# .claude/data/learnings/errors.yaml
category: errors
last_updated: "2025-11-25T12:00:00Z"
learnings:
  - id: "err-001"
    created: "2025-11-25T12:00:00Z"
    keywords:
      - "import"
      - "module not found"
      - "circular dependency"
    summary: "Circular imports cause 'module not found' errors"
    insight: |
      When module A imports from module B and module B imports from module A,
      Python raises ImportError. Solution: Move shared code to a third module
      or use lazy imports.
    example: |
      # Bad: circular import
      # utils.py imports from models.py
      # models.py imports from utils.py

      # Good: extract shared code
      # shared.py has common functions
      # both utils.py and models.py import from shared.py
    confidence: 0.9
    times_used: 3

## Past Learnings Relevant to This Task

### [Category]: [Summary]

## [Insight with example if helpful]

Session: Debugging circular import issue in Neo4j module
Duration: 45 minutes
Resolution: Moved shared types to separate file

Extracted Learning:
- Category: errors
- Keywords: [import, circular, neo4j, type]
- Summary: Circular imports in Neo4j types cause ImportError
- Insight: When Neo4jNode imports from connection.py which imports
  Node types, move types to separate types.py module
- Example: types.py with dataclasses, connection.py imports from types.py

Session Start Prompt: "Fix the import error in the memory module"

Matched Learnings:
1. errors/err-001: "Circular imports cause 'module not found' errors" (85% match)
2. debugging/dbg-003: "Use `python -c` to isolate import issues" (60% match)

Injected Context:
## Past Learnings Relevant to This Task

### Errors: Circular imports cause 'module not found' errors
When module A imports from module B and B imports from A, Python raises
ImportError. Solution: Move shared code to a third module or use lazy imports.
---

User: Show me what I've learned about testing

Claude (using this skill):
1. Reads .claude/data/learnings/workflows.yaml
2. Filters learnings with keywords containing "test"
3. Displays formatted list with summaries and examples

def calculate_relevance(task_keywords: set, learning_keywords: set) -> float:
    """Calculate relevance score between 0 and 1."""
    if not task_keywords or not learning_keywords:
        return 0.0

    # Count overlapping keywords
    overlap = task_keywords & learning_keywords

    # Score: overlap / min(task, learning) to not penalize short queries
    return len(overlap) / min(len(task_keywords), len(learning_keywords))

.claude/
  data/
    learnings/
      errors.yaml        # Error patterns and solutions
      workflows.yaml     # Workflow insights
      tools.yaml         # Tool usage patterns
      architecture.yaml  # Design decisions
      debugging.yaml     # Debugging strategies
      _stats.yaml        # Usage statistics (auto-generated)

import yaml
from pathlib import Path

def safe_load_learnings(filepath: Path) -> dict:
    """Load learnings with graceful error handling."""
    try:
        content = filepath.read_text()
        data = yaml.safe_load(content)
        if not isinstance(data, dict) or "learnings" not in data:
            print(f"Warning: Invalid structure in {filepath}, using empty learnings")
            return {"category": filepath.stem, "learnings": []}
        return data
    except yaml.YAMLError as e:
        print(f"Warning: YAML error in {filepath}: {e}")
        # Create backup before recovery
        backup = filepath.with_suffix(".yaml.bak")
        filepath.rename(backup)
        print(f"Backed up corrupted file to {backup}")
        return {"category": filepath.stem, "learnings": []}
    except Exception as e:
        print(f"Warning: Could not read {filepath}: {e}")
        return {"category": filepath.stem, "learnings": []}

def ensure_learnings_directory():
    """Create learnings directory and empty files if missing."""
    learnings_dir = Path(".claude/data/learnings")
    learnings_dir.mkdir(parents=True, exist_ok=True)

    categories = ["errors", "workflows", "tools", "architecture", "debugging"]
    for cat in categories:
        filepath = learnings_dir / f"{cat}.yaml"
        if not filepath.exists():
            filepath.write_text(f"category: {cat}\nlearnings: []\n")

# .claude/tools/amplihack/hooks/stop_hook.py

async def extract_session_learnings(transcript: str, session_id: str):
    """Extract learnings from session transcript at stop."""
    from pathlib import Path
    import yaml
    from datetime import datetime

    # Only extract if session was substantive (not just a quick question)
    if len(transcript) < 1000:
        return

    # Use Claude to extract insights (simplified example)
    extraction_prompt = f"""
    Analyze this session transcript and extract any reusable learnings.

    Categories:
    - errors: Error patterns and solutions
    - workflows: Process improvements
    - tools: Tool usage insights
    - architecture: Design decisions
    - debugging: Debug strategies

    For each learning, provide:
    - category (one of the above)
    - keywords (3-5 searchable terms)
    - summary (one sentence)
    - insight (detailed explanation)
    - example (code if applicable)
    - confidence (0.5-1.0)

    Transcript:
    {transcript[:5000]}  # Truncate for token limits
    """

    # ... call Claude to extract ...
    # ... parse response and add to appropriate YAML files ...

def on_stop(session_data: dict):
    """Stop hook entry point."""
    # ... other stop hook logic ...

    # Extract learnings (non-blocking)
    try:
        import asyncio
        asyncio.create_task(
            extract_session_learnings(
                session_data.get("transcript", ""),
                session_data.get("session_id", "")
            )
        )
    except Exception as e:
        print(f"Learning extraction failed (non-blocking): {e}")

# .claude/tools/amplihack/hooks/session_start_hook.py

def inject_relevant_learnings(initial_prompt: str) -> str:
    """Find and format relevant learnings for injection."""
    from pathlib import Path
    import yaml

    learnings_dir = Path(".claude/data/learnings")
    if not learnings_dir.exists():
        return ""

    # Extract keywords from prompt
    prompt_lower = initial_prompt.lower()
    task_keywords = set()
    for word in prompt_lower.split():
        if len(word) > 3:  # Skip short words
            task_keywords.add(word.strip(".,!?"))

    # Find matching learnings
    matches = []
    for yaml_file in learnings_dir.glob("*.yaml"):
        if yaml_file.name.startswith("_"):
            continue  # Skip _stats.yaml

        try:
            data = yaml.safe_load(yaml_file.read_text())
            for learning in data.get("learnings", []):
                learning_keywords = set(k.lower() for k in learning.get("keywords", []))
                overlap = task_keywords & learning_keywords
                if overlap:
                    score = len(overlap) * learning.get("confidence", 0.5)
                    matches.append((score, learning))
        except Exception:
            continue

    # Return top 3 matches
    matches.sort(key=lambda x: x[0], reverse=True)
    if not matches:
        return ""

    context = "## Past Learnings Relevant to This Task\n\n"
    for score, learning in matches[:3]:
        context += f"### {learning.get('summary', 'Insight')}\n"
        context += f"{learning.get('insight', '')}\n\n"

    return context

def on_session_start(session_data: dict) -> dict:
    """Session start hook entry point."""
    initial_prompt = session_data.get("prompt", "")

    # Inject relevant learnings
    try:
        learning_context = inject_relevant_learnings(initial_prompt)
        if learning_context:
            session_data["injected_context"] = learning_context
    except Exception as e:
        print(f"Learning injection failed (non-blocking): {e}")

    return session_data