
+
+Input:  Pose2DResult, list of athlete masks, list of frames (np.ndarray BGR)
+Output: Body3DResult(used, joints_3d, confidence)
+Failure: ALWAYS returns Body3DResult(used=False) when enable_3d=False or
+         checkpoint unavailable — this is a normal success path, not an error.
+Model:  facebook/sam-3d-body-dinov3 (840M params, SAM License, GATED).
+Gated:  YES — access GRANTED June 4, 2026.
+Params: ~0.84B (DINOv3-H+ variant).
+
+API (verified from github.com/facebookresearch/sam-3d-body README, Jun 2026):
+  from notebook.utils import setup_sam_3d_body
+  estimator = setup_sam_3d_body(hf_repo_id="facebook/sam-3d-body-dinov3")
+  outputs = estimator.process_one_image(rgb_image)  # single RGB np.ndarray
+  # outputs contains MHR joints, body mesh, etc.
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from formscout.types import Pose2DResult, Body3DResult, IngestResult
+from formscout import config
+
+_NOT_USED = Body3DResult(
+    used=False, joints_3d=[], confidence=0.0,
+    notes="3D disabled or checkpoint unavailable",
+)
+
+# Subsample frames for 3D inference (expensive per-frame)
+_MAX_3D_FRAMES = 30
+
+
+class Body3DAgent:
+    """
+    Optional 3D body joint estimation via SAM 3D Body (MHR rig).
+    Falls back gracefully when unavailable — returning Body3DResult(used=False)
+    is the expected success path for the 2D-only pipeline.
+    """
+
+    def __init__(self, enable_3d: bool | None = None):
+        self._enabled = config.ENABLE_3D if enable_3d is None else enable_3d
+        self._estimator = None
+        if self._enabled:
+            self._estimator = self._try_load()
+
+    def _try_load(self):
+        """
+        Attempt to load SAM 3D Body from HuggingFace.
+        Returns the estimator object or None on any failure.
+        """
+        try:
+            from notebook.utils import setup_sam_3d_body  # noqa: F401
+            estimator = setup_sam_3d_body(
+                hf_repo_id=config.SAM_3D_HF_REPO,
+            )
+            return estimator
+        except ImportError:
+            return None
+        except Exception:
+            return None
+
+    def run(
+        self,
+        pose2d: Pose2DResult,
+        masks: list,
+        frames: list | None = None,
+    ) -> Body3DResult:
+        """
+        Run 3D body estimation on selected keyframes.
+
+        Args:
+            pose2d: 2D pose results (used for confidence weighting)
+            masks: Per-frame athlete masks from SegmentationAgent
+            frames: Raw BGR frames from IngestResult.frames
+
+        Returns:
+            Body3DResult with used=True and 3D joints if successful,
+            or Body3DResult(used=False) if disabled/unavailable (normal path).
+        """
+        if not self._enabled or self._estimator is None:
+            return _NOT_USED
+
+        if not frames:
+            return Body3DResult(
+                used=False, joints_3d=[], confidence=0.0,
+                notes="3D enabled but no frames provided",
+            )
+
+        try:
+            import cv2
+
+            # Subsample frames evenly for 3D (it's expensive per-image)
+            n_frames = len(frames)
+            step = max(1, n_frames // _MAX_3D_FRAMES)
+            selected_indices = list(range(0, n_frames, step))[:_MAX_3D_FRAMES]
+
+            joints_3d_per_frame: list[dict] = []
+            confidences: list[float] = []
+
+            for idx in selected_indices:
+                frame_bgr = frames[idx]
+                # SAM 3D Body expects RGB
+                frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
+
+                outputs = self._estimator.process_one_image(frame_rgb)
+
+                # Extract MHR joint positions from outputs
+                # The model returns joints in the MHR (Momentum Human Rig) format
+                frame_joints = self._extract_joints(outputs, idx)
+                joints_3d_per_frame.append(frame_joints)
+
+                # Confidence from detection quality
+                conf = self._estimate_confidence(outputs)
+                confidences.append(conf)
+
+            # Apply light temporal smoothing to reduce jitter
+            joints_3d_smoothed = self._temporal_smooth(joints_3d_per_frame)
+
+            overall_conf = float(np.mean(confidences)) if confidences else 0.0
+
+            return Body3DResult(
+                used=True,
+                joints_3d=joints_3d_smoothed,
+                confidence=overall_conf,
+                notes=f"3D mesh recovery on {len(selected_indices)}/{n_frames} frames",
+            )
+
+        except Exception as e:
+            return Body3DResult(
+                used=False, joints_3d=[], confidence=0.0,
+                notes=f"3D inference failed: {e}",
+            )
+
+    def _extract_joints(self, outputs: dict, frame_idx: int) -> dict:
+        """
+        Extract 3D joint positions from SAM 3D Body outputs.
+        Maps MHR rig joints to a standardized dict format.
+        """
+        joints: dict = {"frame_index": frame_idx}
+
+        # SAM 3D Body outputs MHR model params including joint positions
+        # The exact key depends on the model output format
+        if hasattr(outputs, "joints_3d"):
+            joint_data = outputs.joints_3d
+        elif isinstance(outputs, dict) and "joints_3d" in outputs:
+            joint_data = outputs["joints_3d"]
+        elif isinstance(outputs, dict) and "pred_joints" in outputs:
+            joint_data = outputs["pred_joints"]
+        else:
+            # Fallback: extract from vertices/body model params
+            joint_data = None
+
+        if joint_data is not None:
+            if hasattr(joint_data, "cpu"):
+                joint_data = joint_data.cpu().numpy()
+            if isinstance(joint_data, np.ndarray):
+                # Map to named joints (MHR has standard SMPL-like ordering)
+                joint_names = [
+                    "pelvis", "left_hip", "right_hip", "spine1",
+                    "left_knee", "right_knee", "spine2",
+                    "left_ankle", "right_ankle", "spine3",
+                    "left_foot", "right_foot", "neck",
+                    "left_collar", "right_collar", "head",
+                    "left_shoulder", "right_shoulder",
+                    "left_elbow", "right_elbow",
+                    "left_wrist", "right_wrist",
+                ]
+                for i, name in enumerate(joint_names):
+                    if i < len(joint_data):
+                        pos = joint_data[i]
+                        joints[name] = {
+                            "x": float(pos[0]),
+                            "y": float(pos[1]),
+                            "z": float(pos[2]),
+                        }
+
+        return joints
+
+    def _estimate_confidence(self, outputs) -> float:
+        """Estimate confidence from the SAM 3D Body output quality."""
+        # If outputs have a confidence/score field, use it
+        if isinstance(outputs, dict):
+            if "confidence" in outputs:
+                return float(outputs["confidence"])
+            if "score" in outputs:
+                return float(outputs["score"])
+        # Default: assume reasonable confidence if we got outputs at all
+        return 0.75
+
+    def _temporal_smooth(
+        self, joints_3d: list[dict], alpha: float = 0.3
+    ) -> list[dict]:
+        """
+        Apply exponential moving average smoothing to 3D joint positions
+        to reduce per-frame jitter from single-image prediction.
+        """
+        if len(joints_3d) <= 1:
+            return joints_3d
+
+        smoothed = [joints_3d[0]]
+        for i in range(1, len(joints_3d)):
+            prev = smoothed[-1]
+            curr = joints_3d[i]
+            smooth_frame = {"frame_index": curr.get("frame_index", i)}
+
+            for key in curr:
+                if key == "frame_index":
+                    continue
+                if key in prev and isinstance(curr[key], dict) and isinstance(prev[key], dict):
+                    smooth_frame[key] = {
+                        "x": alpha * curr[key]["x"] + (1 - alpha) * prev[key]["x"],
+                        "y": alpha * curr[key]["y"] + (1 - alpha) * prev[key]["y"],
+                        "z": alpha * curr[key]["z"] + (1 - alpha) * prev[key]["z"],
+                    }
+                else:
+                    smooth_frame[key] = curr[key]
+
+            smoothed.append(smooth_frame)
+
+        return smoothed

