CHANGELOG.md

# Session Changelog

## [2026-01-22] [Enhancement] [COMPLETED] UI Instructions - User-Focused Quick Start Guide

**Problem:** Default template instructions were developer-focused ("clone this space, modify code") and not helpful for end users.

**Solution:** Rewrote instructions to be concise and user-oriented:

**Before:**

- Generic numbered steps
- Talked about cloning/modifying code (irrelevant for end users)
- Long rambling disclaimer about sub-optimal setup

**After:**

- **Quick Start** section with bolded key actions
- **What happens** section explaining the workflow
- **Expectations** section managing user expectations about time and downloads
- Explicitly mentions JSON + HTML export formats

**Modified Files:**

- `app.py` (lines 910-927)

---

## [2026-01-22] [Refactor] [COMPLETED] Export Architecture - Canonical Data Model

**Problem:** HTML export called JSON export internally, wrote JSON to disk, read it back, then wrote HTML. This was:

- Inefficient (redundant disk I/O)
- Tightly coupled (HTML depended on JSON format)
- Error-prone (data structure mismatch)

**Solution:** Refactored to use canonical data model:

1. **`_build_export_data()`** - Single source of truth, builds canonical data structure
2. **`export_results_to_json()`** - Calls canonical builder, writes JSON
3. **`export_results_to_html()`** - Calls canonical builder, writes HTML

**Benefits:**

- No redundant processing (no disk I/O between exports)
- Loose coupling (exports are independent)
- Consistent data (both use identical source)
- Easier to extend (add CSV, PDF exports easily)

**Modified Files:**

- `app.py` (~200 lines refactored)

---

## [2026-01-21] [Bugfix] [COMPLETED] DataFrame Scroll Bug - Replaced with HTML Export

**Problem:** Gradio 6.2.0 DataFrame has critical scrolling bugs (virtualized scrolling from Gradio 3.43+):

- Spring-back to top when scrolling
- Random scroll positions
- Locked scrolling after window resize

**Attempted Solutions (all failed):**

- `max_height` parameter
- `row_count` parameter
- `interactive=False`
- Custom CSS overrides
- Downgrade to Gradio 3.x (numpy conflict)

**Solution:** Removed DataFrame entirely, replaced with:

1. **JSON Export** - Full data download
2. **HTML Export** - Interactive table with scrollable cells

**UI Changes:**

- Removed: `gr.DataFrame` component
- Added: `gr.File` components for JSON and HTML downloads
- Updated: All return statements in `run_and_submit_all()`

**Modified Files:**

- `app.py` (~50 lines modified)

---

## [2026-01-21] [Debug] [FAILED] Gradio DataFrame Scroll Bug - Multiple Attempted Fixes

**Problem:** Gradio 6.2.0 DataFrame has critical scrolling bugs due to virtualized scrolling introduced in Gradio 3.43+:

- Spring-back to top when scrolling
- Random scroll positions on click
- Locked scrolling after window resize

**Attempted Solutions (all failed):**

1. **`max_height` parameter** - No effect, virtualized scrolling still active
2. **`row_count` parameter** - No effect, display issues persisted
3. **`interactive=False`** - No effect, scrolling still broken
4. **Custom CSS overrides** - Attempted to override virtualized styles, no effect
5. **Downgrade to Gradio 3.x** - Failed due to numpy 1.x vs 2.x dependency conflict

**Root Cause Identified:**

- Virtualized scrolling in Gradio 3.43+ fundamentally breaks DataFrame display
- No workarounds available in Gradio 6.2.0
- Downgrade blocked by dependency constraints

**Resolution:** Abandoned DataFrame UI, replaced with export buttons (see next entry)

**Status:** FAILED - UI bug unfixable, switched to alternative solution

**Modified Files:**

- `app.py` (multiple attempted fixes, all reverted)

---

## [2026-01-21] [Documentation] [COMPLETED] ACHIEVEMENT.md - Project Success Report

**Problem:** Need professional marketing/stakeholder report showcasing GAIA agent engineering journey and achievements.

**Solution:** Created comprehensive achievement report focusing on strategic engineering decisions and architectural choices.

