everything-claude-code/commands/learn-eval.md

description

description
Extract reusable patterns from the session, self-evaluate quality before saving, and determine the right save location (Global vs Project).

/learn-eval - Extract, Evaluate, then Save

Extends /learn with a quality gate, save-location decision, and knowledge-placement awareness 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]

5. Quality gate — Checklist + Holistic verdict

   5a. Required checklist (verify by actually reading files)

   Execute all of the following before evaluating the draft:

   • Grep ~/.claude/skills/ by keyword to check for content overlap
   • Check MEMORY.md (both project and global) for overlap
   • Consider whether appending to an existing skill would suffice
   • Confirm this is a reusable pattern, not a one-off fix

   5b. Holistic verdict

   Synthesize the checklist results and draft quality, then choose one of the following:

   Verdict	Meaning	Next Action
   Save	Unique, specific, well-scoped	Proceed to Step 6
   Improve then Save	Valuable but needs refinement	List improvements → revise → re-evaluate (once)
   Absorb into [X]	Should be appended to an existing skill	Show target skill and additions → Step 6
   Drop	Trivial, redundant, or too abstract	Explain reasoning and stop

   Guideline dimensions (informing the verdict, not scored):

   • Specificity & Actionability: Contains code examples or commands that are immediately usable
   • Scope Fit: Name, trigger conditions, and content are aligned and focused on a single pattern
   • Uniqueness: Provides value not covered by existing skills (informed by checklist results)
   • Reusability: Realistic trigger scenarios exist in future sessions

6. Verdict-specific confirmation flow

   • Save: Present save path + checklist results + 1-line verdict rationale + full draft → save after user confirmation
   • Absorb into [X]: Present target path + additions (diff format) + checklist results + verdict rationale → append after user confirmation
   • Drop: Show checklist results + reasoning only (no confirmation needed)

7. Save / Absorb to the determined location

Output Format for Step 5

### Checklist
- [x] skills/ grep: no overlap (or: overlap found → details)
- [x] MEMORY.md: no overlap (or: overlap found → details)
- [x] Existing skill append: new file appropriate (or: should append to [X])
- [x] Reusability: confirmed (or: one-off → Drop)

### Verdict: Save / Improve then Save / Absorb into [X] / Drop

**Rationale:** (1-2 sentences explaining the verdict)

Design Rationale

This version replaces the previous 5-dimension numeric scoring rubric (Specificity, Actionability, Scope Fit, Non-redundancy, Coverage scored 1-5) with a checklist-based holistic verdict system. Modern frontier models (Opus 4.6+) have strong contextual judgment — forcing rich qualitative signals into numeric scores loses nuance and can produce misleading totals. The holistic approach lets the model weigh all factors naturally, producing more accurate save/drop decisions while the explicit checklist ensures no critical check is skipped.

Notes

• Don't extract trivial fixes (typos, simple syntax errors)
• Don't extract one-time issues (specific API outages, etc.)
• Focus on patterns that will save time in future sessions
• Keep skills focused — one pattern per skill
• When the verdict is Absorb, append to the existing skill rather than creating a new file