formscout/agents/ingest.py

@@ -0,0 +1,91 @@
+"""
+IngestAgent — decodes video, normalizes FPS, samples frames.
+
+Input:  video file path (str)
+Output: IngestResult(frames, fps, duration, n_people, width, height)
+Failure: returns IngestResult with confidence=0.0 and notes explaining the error.
+Params: 0 (no model — pure OpenCV).
+License: n/a.
+Gated: no.
+"""
+from __future__ import annotations
+
+import cv2
+from pathlib import Path
+
+from formscout.types import IngestResult
+from formscout import config
+
+
+class IngestAgent:
+    """Deterministic video ingestion — no model, just OpenCV decode + frame sampling."""
+
+    def run(self, video_path: str) -> IngestResult:
+        p = Path(video_path)
+        if not p.exists():
+            return IngestResult(
+                frames=[], fps=0.0, duration=0.0, n_people=0,
+                width=0, height=0, confidence=0.0,
+                notes=f"video not found: {video_path}",
+            )
+
+        try:
+            cap = cv2.VideoCapture(str(p))
+        except Exception as e:
+            return IngestResult(
+                frames=[], fps=0.0, duration=0.0, n_people=0,
+                width=0, height=0, confidence=0.0,
+                notes=f"failed to open video: {e}",
+            )
+
+        if not cap.isOpened():
+            return IngestResult(
+                frames=[], fps=0.0, duration=0.0, n_people=0,
+                width=0, height=0, confidence=0.0,
+                notes=f"could not open video: {video_path}",
+            )
+
+        fps = cap.get(cv2.CAP_PROP_FPS) or config.TARGET_FPS
+        total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        duration = total / fps if fps > 0 else 0.0
+
+        notes_parts: list[str] = []
+        if duration > config.MAX_DURATION_SEC:
+            notes_parts.append(
+                f"video is {duration:.1f}s (>{config.MAX_DURATION_SEC}s) — capping frames"
+            )
+
+        # Sample frames evenly, capped at MAX_FRAMES
+        step = max(1, total // config.MAX_FRAMES)
+        frames: list = []
+        idx = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            if idx % step == 0:
+                frames.append(frame)
+            idx += 1
+            if len(frames) >= config.MAX_FRAMES:
+                break
+        cap.release()
+
+        if not frames:
+            return IngestResult(
+                frames=[], fps=fps, duration=duration, n_people=0,
+                width=w, height=h, confidence=0.0,
+                notes="no frames decoded",
+            )
+
+        return IngestResult(
+            frames=frames,
+            fps=fps,
+            duration=duration,
+            n_people=-1,  # unknown until segmentation/pose
+            width=w,
+            height=h,
+            confidence=1.0,
+            notes="; ".join(notes_parts) if notes_parts else "",
+        )