**Report Structure:**

1. **Executive Summary** - Design-first approach (10 days planning + 4 days implementation), key achievements
2. **Strategic Engineering Decisions** - 7 major decisions documented:
   - Decision 1: Design-First Approach (8-Level Framework)
   - Decision 2: Tech Stack Selection (LangGraph, Gradio, model selection criteria)
   - Decision 3: Free-Tier-First Cost Architecture (4-tier LLM fallback)
   - Decision 4: UI-Driven Runtime Configuration
   - Decision 5: Unified Fallback Pattern Architecture
   - Decision 6: Evidence-Based State Design
   - Decision 7: Dynamic Planning via LLM
3. **Implementation Journey** - 6 stages with architectural decisions per stage
4. **Performance Progression Timeline** - 10% → 25% → 30% accuracy progression
5. **Production Readiness Highlights** - Deployment, cost optimization, resilience engineering
6. **Quantifiable Impact Summary** - Metrics table with 10 key achievements
7. **Key Learnings & Takeaways** - 6 strategic insights
8. **Conclusion** - Final stats and repository link

**Tech Stack Details Added:**

- **LLM Chain:** Gemini 2.0 Flash Exp → GPT-OSS 120B (HF) → GPT-OSS 120B (Groq) → Claude Sonnet 4.5
- **Vision:** Gemma-3-27B (HF) → Gemini 2.0 Flash → Claude Sonnet 4.5
- **Search:** Tavily → Exa
- **Audio:** Whisper Small with ZeroGPU
- **Frameworks:** LangGraph (not LangChain), Gradio (not Streamlit), uv (not pip/poetry)

**Focus:** Strategic WHY (engineering decisions) over technical WHAT (bug fixes), emphasizing architectural thinking and product design.

**Modified Files:**

- **ACHIEVEMENT.md** (401 lines created) - Complete marketing report with executive summary, strategic decisions, implementation journey, metrics

**Result:** Professional achievement report ready for employers, recruiters, investors, and blog/social media sharing.

---

## [2026-01-14] [Enhancement] [COMPLETED] Unified Log Format - Markdown Standard

**Problem:** Inconsistent log formats across different components, wasteful `====` separators.

**Solution:** Standardize all logs to Markdown format with clean structure.

**Unified Log Standard:**

```markdown
# Title

**Key:** value
**Key:** value

## Section

Content
```

**Files Updated:**

1. **LLM Session Logs** (`llm_session_*.md`):
   - Header: `# LLM Synthesis Session Log`
   - Questions: `## Question [timestamp]`
   - Sections: `### Evidence & Prompt`, `### LLM Response`
   - Code blocks: triple backticks

2. **YouTube Transcript Logs** (`{video_id}_transcript.md`):
   - Header: `# YouTube Transcript`
   - Metadata: `**Video ID:**`, `**Source:**`, `**Length:**`
   - Content: `## Transcript`

**Note:** No horizontal rules (`---`) - already banned in global CLAUDE.md, breaks collapsible sections

**Token Savings:**

| Style             | Tokens per separator | 20 questions |
| ----------------- | -------------------- | ------------ |
| `====` x 80 chars | ~40 tokens           | ~800 tokens  |
| `##` heading      | ~2 tokens            | ~40 tokens   |

**Savings:** ~760 tokens per session (95% reduction)

**Benefits:**

- ✅ Collapsible headings in all Markdown editors
- ✅ Consistent structure across all log files
- ✅ Token-efficient for LLM processing
- ✅ Readable in both rendered and plain text
- ✅ `.md` extension for proper syntax highlighting

**Modified Files:**

- `src/agent/llm_client.py` (LLM session logs)
- `src/tools/youtube.py` (transcript logs)
- `CLAUDE.md` (added unified log format standard)

## [2026-01-14] [Cleanup] [COMPLETED] Session Log Optimization - Reduce Static Content Redundancy

**Problem:** System prompt (~30 lines) was written for every question (20x = 600 lines of redundant text).

**Solution:** Write system prompt once on first question, skip for subsequent questions.

**Implementation:**

- Added `_SYSTEM_PROMPT_WRITTEN` flag to track if system prompt was logged
- First question includes full SYSTEM PROMPT section
- Subsequent questions only show dynamic content (question, evidence, response)

**Log format comparison:**

Before (every question):

```
QUESTION START
SYSTEM PROMPT: [30 lines repeated]
USER PROMPT: [dynamic]
LLM RESPONSE: [dynamic]
```

After (first question):

```
SYSTEM PROMPT (static - used for all questions): [30 lines]
QUESTION [...]
EVIDENCE & PROMPT: [dynamic]
LLM RESPONSE: [dynamic]
```

After (subsequent questions):

```
QUESTION [...]
EVIDENCE & PROMPT: [dynamic]
LLM RESPONSE: [dynamic]
```

**Result:** ~570 lines less redundancy per 20-question evaluation.

**Modified Files:**

- `src/agent/llm_client.py` (~30 lines modified - added flag, conditional logging)

## [2026-01-14] [Bugfix] [COMPLETED] Session Log Synchronization - Atomic Per-Question Logging

**Problem:** When processing multiple questions, LLM responses were written out of order relative to their questions, causing mismatched prompts/responses in session logs.

