General

codify-learning - Claude MCP Skill

Transform session learnings into permanent, executable improvements. Invoke at end of any session that involved debugging, fixing, or learning something. Default: Codify everything. Exception: Justify not codifying.

SEO Guide: Enhance your AI agent with the codify-learning tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to transform session learnings into permanent, executable improvements. invoke at end of any session th... Download and configure this skill to unlock new capabilities for your AI workflow.

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Documentation

SKILL.md
# /codify-learning

Transform ephemeral learnings into durable system improvements.

## Philosophy

**Default codify, justify exceptions.** Every correction, feedback, or "I should have known" moment represents a gap in the system. Codification closes that gap.

The "3+ occurrences" threshold is a myth - we have no cross-session memory. If you learned something, codify it.

## Process

### 1. Identify Learnings

Scan the session for:
- Errors encountered and how they were fixed
- PR feedback received
- Debugging insights ("the real problem was...")
- Workflow improvements discovered
- Patterns that should be enforced

### 2. Brainstorm Codification Targets

For each learning, consider:
- **Hook** - Should this be guaranteed/blocked? (most deterministic)
- **Lint rule** - Can a lint rule catch this at edit time? → invoke `/guardrail`
- **Agent** - Should a reviewer catch this pattern?
- **Skill** - Is this a reusable workflow?
- **CLAUDE.md** - Is this philosophy/convention?

Choose the target that provides the most leverage. Hooks > Lint rules > Agents > Skills > CLAUDE.md for enforcement. Skills > CLAUDE.md for workflows.

Lint rules are ideal for: import boundaries, naming conventions, deprecated API usage, auth enforcement, architectural layering violations. If the pattern can be expressed as "this code shape should never/always appear," it's a lint rule.

### 3. Implement

For each codification:
1. Read the target file
2. Add the learning in appropriate format
3. Wire up if needed (hooks need settings.json entry)
4. Verify no duplication

### 4. Report

```
CODIFIED:
- [learning] → [file]: [summary of change]

NOT CODIFIED:
- [learning]: [justification - must be specific]
```

## Anti-Patterns

āŒ "No patterns detected" - One occurrence is enough
āŒ "First time seeing this" - No cross-session memory exists
āŒ "Seems too minor" - Minor issues compound into major friction
āŒ "Not sure where to put it" - Brainstorm, ask, don't skip
āŒ "Already obvious" - If it wasn't codified, the system didn't know it

See CLAUDE.md "Continuous Learning Philosophy" for valid exceptions and the full codification philosophy.

## See Also

`/done` — Full session retrospective (subsumes codification as one step in a broader process: went-well, friction, bugs, codify, report).

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Information

Repository
phrazzld/claude-config
Author
phrazzld
Last Sync
3/2/2026
Repo Updated
3/1/2026
Created
1/24/2026

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