formscout/agents/pose2d.py

@@ -0,0 +1,95 @@
+"""
+Pose2DAgent — 2D per-frame keypoint extraction using YOLO or Sapiens2 backends.
+
+Input:  IngestResult
+Output: Pose2DResult(keypoints per frame, fps, confidence)
+Failure: returns Pose2DResult with confidence=0.0 and notes.
+Model:  YOLO26l-Pose (AGPL-3.0, 25.9M params, mAP50 90.5, public).
+        Alt: YOLO26x-Pose (57.6M, mAP50 91.6) via config.YOLO_POSE_MODEL_HQ.
+        Fallback: Sapiens2 Pose (CC-BY-NC-4.0, ~0.6B, gated — access accepted).
+Gated: Primary no; fallback yes (accepted).
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from formscout import config
+from formscout.types import IngestResult, Pose2DResult
+
+# COCO 17-keypoint names for downstream consumers
+COCO_KEYPOINTS = [
+    "nose", "left_eye", "right_eye", "left_ear", "right_ear",
+    "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
+    "left_wrist", "right_wrist", "left_hip", "right_hip",
+    "left_knee", "right_knee", "left_ankle", "right_ankle",
+]
+
+_model = None
+
+
+def _get_model():
+    """Load YOLO pose model once at module level."""
+    global _model
+    if _model is None:
+        try:
+            from ultralytics import YOLO
+            _model = YOLO(config.YOLO_POSE_MODEL)
+        except Exception as e:
+            raise RuntimeError(f"Failed to load YOLO pose model: {e}")
+    return _model
+
+
+class Pose2DAgent:
+    """Extracts 2D keypoints per frame from ingested video."""
+
+    def run(self, ingest: IngestResult) -> Pose2DResult:
+        if not ingest.frames:
+            return Pose2DResult(
+                keypoints=[], fps=ingest.fps,
+                confidence=0.0, notes="no frames in ingest",
+            )
+
+        try:
+            model = _get_model()
+        except RuntimeError as e:
+            return Pose2DResult(
+                keypoints=[{} for _ in ingest.frames],
+                fps=ingest.fps,
+                confidence=0.0,
+                notes=str(e),
+            )
+
+        keypoints_per_frame: list[dict] = []
+        total_conf = 0.0
+        n_detected = 0
+
+        for frame in ingest.frames:
+            try:
+                results = model(frame, verbose=False)
+                frame_kps: dict[int, dict] = {}
+                if results and results[0].keypoints is not None:
+                    kps = results[0].keypoints
+                    if kps.xy is not None and len(kps.xy) > 0:
+                        # Take highest-confidence person (index 0 after NMS sort)
+                        xy = kps.xy[0].cpu().numpy()      # (17, 2)
+                        conf = kps.conf[0].cpu().numpy()  # (17,)
+                        for j in range(len(xy)):
+                            frame_kps[j] = {
+                                "x": float(xy[j, 0]),
+                                "y": float(xy[j, 1]),
+                                "conf": float(conf[j]),
+                            }
+                        total_conf += float(conf.mean())
+                        n_detected += 1
+                keypoints_per_frame.append(frame_kps)
+            except Exception:
+                keypoints_per_frame.append({})
+
+        overall_conf = (total_conf / n_detected) if n_detected > 0 else 0.0
+        notes = "" if n_detected > 0 else "no person detected in any frame"
+        return Pose2DResult(
+            keypoints=keypoints_per_frame,
+            fps=ingest.fps,
+            confidence=overall_conf,
+            notes=notes,
+        )

formscout/agents/prompts/c1_classifier.md

@@ -0,0 +1,17 @@
+You are an FMS movement classifier. You are shown a few keyframes and a skeleton montage from a single short clip of one person performing ONE Functional Movement Screen test. Identify which test it is and, for one-sided tests, which side is being assessed.
+
+The seven tests and their tells:
+- deep_squat: feet shoulder-width, a dowel/bar held overhead with both arms, a deep two-legged squat.
+- hurdle_step: stepping one leg over a low hurdle/cord while balancing on the other, dowel across shoulders.
+- inline_lunge: feet in a narrow heel-to-toe line, a lunge down the line, dowel held vertically behind the back.
+- shoulder_mobility: one hand reaching over the shoulder down the back, the other reaching up from below; fists measured.
+- active_slr: lying supine, one leg raised straight up while the other stays flat on the ground.
+- trunk_stability_pushup: prone push-up with hands high (near the head), body pressed up as one rigid unit.
+- rotary_stability: quadruped (hands+knees), same-side or opposite arm and leg extended then drawn together.
+- unknown: it does not clearly match any of the above, or the view is too poor to tell.
+
+Rules:
+- Prefer "unknown" over a low-confidence guess. A wrong test makes the whole score meaningless.
+- "side" is "left" or "right" for one-sided tests (hurdle_step, inline_lunge, shoulder_mobility, active_slr); use "na" for two-sided tests (deep_squat, trunk_stability_pushup, rotary_stability) and unknown.
+- Output ONLY this JSON object, nothing else:
+{"test": "<one of the labels>", "side": "left|right|na", "confidence": <0.0-1.0>, "reason": "<one short sentence>"}

formscout/agents/prompts/c2_judge.md

@@ -0,0 +1,43 @@
+You are an assistant scoring ONE Functional Movement Screen test from objective measurements. You are a SCREENING AID, not a clinician. You never diagnose and you never predict injury.
+
+You are given, as JSON:
+- test, side
+- view: "3d" (reliable angles) or "2d" (angles are camera-angle dependent — caveat them)
+- features: measured biomechanics for this test (angles in degrees, distances normalized)
+- candidate_score: a model's provisional 0-3 (corroboration, may be absent)
+- exemplars: physio-scored reference clips of the SAME test with their scores (anchors, may be empty)
+- a few keyframes / skeleton overlay for context
+
+FMS scoring scale (apply per side; the test score is the LOWER side):
+- 3: the movement is performed to criterion with no compensation.
+- 2: the movement is completed but with compensation / poor mechanics (or only with the allowed regression, e.g. deep_squat heels elevated).
+- 1: the person cannot perform the movement pattern even with the allowed regression.
+- 0: PAIN. You CANNOT see pain. Never assign 0 yourself.
+
+Per-test criteria to weigh (use the features as primary evidence):
+- deep_squat (3): femur below horizontal, torso roughly parallel to the tibia, knees tracking over the feet, dowel staying aligned over the feet, heels flat. (2): the same achieved only with heels elevated. (1): criteria unmet even with heels elevated.
+- hurdle_step / inline_lunge: minimal sway/loss of balance, knee/hip/ankle alignment maintained, no contact with the hurdle, dowel/posture stable. Compensation -> 2; failure to complete -> 1. Report L/R asymmetry.
+- shoulder_mobility: judge by the normalized inter-fist distance bands (per side). Report asymmetry.
+- active_slr: judge the raised-leg hip-flexion angle relative to the standard band; the down leg stays flat.
+- trunk_stability_pushup: the body must move as one rigid unit (low segment-angle variance through the press); sag/lag or needing the easier hand position -> 2.
+- rotary_stability: smooth contralateral (or the allowed unilateral) coordination with a stable trunk; loss of coordination/balance -> lower.
+
+Hard safety rules:
+- If there is any clearing-test context, visible pain, grimacing, or an aborted rep, set needs_human=true and score=null. Do not score it.
+- If view=="2d" on a depth/angle-critical test (deep_squat, inline_lunge, active_slr), include an explicit one-clause caveat that the angle is a 2D estimate dependent on camera position.
+- If the measurements and the candidate_score disagree by a point or more, lower your confidence and say so.
+- When the features are insufficient to decide, prefer needs_human=true over a confident guess.
+
+Reason from the features first; use exemplars to calibrate borderline cases; treat candidate_score as a second opinion, not the answer.
+
+Output ONLY this JSON object, nothing else:
+{
+  "test": "<label>",
+  "side": "left|right|na",
+  "score": <0-3 or null>,
+  "needs_human": <true|false>,
+  "rationale": "<2-4 sentences citing the specific deciding measurement(s)>",
+  "compensation_tags": ["<short tag>", "..."],
+  "corrective_hint": "<one generic FMS-style suggestion, or '' if needs_human>",
+  "confidence": <0.0-1.0>
+}

