AI_template/.claude/skills/continuous-learning/SKILL.md

name, description, disable-model-invocation

name	description	disable-model-invocation
continuous-learning	Extract patterns and instincts from the current session — what worked, what didn't, what should be remembered. Produces actionable learnings that improve future sessions.	true

Continuous Learning

Extract learnings from the current session to improve future work.

Context

Recent git history: !git log --oneline -10 2>/dev/null || echo "No git history"

Project rules: !head -30 RULES.md 2>/dev/null || echo "No RULES.md"

Steps

1. Review the session

Analyze what happened in this session:

• Tasks completed: What was accomplished?
• Approaches tried: What methods were used?
• Errors encountered: What went wrong and how was it fixed?
• User corrections: Where did the user redirect or correct the approach?
• Surprises: What was unexpected about the codebase or requirements?

2. Extract instincts

An instinct is a pattern that should influence future behavior. Categories:

Project-specific instincts

Patterns unique to this codebase:

• Code conventions not captured in RULES.md
• Hidden dependencies between modules
• "Gotchas" that aren't obvious from reading the code
• User preferences for how work should be done

Approach instincts

What worked or didn't work:

• Tools/agents that were effective for specific tasks
• Sequences of actions that solved problems efficiently
• Dead ends that should be avoided next time

Communication instincts

How the user prefers to interact:

• Level of detail they want in explanations
• Whether they prefer to be asked or just shown solutions
• How they respond to different types of suggestions

3. Classify each instinct

For each instinct, determine:

Field	Description
Pattern	What was observed (specific, not vague)
Context	When this applies (file types, tasks, situations)
Action	What to do differently next time
Confidence	How certain (low/medium/high) based on how many times observed
Scope	Project-only or applicable to all projects

4. Decide where to store

Based on scope and type:

Learning Type	Store In
User preference	Memory (type: feedback)
Project convention	RULES.md or RECOMMENDATIONS.md
Codebase gotcha	Code comment or RECOMMENDATIONS.md
Tool/agent effectiveness	Memory (type: feedback)
Communication preference	Memory (type: user or feedback)
Temporary state	Don't store — it's ephemeral

5. Output

# Session Learnings

## Summary
[1-2 sentences: what was accomplished, overall assessment]

## Instincts Extracted

### 1. [Pattern name]
- **Observed**: [what happened]
- **Context**: [when this applies]
- **Action**: [what to do next time]
- **Confidence**: [low/medium/high]
- **Store in**: [memory type / RULES.md / RECOMMENDATIONS.md / skip]

### 2. ...

## Recommendations
- [Changes to RULES.md if any patterns should become rules]
- [Changes to RECOMMENDATIONS.md if decisions should be recorded]
- [New memory entries to create]

## Anti-Learnings
[Things that seemed like patterns but were actually one-off situations — don't generalize these]

6. Anti-patterns

• Over-generalizing — Don't turn a one-time fix into a permanent rule
• Storing ephemera — Don't save things like "currently debugging X"
• Ignoring negative results — Failed approaches are as valuable as successful ones
• Redundant storage — Don't save what's already in RULES.md or git history
• Low-confidence instincts — If you've only seen it once, note it but don't enforce it