**Root Cause:** `synthesize_answer_hf()` wrote QUESTION START immediately, but appended LLM RESPONSE later after API call completed. With concurrent processing, responses finished in different order.

**Solution:** Buffer complete question block in memory, write atomically when response arrives:

```python
# Before (broken):
write_question_start()  # immediate
api_response = call_llm()
write_llm_response()     # later, out of order

# After (fixed):
question_header = buffer_question_start()
api_response = call_llm()
complete_block = question_header + response + end
write_atomic(complete_block)  # all at once
```

**Result:** Each question block is self-contained, no mismatched prompts/responses.

**Modified Files:**

- `src/agent/llm_client.py` (~40 lines modified - synthesize_answer_hf function)

## [2026-01-13] [Cleanup] [COMPLETED] LLM Session Log Format - Removed Duplicate Evidence

**Problem:** Evidence appeared twice in session log - once in USER PROMPT section, again in EVIDENCE ITEMS section.

**Solution:** Removed standalone EVIDENCE ITEMS section, kept evidence in USER PROMPT only.

**Rationale:** USER PROMPT shows what's actually sent to the LLM (system + user messages together).

**Modified Files:**

- `src/agent/llm_client.py` - Removed duplicate logging section (lines 1189-1194 deleted)

**Result:** Cleaner logs, no duplication

## [2026-01-13] [Feature] [COMPLETED] YouTube Frame Processing Mode - Visual Video Analysis

**Problem:** Transcript mode captures audio but misses visual information (objects, scenes, actions).

**Solution:** Implemented frame extraction and vision-based video analysis mode.

**Implementation:**

**1. Frame Extraction (`src/tools/youtube.py`):**

- `download_video()` - Downloads video using yt-dlp
- `extract_frames()` - Extracts N frames at regular intervals using OpenCV
- `analyze_frames()` - Analyzes frames with vision models
- `process_video_frames()` - Complete frame processing pipeline
- `youtube_analyze()` - Unified API with mode parameter

**2. CONFIG Settings:**

- `FRAME_COUNT = 6` - Number of frames to extract
- `FRAME_QUALITY = "worst"` - Download quality (faster)

**3. UI Integration (`app.py`):**

- Added radio button: "YouTube Processing Mode"
- Choices: "Transcript" (default) or "Frames"
- Sets `YOUTUBE_MODE` environment variable

**4. Updated Dependencies:**

- `requirements.txt` - Added `opencv-python>=4.8.0`
- `pyproject.toml` - Added via `uv add opencv-python`

**5. Tool Description Update (`src/tools/__init__.py`):**

- Updated `youtube_transcript` description to mention both modes

**Architecture:**

```
youtube_transcript() → reads YOUTUBE_MODE env
                        ├─ "transcript" → audio/subtitle extraction
                        └─ "frames" → video download → extract 6 frames → vision analysis
```

**Test Result:**

- Successfully processed video with 6 frames analyzed
- Each frame analyzed with vision model, combined output returned
- Frame timestamps: 0s, 20s, 40s, 60s, 80s, 100s (spread evenly)

**Known Limitation:**

- Frame sampling is random (regular intervals)
- Low probability of capturing transient events (~5.5% for 108s video)
- Future: Hybrid mode using timestamps to guide frame extraction (documented in `user_io/knowledge/hybrid_video_audio_analysis.md`)

**Status:** Implemented and tested, ready for use

**Modified Files:**

- `src/tools/youtube.py` (~200 lines added - frame extraction + analysis)
- `app.py` (~5 lines modified - UI toggle)
- `requirements.txt` (1 line added - opencv-python)
- `src/tools/__init__.py` (1 line modified - tool description)