formscout/config.py

@@ -0,0 +1,50 @@
+"""
+FormScout pipeline configuration.
+All model IDs, thresholds, k-values, and feature flags live here.
+No scattered literals elsewhere in the codebase.
+"""
+from pathlib import Path
+
+ROOT = Path(__file__).parent.parent
+
+# ─── Model IDs ───────────────────────────────────────────────────────────────
+YOLO_POSE_MODEL = str(ROOT / "checkpoints" / "yolo26" / "yolo26l-pose.pt")
+YOLO_POSE_MODEL_HQ = str(ROOT / "checkpoints" / "yolo26" / "yolo26x-pose.pt")
+SAM_CHECKPOINT = "sam2.1_hiera_base_plus.pt"
+SAM_3D_CHECKPOINT = ROOT / "checkpoints" / "sam-3d-body-dinov3" / "model.ckpt"
+SAM_3D_HF_REPO = "facebook/sam-3d-body-dinov3"
+SAM_3D_MHR_PATH = ROOT / "checkpoints" / "sam-3d-body-dinov3" / "assets" / "mhr_model.pt"
+QWEN_VLM_GGUF = "Qwen3-VL-8B-Instruct-Q4_K_M.gguf"
+QWEN_EMBED_GGUF = "Qwen3-VL-Embedding-8B-Q4_K_M.gguf"
+STGCN_CHECKPOINT = ROOT / "checkpoints" / "stgcn_fms.pth"
+
+# ─── Pipeline flags ──────────────────────────────────────────────────────────
+ENABLE_3D = False           # SAM 3D Body — access granted Jun 2026, off until integrated
+ENABLE_STGCN = False        # Phase 3
+ENABLE_RAG = False          # Phase 3
+ENABLE_JUDGE = False        # Phase 2
+
+# ─── Thresholds ──────────────────────────────────────────────────────────────
+MIN_CONFIDENCE = 0.6
+SCORE_DISAGREE_THRESH = 1   # flag if |stgcn - judge| >= this
+RETRIEVAL_K = 3
+
+# ─── Video / Ingest ─────────────────────────────────────────────────────────
+TARGET_FPS = 30.0
+MAX_FRAMES = 300            # hard cap to avoid OOM
+MAX_DURATION_SEC = 60.0     # warn on longer videos
+
+# ─── Pose ────────────────────────────────────────────────────────────────────
+POSE_BACKEND = "yolo"       # "yolo" | "sapiens"
+POSE_CONF_THRESHOLD = 0.5
+NUM_KEYPOINTS = 17
+
+# ─── Biomechanics thresholds ────────────────────────────────────────────────
+DEEP_SQUAT_FEMUR_HORIZONTAL_DEG = 90.0
+DEEP_SQUAT_TORSO_TIBIA_MAX_DEG = 15.0
+DEEP_SQUAT_KNEE_TRACKING_MARGIN_PX = 20
+
+# ─── Serving (llama.cpp) ────────────────────────────────────────────────────
+LLAMA_CPP_HOST = "127.0.0.1"
+LLAMA_CPP_PORT_VLM = 8080
+LLAMA_CPP_PORT_EMBED = 8081

