learn-eval

/learn-eval - Extract, Evaluate, then Save

Extends /learn with a quality gate and save-location decision before writing any skill file.

What to Extract

Look for:

1. Error Resolution Patterns — root cause + fix + reusability
2. Debugging Techniques — non-obvious steps, tool combinations
3. Workarounds — library quirks, API limitations, version-specific fixes
4. Project-Specific Patterns — conventions, architecture decisions, integration patterns

Process

1. Review the session for extractable patterns

2. Identify the most valuable/reusable insight

3. Determine save location:

   • Ask: "Would this pattern be useful in a different project?"
   • Global (~/.claude/skills/learned/): Generic patterns usable across 2+ projects (bash compatibility, LLM API behavior, debugging techniques, etc.)
   • Project (.claude/skills/learned/ in current project): Project-specific knowledge (quirks of a particular config file, project-specific architecture decisions, etc.)
   • When in doubt, choose Global (moving Global → Project is easier than the reverse)

4. Draft the skill file using this format:

---
name: pattern-name
description: "Under 130 characters"
user-invocable: false
origin: auto-extracted
---

# [Descriptive Pattern Name]

**Extracted:** [Date]
**Context:** [Brief description of when this applies]

## Problem
[What problem this solves - be specific]

## Solution
[The pattern/technique/workaround - with code examples]

## When to Use
[Trigger conditions]

1. Self-evaluate before saving using this rubric:

   Dimension	1	3	5
   Specificity	Abstract principles only, no code examples	Representative code example present	Rich examples covering all usage patterns
   Actionability	Unclear what to do	Main steps are understandable	Immediately actionable, edge cases covered
   Scope Fit	Too broad or too narrow	Mostly appropriate, some boundary ambiguity	Name, trigger, and content perfectly aligned
   Non-redundancy	Nearly identical to another skill	Some overlap but unique perspective exists	Completely unique value
   Coverage	Covers only a fraction of the target task	Main cases covered, common variants missing	Main cases, edge cases, and pitfalls covered