## [2026-01-13] [Investigation] [OPEN] HF Spaces vs Local Performance Discrepancy

**Problem:** HF Space deployment shows significantly lower scores (5%) than local execution (20-30%).

**Investigation:**

| Environment      | Score  | System Errors | NoneType Errors |
| ---------------- | ------ | ------------- | --------------- |
| **Local**        | 20-30% | 3 (15%)       | 1               |
| **HF ZeroGPU**   | 5%     | 5 (25%)       | 3               |
| **HF CPU Basic** | 5%     | 5 (25%)       | 3               |

**Verified:** Code is 100% identical (cloned HF Space repo, git history matches at commit `3dcf523`).

**Issue:** HF Spaces infrastructure causes LLM to return empty/None responses during synthesis.

**Known Limitations (Local 30% Run):**

- 3 system errors: reverse text (calculator), chess vision (NoneType), Python .py execution
- 10 "Unable to answer": search evidence extraction issues
- 1 wrong answer: Wikipedia dinosaur (Jimfbleak vs FunkMonk)

**Resolution:** Competition accepts local results. HF Spaces deployment not required.

**Status:** OPEN - Infrastructure Issue, Won't Fix (use local execution)

## [2026-01-13] [Infrastructure] [COMPLETED] 3-Tier Folder Naming Convention

**Problem:** Previous rename used `_` prefix for both runtime folders AND user-only folders, creating ambiguity.

**Solution:** Implemented 3-tier naming convention to clearly distinguish folder purposes.

**3-Tier Convention:**

1. **User-only** (`user_*` prefix) - Manual use, not app runtime:
   - `user_input/` - User testing files, not app input
   - `user_output/` - User downloads, not app output
   - `user_dev/` - Dev records (manual documentation)
   - `user_archive/` - Archived code/reference materials

2. **Runtime/Internal** (`_` prefix) - App creates, temporary:
   - `_cache/` - Runtime cache, served via app download
   - `_log/` - Runtime logs, debugging

3. **Application** (no prefix) - Permanent code:
   - `src/`, `test/`, `docs/`, `ref/` - Application folders

**Folders Renamed:**

- `_input/` → `user_input/` (user testing files)
- `_output/` → `user_output/` (user downloads)
- `dev/` → `user_dev/` (dev records)
- `archive/` → `user_archive/` (archived materials)

**Folders Unchanged (correct tier):**

- `_cache/`, `_log/` - Runtime ✓
- `src/`, `test/`, `docs/`, `ref/` - Application ✓

**Updated Files:**

- **test/test_phase0_hf_vision_api.py** - `Path("_output")` → `Path("user_output")`
- **.gitignore** - Updated folder references and comments

**Git Status:**

- Old folders removed from git tracking
- New folders excluded by .gitignore
- Existing files become untracked

**Result:** Clear 3-tier structure: user*\*, *\*, and no prefix

## [2026-01-13] [Infrastructure] [COMPLETED] Runtime Folder Naming Convention - Underscore Prefix

**Problem:** Folders `log/`, `output/`, and `input/` didn't clearly indicate they were runtime-only storage, making it unclear which folders are internal vs permanent.

**Solution:** Renamed all runtime-only folders to use `_` prefix, following Python convention for internal/private.

**Folders Renamed:**

- `log/` → `_log/` (runtime logs, debugging)
- `output/` → `_output/` (runtime results, user downloads)
- `input/` → `_input/` (user testing files, not app input)

**Rationale:**

- `_` prefix signals "internal, temporary, not part of public API"
- Consistent with Python convention (`_private`, `__dunder__`)
- Distinguishes runtime storage from permanent project folders

**Updated Files:**

- `src/agent/llm_client.py` - `Path("log")` → `Path("_log")`
- `src/tools/youtube.py` - `Path("log")` → `Path("_log")`
- `test/test_phase0_hf_vision_api.py` - `Path("output")` → `Path("_output")`
- `.gitignore` - Updated folder references

**Result:** Runtime folders now clearly marked with `_` prefix

## [2026-01-13] [Documentation] [COMPLETED] Log Consolidation - Session-Level Logging

**Problem:** Each question created separate log file (`llm_context_TIMESTAMP.txt`), polluting the log/ folder with 20+ files per evaluation.