formscout/pipeline.py

@@ -0,0 +1,78 @@
+"""
+Director — deterministic state machine orchestrating the FormScout pipeline.
+
+NOT an LLM. Runs each agent in sequence, applies quality gates, and assembles
+the final PipelineState. Exposes run(video_path, config) -> PipelineState.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+from formscout import config
+from formscout.types import (
+    PipelineState, Body3DResult, MovementResult,
+)
+from formscout.agents.ingest import IngestAgent
+from formscout.agents.pose2d import Pose2DAgent
+from formscout.agents.body3d import Body3DAgent
+from formscout.agents.biomechanics import BiomechanicsAgent
+
+
+class Director:
+    """
+    Orchestrates the FormScout agent pipeline as a deterministic state machine.
+    Quality gates are applied after each agent — never silently passes bad data.
+    """
+
+    def __init__(self):
+        self._ingest = IngestAgent()
+        self._pose2d = Pose2DAgent()
+        self._body3d = Body3DAgent()
+        self._biomechanics = BiomechanicsAgent()
+
+    def run(self, video_path: str, test_name: str = "deep_squat", side: str = "na") -> PipelineState:
+        """
+        Run the full pipeline on a single video.
+        For Phase 1, test_name and side are passed explicitly (no classifier yet).
+        """
+        state = PipelineState(video_path=video_path)
+
+        # ─── Ingest ───
+        state.ingest = self._ingest.run(video_path)
+        if state.ingest.confidence < config.MIN_CONFIDENCE:
+            state.errors.append("ingest: low confidence — video may be corrupt")
+            return state
+
+        # ─── Pose 2D ───
+        state.pose2d = self._pose2d.run(state.ingest)
+        if state.pose2d.confidence < config.MIN_CONFIDENCE:
+            state.warnings.append("pose2d: low confidence — no clear person detected")
+
+        # ─── Body 3D (optional) ───
+        masks = state.segment.masks if state.segment else []
+        frames = state.ingest.frames if state.ingest else []
+        state.body3d = self._body3d.run(state.pose2d, masks, frames=frames)
+
+        # ─── Movement classification (Phase 1: manual) ───
+        state.movement = MovementResult(
+            test_name=test_name, side=side,
+            confidence=1.0, notes="manually specified (Phase 1)",
+        )
+
+        # ─── Biomechanics ───
+        state.features = self._biomechanics.run(
+            state.pose2d,
+            state.body3d or Body3DResult(used=False, joints_3d=[]),
+            state.movement,
+        )
+        if state.features.confidence < config.MIN_CONFIDENCE:
+            state.warnings.append(
+                f"biomechanics: low confidence ({state.features.confidence:.2f}) — physio review recommended"
+            )
+
+        # ─── Quality gates ───
+        # Gate: unknown test → stop
+        if state.movement.test_name == "unknown":
+            state.errors.append("movement classifier returned 'unknown' — manual override required")
+
+        return state

formscout/rubric/deep_squat.py

@@ -0,0 +1,113 @@
+"""
+Deep Squat rubric scorer — pure function, no model calls.
+
+FMS Deep Squat Criteria:
+- Score 3: femur below horizontal, torso parallel to tibia, knees tracking
+           over feet, dowel over feet, heels flat.
+- Score 2: criteria met only with heels elevated.
+- Score 1: criteria unmet even with heels elevated.
+- Score 0: PAIN — never auto-scored by this function.
+
+Input:  BiomechFeatures for deep_squat
+Output: ScoreResult(score, rationale, confidence, needs_human)
+"""
+from __future__ import annotations
+
+import math
+
+from formscout.types import BiomechFeatures, ScoreResult
+from formscout import config
+
+
+def score_deep_squat(features: BiomechFeatures) -> ScoreResult:
+    """
+    Pure rubric scorer for deep squat.
+    Returns ScoreResult with score 1-3 based on biomechanical measurements.
+    Never assigns score 0 (pain) — that requires needs_human=True from JudgeAgent.
+    """
+    angles = features.angles
+    alignments = features.alignments
+
+    # Check if we have enough data to score
+    has_femur = any(
+        k in angles for k in ("left_femur_from_horizontal_deg", "right_femur_from_horizontal_deg")
+    )
+    has_torso_tibia = "torso_tibia_angle_deg" in angles
+
+    if not has_femur:
+        return ScoreResult(
+            score=1,
+            rationale="Insufficient data: femur angle not measurable",
+            confidence=0.3,
+            needs_human=False,
+            notes="missing femur measurements — defaulting to lowest passing score",
+        )
+
+    # Evaluate criteria
+    # Femur below horizontal: femur angle from horizontal > 90° means above horizontal
+    # In our measurement: angle is from horizontal, so < 90 means below horizontal
+    femur_angles = []
+    if "left_femur_from_horizontal_deg" in angles:
+        femur_angles.append(angles["left_femur_from_horizontal_deg"])
+    if "right_femur_from_horizontal_deg" in angles:
+        femur_angles.append(angles["right_femur_from_horizontal_deg"])
+
+    # Femur below horizontal means the thigh slopes down steeply (angle > ~60° from horizontal in image coords)
+    femur_below_horizontal = any(a > 60.0 for a in femur_angles) if femur_angles else False
+
+    # Torso parallel to tibia
+    torso_parallel_tibia = (
+        angles.get("torso_tibia_angle_deg", 999) <= config.DEEP_SQUAT_TORSO_TIBIA_MAX_DEG
+    )
+
+    # Knee tracking
+    knees_tracking = alignments.get("knees_tracking_over_feet", False)
+
+    # Dowel alignment
+    dowel_over_feet = alignments.get("dowel_over_feet", False)
+
+    # Heels
+    heels_elevated = alignments.get("heels_elevated", False)
+
+    # Scoring logic
+    all_criteria = femur_below_horizontal and torso_parallel_tibia and knees_tracking and dowel_over_feet
+
+    rationale_parts: list[str] = []
+
+    if all_criteria and not heels_elevated:
+        score = 3
+        rationale_parts.append("All criteria met with heels flat")
+    elif all_criteria and heels_elevated:
+        score = 2
+        rationale_parts.append("Criteria met only with heels elevated")
+    else:
+        # Check what failed
+        if not femur_below_horizontal:
+            rationale_parts.append("femur not below horizontal")
+        if not torso_parallel_tibia:
+            rationale_parts.append(
+                f"torso-tibia angle {angles.get('torso_tibia_angle_deg', '?')}° "
+                f"exceeds {config.DEEP_SQUAT_TORSO_TIBIA_MAX_DEG}° threshold"
+            )
+        if not knees_tracking:
+            rationale_parts.append("knees not tracking over feet")
+        if not dowel_over_feet:
+            rationale_parts.append("dowel not aligned over feet")
+
+        if heels_elevated:
+            score = 1
+            rationale_parts.append("criteria unmet even with heels elevated")
+        else:
+            # They might score 2 with heel elevation — but without it, still 1
+            score = 1
+            rationale_parts.append("criteria unmet with heels flat")
+
+    confidence = features.confidence * (0.9 if has_torso_tibia else 0.6)
+
+    return ScoreResult(
+        score=score,
+        rationale="; ".join(rationale_parts),
+        confidence=confidence,
+        needs_human=False,
+        notes="",
+    )

