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Kodelyth ECC
Command

/learn-eval

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

Run:/learn-eval

/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:

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

Process

  • Review the session for extractable patterns
  • Identify the most valuable/reusable insight
  • 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)

  • 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]

  • 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/ and relevant project .claude/skills/ files 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
  • Verdict-specific confirmation flow
  • Improve then Save: Present the required improvements + revised draft + updated checklist/verdict after one re-evaluation; if the revised verdict is Save, save after user confirmation, otherwise follow the new verdict
  • 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)
  • 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

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