**Solution:** Implemented session-level log file where all questions append to single file.

**Implementation:**

- Added `get_session_log_file()` function in `src/agent/llm_client.py`
- Creates `log/llm_session_YYYYMMDD_HHMMSS.txt` on first use
- All questions append to same file with question delimiters
- Added `reset_session_log()` for testing/new runs

**Updated File:**

- `src/agent/llm_client.py` (~40 lines added)
  - Session log management (lines 62-99)
  - Updated `synthesize_answer_hf` to append to session log

**Result:** One log file per evaluation instead of 20+

## [2026-01-13] [Infrastructure] [COMPLETED] Project Template Reference Move

**Problem:** Project template moved to new location, documentation references outdated.

**Solution:** Updated CHANGELOG.md references to new template location.

**Changes:**

- Moved: `project_template_original/` → `ref/project_template_original/`
- Updated CHANGELOG.md (7 occurrences)
- Added `ref/` to .gitignore (static copies, not in git)

**Result:** Documentation reflects new template location

## [2026-01-12] [Infrastructure] [COMPLETED] Git Ignore Fixes - PDF Commit Block

**Problem:** Git push rejected due to binary files in `docs/` folder.

**Solution:**

1. Reset commit: `git reset --soft HEAD~1`
2. Added `docs/*.pdf` to .gitignore
3. Removed PDF files from git: `git rm --cached "docs/*.pdf"`
4. Recommitted without PDFs
5. Push successful

**User feedback:** "can just gitignore all the docs also"

**Final Fix:** Changed `docs/*.pdf` to `docs/` to ignore entire docs folder

**Updated Files:**

- `.gitignore` - Added `docs/` folder ignore

**Result:** Clean git history, no binary files committed

## [2026-01-13] [Documentation] [COMPLETED] 30% Results Analysis - Phase 1 Success

**Problem:** Need to analyze results to understand what's working and what needs improvement.

**Analysis of gaia_results_20260113_174815.json (30% score):**

**Results Breakdown:**

- **6 Correct** (30%):
  - `a1e91b78` (YouTube bird count) - Phase 1 fix working ✓
  - `9d191bce` (YouTube Teal'c) - Phase 1 fix working ✓
  - `6f37996b` (CSV table) - Calculator working ✓
  - `1f975693` (Calculus MP3) - Audio transcription working ✓
  - `99c9cc74` (Strawberry pie MP3) - Audio transcription working ✓
  - `7bd855d8` (Excel food sales) - File parsing working ✓

- **3 System Errors** (15%):
  - `2d83110e` (Reverse text) - Calculator: SyntaxError
  - `cca530fc` (Chess position) - NoneType error (vision)
  - `f918266a` (Python code) - parse_file: ValueError

- **10 "Unable to answer"** (50%):
  - Search evidence extraction insufficient
  - Need better LLM prompts or search processing

- **1 Wrong Answer** (5%):
  - `4fc2f1ae` (Wikipedia dinosaur) - Found "Jimfbleak" instead of "FunkMonk"

**Phase 1 Impact (YouTube + Audio):**

- Fixed 4 questions that would have failed before
- YouTube transcription with Whisper fallback working
- Audio transcription working well

**Next Steps:**

1. Fix 3 system errors (text manipulation, vision NoneType, Python execution)
2. Improve search evidence extraction (10 questions)
3. Investigate wrong answer (Wikipedia search precision)

## [2026-01-13] [Feature] [COMPLETED] Phase 1: YouTube + Audio Transcription Support

**Problem:** Questions with YouTube videos and audio files couldn't be answered.

**Solution:** Implemented two-phase transcription system.

**YouTube Transcription (`src/tools/youtube.py`):**

- Extracts transcript using `youtube_transcript_api`
- Falls back to Whisper audio transcription if captions unavailable
- Saves transcript to `_log/{video_id}_transcript.txt`

**Audio Transcription (`src/tools/audio.py`):**

- Uses Groq's Whisper-large-v3 model (ZeroGPU compatible)
- Supports MP3, WAV, M4A, OGG, FLAC, AAC formats
- Saves transcript to `_log/` for debugging

**Impact:**

- 4 additional questions answered correctly (30% vs ~10% before)
- `9d191bce` (YouTube Teal'c) - "Extremely" ✓
- `a1e91b78` (YouTube birds) - "3" ✓
- `1f975693` (Calculus MP3) - "132, 133, 134, 197, 245" ✓
- `99c9cc74` (Strawberry pie MP3) - Full ingredient list ✓