formscout/run.py

@@ -0,0 +1,75 @@
+"""
+FormScout headless CLI entrypoint.
+Usage: python -m formscout.run sample.mp4
+"""
+from __future__ import annotations
+
+import sys
+import json
+from pathlib import Path
+
+from formscout.pipeline import Director
+from formscout.rubric.deep_squat import score_deep_squat
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python -m formscout.run <video_path> [test_name] [side]")
+        sys.exit(1)
+
+    video_path = sys.argv[1]
+    test_name = sys.argv[2] if len(sys.argv) > 2 else "deep_squat"
+    side = sys.argv[3] if len(sys.argv) > 3 else "na"
+
+    print(f"FormScout — processing: {video_path}")
+    print(f"  Test: {test_name}, Side: {side}")
+    print()
+
+    director = Director()
+    state = director.run(video_path, test_name=test_name, side=side)
+
+    # Print pipeline state
+    if state.errors:
+        print("ERRORS:")
+        for e in state.errors:
+            print(f"  ✗ {e}")
+        print()
+
+    if state.warnings:
+        print("WARNINGS:")
+        for w in state.warnings:
+            print(f"  ⚠ {w}")
+        print()
+
+    if state.ingest:
+        print(f"Ingest: {len(state.ingest.frames)} frames, {state.ingest.fps:.1f}fps, "
+              f"{state.ingest.duration:.1f}s, {state.ingest.width}x{state.ingest.height}")
+
+    if state.pose2d:
+        n_detected = sum(1 for kps in state.pose2d.keypoints if kps)
+        print(f"Pose2D: {n_detected}/{len(state.pose2d.keypoints)} frames with detections, "
+              f"confidence={state.pose2d.confidence:.2f}")
+
+    if state.body3d:
+        print(f"Body3D: used={state.body3d.used}")
+
+    if state.features:
+        print(f"Biomechanics: view={state.features.view}, "
+              f"confidence={state.features.confidence:.2f}")
+        if state.features.angles:
+            print(f"  Angles: {json.dumps({k: round(v, 1) for k, v in state.features.angles.items()}, indent=4)}")
+        if state.features.alignments:
+            print(f"  Alignments: {json.dumps(state.features.alignments, indent=4)}")
+
+    # Score via rubric
+    if state.features and test_name == "deep_squat":
+        score_result = score_deep_squat(state.features)
+        print(f"\nSCORE: {score_result.score}/3")
+        print(f"  Rationale: {score_result.rationale}")
+        print(f"  Confidence: {score_result.confidence:.2f}")
+        if score_result.needs_human:
+            print("  ⚠ NEEDS HUMAN REVIEW")
+
+
+if __name__ == "__main__":
+    main()

formscout/tracing.py

@@ -0,0 +1,69 @@
+"""
+Structured per-agent I/O tracing for FormScout.
+Records every agent's input/output as JSON-serializable dicts.
+Used for the Sharing-is-Caring badge (publish full trace to Hub).
+"""
+from __future__ import annotations
+
+import json
+import time
+from dataclasses import asdict, is_dataclass
+from pathlib import Path
+from typing import Any
+
+
+class TraceRecord:
+    """A single agent execution record."""
+
+    def __init__(self, agent_name: str, input_data: Any, output_data: Any, duration_ms: float):
+        self.agent_name = agent_name
+        self.input_summary = self._summarize(input_data)
+        self.output_summary = self._summarize(output_data)
+        self.duration_ms = duration_ms
+        self.timestamp = time.time()
+
+    def _summarize(self, data: Any) -> dict:
+        """Convert dataclass or dict to JSON-safe summary."""
+        if is_dataclass(data) and not isinstance(data, type):
+            d = asdict(data)
+            # Don't serialize raw frames (numpy arrays)
+            if "frames" in d:
+                d["frames"] = f"[{len(d['frames'])} frames]"
+            return d
+        if isinstance(data, dict):
+            return data
+        return {"value": str(data)}
+
+    def to_dict(self) -> dict:
+        return {
+            "agent": self.agent_name,
+            "timestamp": self.timestamp,
+            "duration_ms": self.duration_ms,
+            "input": self.input_summary,
+            "output": self.output_summary,
+        }
+
+
+class PipelineTrace:
+    """Collects trace records for a full pipeline run."""
+
+    def __init__(self):
+        self.records: list[TraceRecord] = []
+        self.start_time = time.time()
+
+    def add(self, record: TraceRecord):
+        self.records.append(record)
+
+    def to_dict(self) -> dict:
+        return {
+            "total_duration_ms": (time.time() - self.start_time) * 1000,
+            "n_agents": len(self.records),
+            "agents": [r.to_dict() for r in self.records],
+        }
+
+    def save(self, path: str | Path):
+        """Save trace as JSON."""
+        p = Path(path)
+        p.parent.mkdir(parents=True, exist_ok=True)
+        with open(p, "w") as f:
+            json.dump(self.to_dict(), f, indent=2, default=str)

