HONEST_SUBMISSION.md

Honest Submission Notes — TurboSkillSlug

This document is the unvarnished account of what TurboSkillSlug actually does, what is genuinely strong, what is partial, and what is aspirational. If a claim is not in here with its caveats, treat the marketing copy as marketing.

Demo video: https://youtu.be/qSP9olWRv7o Space: https://huggingface.co/spaces/build-small-hackathon/TurboSkillSlug Code: https://github.com/AnubhavBharadwaaj/turbo-skill-slug

What it does, honestly

You give the slug a build session two ways: narrate it aloud (audio), or drop a Claude Code / Codex CLI session trace (.jsonl). It transcribes/parses, extracts a structured record, and returns four artifacts: a transferable SKILL.md, a grounded second-person recap in a fine-tuned "slug voice," a procedural SVG shell, and a thermal receipt. The shell is born on screen as a scroll that unrolls along its spiral arm, with a byōbu (folding-screen) battle drawn across it.

What is genuinely strong

• Two custom 1.5B LoRA adapters, both published and both used. Voice (500 pairs, loss 0.89) and extraction (167 pairs, loss 0.81). The extraction LoRA replaced a prompted Qwen-7B, bringing the primary pipeline to ~2.6B active params (Whisper 809M + one 1.5B base serving both adapters on a single T4).
• The groundedness eval is real and published with its raw data. On 25 held-out transcripts the fine-tuned 1.5B reaches semantic groundedness 0.76 vs the 7B's 0.72, at a third the active size, by paraphrasing rather than copying.
• The shell is fully procedural and traceable. Every visual element derives from a real session feature: dead ends are knots/fallen warriors, gotchas are jewels/archers, the breakthrough is the aperture/dragon, sentiment is the color arc. No image generation.
• The SKILL.md is built for real uplift, gotchas-first (symptom→cause→fix), "what does NOT work and why," transferable principles. Built and de-leaked deterministically from the structured extraction, not trusted to model prose.
• Trace input works. A real Claude Code or Codex session log runs through the same pipeline as audio. Judges can feed their own logs.
• Graceful degradation is real and now observable. If the extract adapter emits invalid JSON, the 7B fallback catches it. If the voice adapter is down, a deterministic extraction-based voice net prevents placeholder recaps. Every voice-path outcome is logged with a [VOICE] tag (EXTRACT_DOWN / VOICE_DOWN / VOICE_EMPTY / VOICE_OK / FALLBACK_VOICE_OK / NET_FROM_EXTRACTION / …) so failures are diagnosable, not mysterious.

What is partial or has real caveats

• Extraction parse reliability: 21/25 vs the 7B's 24/25. The smaller model produces invalid JSON more often. In the app a brace-walking parser and field validators recover most of this, and the 7B fallback catches the rest, but the raw rate is a real cost of going small, and we report it unsoftened. (You will see [VOICE] EXTRACT_DOWN in the logs when this fires; the fallback then runs.)
• The groundedness metric is calibrated to 5/6, not 6/6. A hand-labeled calibration block (run before scoring, printed in the logs) missed one case: a grounded paraphrase scored just under threshold. The miss understates the LoRA rather than inflating it. 25 transcripts is a small sample; treat the LoRA-vs-7B gap as "matches or slightly exceeds," not "beats."
• SKILL.md quality depends on extraction quality. A good session yields transferable gotchas; a thin session yields thinner ones. An optional one-shot enrichment pass expands terse gotchas, gated by env and best-effort. We caught and fixed a real bug where few-shot examples in the enrichment prompt leaked into output (a game-theory skill got tree-coloring gotchas); the prompts are de-leaked and a guard now rejects any leaked phrasing.
• The scroll-unroll animation ships ~1.7MB per shell (14 stacked growth stages, so the parchment lays down ALONG the spiral arm rather than as a radial wipe). Browser-rendered via SMIL in a sandboxed iframe, with a "watch it unroll again" replay. On a slow connection the first paint takes a beat. If SMIL fails, the full shell still shows (nothing is hard-hidden); animation is enhancement, not a dependency.
• The 3D paper curl rides the spiral tip via animateMotion. Verified frame-by-frame in offline renders (cairosvg + headless Chromium). Exact tip-tracking in every browser is the part most likely to need a small tweak.

What was freshly added near the deadline (use with that in mind)

• Shared terrarium gallery. Kept shells save to a Modal Volume; a grid shows all of them newest-first; each has a complete clickable permalink that re-loads and re-animates that shell. Freshly built and lightly tested. Honest caveats: each grid card is its own iframe, so past ~30-40 shells the grid gets heavy (lazy-loading is the fix, not yet done). Gallery saves depend on the Modal endpoint URLs matching the client; if they drift, a save fails and the UI says so rather than failing silently.
• Battle Trace (experimental). A temporal replay of the same session as a war between you (the Agent) and the Environment, fed by the SAME extraction (no second parser, no OTel). Framed as complementary to the shell: the shell is the slug's memory of how the battle ended (the frozen folding screen); the trace is the replay of it in time. It is Canvas-2D with simple figurative combatants (a samurai general, a horned adversary, fallen warriors, a dragon at the breakthrough). Honest about its level: these are clean vector figures, not game-quality sprite art. Labeled "experimental" in the UI.

What is aspirational / not built

• Session diff view (compare two sessions' shells) is not built.
• Closing the parse-rate gap needs more training pairs and constrained decoding; not done.
• Higher-fidelity Battle Trace art (detailed sprite-grade samurai) is not built; the current figures are deliberately simple vector shapes (see the Battle Trace note above).

Infrastructure honesty

• The Qwen-7B still exists in the codebase but ONLY as a labeled fallback when the Modal extract path returns nothing usable (including the invalid-JSON case above). The primary path is the 1.5B dual adapter. The README architecture table reflects this.
• The dual-adapter server can hold one warm container for demo reliability (~$0.60/hr). It should be stopped after judging (modal app stop slug-dual-serve).

How to verify our claims

• Groundedness: modal run semantic_eval.py reproduces the table; raw generations are published in the eval dataset so anyone can re-score.
• Trace input: drop the sample .jsonl (one click) or your own session log.
• Models: both LoRA adapters are public on the Hub.
• Shell traceability: change a session's sentiment or dead-end count and watch the shell's colors and knots change accordingly.
• Failure handling: the Space logs tag every voice-path outcome with [VOICE].

The one-sentence honest summary

A small, slow, genuinely ~2.6B pipeline that turns a coding session into a transferable skill, a grounded recap, and a procedural shell, measured honestly (including where the small model costs us), with graceful, observable degradation, and every model and the eval data published for anyone to check.