**Status:** Phase 1 complete, hit 30% target score

## [2026-01-12] [Infrastructure] [COMPLETED] Session Log Implementation

**Problem:** Need to track LLM synthesis context for debugging and analysis.

**Solution:** Created session-level logging system in `src/agent/llm_client.py`.

**Implementation:**

- Session log: `_log/llm_session_YYYYMMDD_HHMMSS.txt`
- Per-question log: `_log/{video_id}_transcript.txt` (YouTube only)
- Captures: questions, evidence items, LLM prompts, answers
- Structured format with timestamps and delimiters

**Result:** Full audit trail for debugging failed questions

## [2026-01-13] [Infrastructure] [COMPLETED] Git Commit & HF Push

**Problem:** Need to deploy changes to HuggingFace Spaces.

**Solution:** Committed and pushed latest changes.

**Commit:** `3dcf523` - "refactor: update folder structure and adjust output paths"

**Changes Deployed:**

- 3-tier folder naming convention
- Session-level logging
- Project template reference move
- Git ignore fixes

**Result:** HF Space updated with latest code

## [2026-01-13] [Testing] [COMPLETED] Phase 0 Vision API Validation

**Problem:** Need to validate vision API works before integrating into agent.

**Solution:** Created test suite `test/test_phase0_hf_vision_api.py`.

**Test Results:**

- Tested 4 image sources
- Validated multimodal LLM responses
- Confirmed HF Inference API compatibility
- Identified NoneType edge case (empty responses)

**File:** `user_io/result_ServerApp/phase0_vision_validation_*.json`

**Result:** Vision API validated, ready for integration

## [2026-01-11] [Feature] [COMPLETED] Multi-Modal Vision Support

**Problem:** Agent couldn't process image-based questions (chess positions, charts, etc.).

**Solution:** Implemented vision tool using HuggingFace Inference API.

**Implementation (`src/tools/vision.py`):**

- `analyze_image()` - Main vision analysis function
- Supports JPEG, PNG, GIF, BMP, WebP formats
- Returns detailed descriptions of visual content
- Fallback to Gemini/Claude if HF fails

**Status:** Implemented, some NoneType errors remain

## [2026-01-10] [Feature] [COMPLETED] File Parser Tool

**Problem:** Agent couldn't read uploaded files (PDF, Excel, Word, CSV, etc.).

**Solution:** Implemented unified file parser (`src/tools/file_parser.py`).

**Supported Formats:**

- PDF (`parse_pdf`) - PyPDF2 extraction
- Excel (`parse_excel`) - Calamine-based parsing
- Word (`parse_word`) - python-docx extraction
- Text/CSV (`parse_text`) - UTF-8 text reading
- Unified `parse_file()` - Auto-detects format

**Result:** Agent can now read file attachments

## [2026-01-09] [Feature] [COMPLETED] Calculator Tool

**Problem:** Agent couldn't perform mathematical calculations.

**Solution:** Implemented safe expression evaluator (`src/tools/calculator.py`).

**Features:**

- `safe_eval()` - Safe math expression evaluation
- Supports: arithmetic, algebra, trigonometry, logarithms
- Constants: pi, e
- Functions: sqrt, sin, cos, log, abs, etc.
- Error handling for invalid expressions

**Result:** CSV table question answered correctly (`6f37996b`)

## [2026-01-08] [Feature] [COMPLETED] Web Search Tool

**Problem:** Agent couldn't access current information beyond training data.

**Solution:** Implemented web search using Tavily API (`src/tools/web_search.py`).

**Features:**

- `tavily_search()` - Primary search via Tavily
- `exa_search()` - Fallback via Exa (if available)
- Unified `search()` - Auto-fallback chain
- Returns structured results with titles, snippets, URLs

**Configuration:**

- `TAVILY_API_KEY` required
- `EXA_API_KEY` optional (fallback)

**Result:** Agent can now search web for current information

## [2026-01-07] [Infrastructure] [COMPLETED] Project Initialization

**Problem:** New project setup required.

**Solution:** Initialized project structure with standard files.

**Created:**

- `README.md` - Project documentation
- `CLAUDE.md` - Project-specific AI instructions
- `CHANGELOG.md` - Session tracking
- `.gitignore` - Git exclusions
- `requirements.txt` - Dependencies
- `pyproject.toml` - UV package config

**Result:** Project scaffold ready for development

**Date:** YYYY-MM-DD
**Dev Record:** [link to dev/dev_YYMMDD_##_concise_title.md]

## What Was Changed

- Change 1
- Change 2