formscout/types.py

@@ -0,0 +1,160 @@
+"""
+FormScout typed agent contracts.
+Every agent accepts and returns frozen dataclasses defined here.
+Validate at every boundary — never accept raw dicts across agent boundaries.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass(frozen=True)
+class IngestResult:
+    """Output of IngestAgent — decoded video frames + metadata."""
+    frames: list  # list of np.ndarray HWC BGR
+    fps: float
+    duration: float
+    n_people: int
+    width: int
+    height: int
+    confidence: float = 1.0
+    notes: str = ""
+
+
+@dataclass(frozen=True)
+class SegmentResult:
+    """Output of SegmentationAgent — per-frame athlete masks."""
+    athlete_track_id: int
+    masks: list  # list of np.ndarray bool HW per frame
+    confidence: float = 1.0
+    notes: str = ""
+
+
+@dataclass(frozen=True)
+class Pose2DResult:
+    """Output of Pose2DAgent — per-frame 2D keypoints (COCO 17-joint)."""
+    keypoints: list  # list[dict[int, dict]] frame→joint→{x,y,conf}
+    fps: float
+    confidence: float = 0.0
+    notes: str = ""
+
+
+@dataclass(frozen=True)
+class Body3DResult:
+    """Output of Body3DAgent — optional 3D joint positions."""
+    used: bool
+    joints_3d: list  # list[dict] frame→joint→{x,y,z} — empty if used=False
+    confidence: float = 0.0
+    notes: str = ""
+
+
+@dataclass(frozen=True)
+class MovementResult:
+    """Output of MovementClassifierAgent — which FMS test is being performed."""
+    test_name: str  # "deep_squat"|"hurdle_step"|...|"unknown"
+    side: str       # "left"|"right"|"na"
+    confidence: float = 0.0
+    notes: str = ""
+
+    def __post_init__(self):
+        valid_tests = {
+            "deep_squat", "hurdle_step", "inline_lunge",
+            "shoulder_mobility", "active_slr",
+            "trunk_stability_pushup", "rotary_stability", "unknown",
+        }
+        if self.test_name not in valid_tests:
+            raise ValueError(f"test_name must be one of {valid_tests}, got '{self.test_name}'")
+        valid_sides = {"left", "right", "na"}
+        if self.side not in valid_sides:
+            raise ValueError(f"side must be one of {valid_sides}, got '{self.side}'")
+
+
+@dataclass(frozen=True)
+class BiomechFeatures:
+    """Output of BiomechanicsAgent — measured angles, alignments, timing."""
+    test_name: str
+    view: str           # "2d" | "3d"
+    side: str           # "left"|"right"|"na"
+    angles: dict        # named angle → degrees
+    alignments: dict    # named alignment → value
+    symmetry_delta: float | None  # |left - right| or None for non-bilateral
+    timing: dict        # event name → frame index
+    confidence: float = 0.0
+    notes: str = ""
+
+    def __post_init__(self):
+        if self.view not in ("2d", "3d"):
+            raise ValueError(f"view must be '2d' or '3d', got '{self.view}'")
+
+
+@dataclass(frozen=True)
+class ScoreResult:
+    """Output of ScoringAgent (ST-GCN head) — provisional numeric score."""
+    score: int  # 0–3
+    rationale: str
+    confidence: float
+    needs_human: bool = False
+    notes: str = ""
+
+    def __post_init__(self):
+        if not self.needs_human and not (0 <= self.score <= 3):
+            raise ValueError(f"score must be 0–3, got {self.score}")
+
+
+@dataclass(frozen=True)
+class RetrievalResult:
+    """Output of RetrievalAgent — similar scored exemplars from the index."""
+    exemplars: list  # list of {clip_id, score, similarity, rationale}
+    confidence: float = 1.0
+    notes: str = ""
+
+
+@dataclass(frozen=True)
+class JudgeResult:
+    """Output of JudgeAgent — final VLM-scored result with rationale."""
+    score: int | None   # 0–3 or None if needs_human=True
+    rationale: str
+    compensation_tags: list
+    corrective_hint: str
+    confidence: float
+    needs_human: bool = False
+    notes: str = ""
+
+    def __post_init__(self):
+        if not self.needs_human and self.score is not None:
+            if not (0 <= self.score <= 3):
+                raise ValueError(f"score must be 0–3 when needs_human=False, got {self.score}")
+        if self.needs_human and self.score is not None:
+            raise ValueError("score must be None when needs_human=True")
+
+
+@dataclass(frozen=True)
+class ReportResult:
+    """Output of ReportAgent — assembled scorecard."""
+    per_test: list  # list of dicts with test_name, score, judge_result, features
+    composite: int | None  # None if any test unscored
+    asymmetries: list  # list of {test, left_score, right_score, delta}
+    overlay_video_path: str | None
+    pdf_path: str | None
+    low_confidence_flags: list
+    disagreement_flags: list
+    notes: str = ""
+
+
+@dataclass
+class PipelineState:
+    """Mutable state threaded through the Director."""
+    video_path: str
+    ingest: IngestResult | None = None
+    segment: SegmentResult | None = None
+    pose2d: Pose2DResult | None = None
+    body3d: Body3DResult | None = None
+    movement: MovementResult | None = None
+    features: BiomechFeatures | None = None
+    stgcn_score: ScoreResult | None = None
+    retrieval: RetrievalResult | None = None
+    judge: JudgeResult | None = None
+    report: ReportResult | None = None
+    errors: list = field(default_factory=list)
+    warnings: list = field(default_factory=list)

formscout/ui/theme.py

@@ -0,0 +1,250 @@
+"""
+FormScout custom Gradio theme — scout/trail inspired.
+Earth tones, topographic accents, sturdy typography.
+"""
+from __future__ import annotations
+
+import gradio as gr
+
+
+def formscout_theme() -> gr.Theme:
+    """Create the FormScout scout/trail theme."""
+    return gr.themes.Soft(
+        primary_hue=gr.themes.colors.emerald,
+        secondary_hue=gr.themes.colors.amber,
+        neutral_hue=gr.themes.colors.stone,
+        font=[
+            gr.themes.GoogleFont("Inter"),
+            "ui-sans-serif",
+            "system-ui",
+            "sans-serif",
+        ],
+        font_mono=[
+            gr.themes.GoogleFont("JetBrains Mono"),
+            "ui-monospace",
+            "monospace",
+        ],
+    ).set(
+        # Background
+        body_background_fill="linear-gradient(135deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%)",
+        body_background_fill_dark="linear-gradient(135deg, #0d1117 0%, #161b22 50%, #1a2332 100%)",
+        # Blocks
+        block_background_fill="rgba(30, 41, 59, 0.85)",
+        block_background_fill_dark="rgba(15, 23, 42, 0.9)",
+        block_border_width="1px",
+        block_border_color="rgba(100, 200, 150, 0.2)",
+        block_shadow="0 4px 20px rgba(0, 0, 0, 0.3)",
+        block_radius="12px",
+        # Buttons
+        button_primary_background_fill="linear-gradient(135deg, #059669 0%, #047857 100%)",
+        button_primary_background_fill_hover="linear-gradient(135deg, #047857 0%, #065f46 100%)",
+        button_primary_text_color="white",
+        button_primary_border_color="rgba(5, 150, 105, 0.5)",
+        button_secondary_background_fill="rgba(51, 65, 85, 0.8)",
+        button_secondary_text_color="#e2e8f0",
+        # Input
+        input_background_fill="rgba(15, 23, 42, 0.8)",
+        input_background_fill_dark="rgba(10, 15, 30, 0.9)",
+        input_border_color="rgba(100, 200, 150, 0.3)",
+        input_border_color_focus="rgba(5, 150, 105, 0.8)",
+        # Text
+        body_text_color="#e2e8f0",
+        body_text_color_dark="#f1f5f9",
+        block_title_text_color="#86efac",
+        block_label_text_color="#94a3b8",
+        # Spacing
+        block_padding="16px",
+        layout_gap="16px",
+    )
+
+
+FORMSCOUT_CSS = """
+/* FormScout Scout/Trail Theme CSS */
+
+.gradio-container {
+    max-width: 1400px !important;
+    margin: 0 auto;
+}
+
+/* Header styling */
+.formscout-header {
+    text-align: center;
+    padding: 20px 0;
+    border-bottom: 2px solid rgba(100, 200, 150, 0.3);
+    margin-bottom: 20px;
+}
+
+.formscout-header h1 {
+    font-size: 2.2em;
+    background: linear-gradient(135deg, #86efac, #059669);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    margin-bottom: 8px;
+}
+
+/* Safety banner */
+.safety-banner {
+    background: linear-gradient(90deg, rgba(245, 158, 11, 0.15), rgba(245, 158, 11, 0.05));
+    border: 1px solid rgba(245, 158, 11, 0.4);
+    border-radius: 8px;
+    padding: 12px 16px;
+    margin: 12px 0;
+    font-size: 0.9em;
+    text-align: center;
+    color: #fbbf24;
+}
+
+/* Score display */
+.score-card {
+    background: rgba(5, 150, 105, 0.1);
+    border: 2px solid rgba(5, 150, 105, 0.4);
+    border-radius: 16px;
+    padding: 24px;
+    text-align: center;
+}
+
+.score-value {
+    font-size: 4em;
+    font-weight: 800;
+    background: linear-gradient(135deg, #86efac, #059669);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+}
+
+/* Confidence meter */
+.confidence-bar {
+    height: 8px;
+    border-radius: 4px;
+    background: rgba(100, 200, 150, 0.2);
+    overflow: hidden;
+    margin-top: 8px;
+}
+
+.confidence-fill {
+    height: 100%;
+    border-radius: 4px;
+    background: linear-gradient(90deg, #ef4444, #f59e0b, #059669);
+    transition: width 0.5s ease;
+}
+
+/* Pipeline steps indicator */
+.pipeline-steps {
+    display: flex;
+    gap: 4px;
+    align-items: center;
+    padding: 8px 0;
+}
+
+.pipeline-step {
+    flex: 1;
+    height: 4px;
+    border-radius: 2px;
+    background: rgba(100, 200, 150, 0.2);
+    transition: background 0.3s ease;
+}
+
+.pipeline-step.active {
+    background: #059669;
+}
+
+.pipeline-step.complete {
+    background: #86efac;
+}
+
+/* Asymmetry indicator */
+.asymmetry-bar {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 12px;
+    background: rgba(30, 41, 59, 0.6);
+    border-radius: 8px;
+    margin: 4px 0;
+}
+
+.asymmetry-label {
+    min-width: 60px;
+    font-size: 0.85em;
+    color: #94a3b8;
+}
+
+.asymmetry-track {
+    flex: 1;
+    height: 6px;
+    background: rgba(100, 200, 150, 0.1);
+    border-radius: 3px;
+    position: relative;
+}
+
+.asymmetry-marker {
+    position: absolute;
+    top: -3px;
+    width: 12px;
+    height: 12px;
+    border-radius: 50%;
+    background: #059669;
+    border: 2px solid #86efac;
+}
+
+/* Topographic pattern accent */
+.topo-accent {
+    background-image:
+        repeating-linear-gradient(
+            0deg,
+            transparent,
+            transparent 40px,
+            rgba(100, 200, 150, 0.03) 40px,
+            rgba(100, 200, 150, 0.03) 41px
+        ),
+        repeating-linear-gradient(
+            90deg,
+            transparent,
+            transparent 40px,
+            rgba(100, 200, 150, 0.02) 40px,
+            rgba(100, 200, 150, 0.02) 41px
+        );
+}
+
+/* Warning/error states */
+.needs-review {
+    border-color: rgba(245, 158, 11, 0.6) !important;
+    background: rgba(245, 158, 11, 0.05) !important;
+}
+
+.low-confidence {
+    opacity: 0.7;
+    border-style: dashed !important;
+}
+
+/* Rubric drawer */
+.rubric-item {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 6px 10px;
+    border-radius: 6px;
+    margin: 2px 0;
+}
+
+.rubric-met {
+    background: rgba(5, 150, 105, 0.1);
+    border-left: 3px solid #059669;
+}
+
+.rubric-unmet {
+    background: rgba(239, 68, 68, 0.1);
+    border-left: 3px solid #ef4444;
+}
+
+/* Responsive */
+@media (max-width: 768px) {
+    .gradio-container {
+        padding: 8px !important;
+    }
+    .score-value {
+        font-size: 3em;
+    }
+}
+"""

pyproject.toml

@@ -0,0 +1,11 @@
+[build-system]
+requires = ["setuptools>=68.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "formscout"
+version = "0.1.0"
+requires-python = ">=3.11"
+
+[tool.setuptools.packages.find]
+include = ["formscout*"]