ddaanet/codify

Codify Learnings into Permanent Docs

Transform session learnings into persistent, actionable documentation. Updates CLAUDE.md, context files, and related docs to capture rules and patterns.

Prerequisite: Execute in a clean session (fresh start). Runs inline — no delegation to sub-agents.

Execution Protocol

1. Assess Learnings

• Run plugin/bin/learning-ages.py to get the ages report
• Entries ≥7 active days → eligible for consolidation
• Entries <7 active days → keep in staging (do not consolidate)
• Read the eligible entries in agents/learnings.md for context
• Read: agents/decisions/*.md (relevant domain doc, if exists)

2. File Selection

For each eligible learning, search candidate target files for related headings:

Grep pattern="<keyword from learning>" path="plugin/fragments/" glob="*.md"
Grep pattern="<keyword from learning>" path="agents/decisions/" glob="*.md"

Use Grep results to ground routing — match the learning to the file that already contains related content.

Behavioral rules → plugin/fragments/*.md: Workflow patterns • Anti-patterns • Directive conflicts • Agent behavior Technical details → agents/decisions/*.md: Architecture • Implementation patterns • Technology choices (consult agents/decisions/README.md for domain → file routing) Implementation patterns → agents/decisions/implementation-notes.md: Mock patterns • Python quirks • API details agents/session.md: Active tasks/decisions • Handoff info • Temporary state • Blockers Skill references: .claude/skills/*/references/learnings.md • Domain-specific patterns (progressive disclosure) Agent templates → plugin/agents/*.md: Execution patterns • Tool usage • Error handling • Domain-specific guidance. Route when learning is actionable for a specific agent role (execution pattern, stop condition, tool preference, error handling heuristic). Eligible agents: artisan, brainstorm-name, corrector, design-corrector, hooks-tester, outline-corrector, refactor, runbook-corrector, runbook-outline-corrector, runbook-simplifier, scout, tdd-auditor, test-driver. Exclude plan-specific agents (generated per-runbook by prepare-runbook.py) Other: Plan files (historical only) Never: README.md • Test files • Temp files

For detailed routing guidance, see references/consolidation-patterns.md

3. Draft Update

Principles:

• Precision over brevity (unambiguous, clear boundaries)
• Examples over abstractions (concrete, actual paths)
• Constraints over guidelines ("Do not" > "avoid", "Always" > "consider", "Must" > "should")
• Atomic changes (one concept, self-contained)
• Measured data over estimates

Format:

### [Rule Name]
**[Imperative/declarative statement]**
[Supporting explanation if needed]
**Example:** [Concrete demonstration]

4. Apply + Verify

• Edit for modifications • Write for new files only (Read first if exists)
• Read updated section → Check formatting → Verify placement
• After consolidation: Remove consolidated learnings from agents/learnings.md, but keep the 3-5 most recent learnings (at bottom of file) for continuity
• File splitting (if needed): After Write/Edit to a decision file, check wc -l. If >400 lines, split by H2/H3 boundaries into 100-300 line sections. Run plugin/bin/validate-memory-index.py --fix after split

Step 4a: Update discovery mechanisms

After consolidating a learning:

1. Append to memory index: Generate /when or /how entry in agents/memory-index.md:
   • Trigger derivation (mechanical — no rephrasing):
     • ## When X Y Z → /when x y z
     • ## How to X Y → /how x y
     • Title IS the trigger. Lowercase, preserve all words after operator prefix
   • Use key compression tool (plugin/bin/compress-key.py) to verify uniqueness
   • Operator selection:
     • /when for behavioral knowledge (when to do X, when X applies)
     • /how for procedural knowledge (how to do X, technique for X)
2. If new fragment created: Add @-reference to CLAUDE.md OR create .claude/rules/ entry if path-scoped. Heuristic: If the learning applies regardless of which files are being edited → @-ref in CLAUDE.md. If it only applies when working with a specific file type or directory → .claude/rules/ entry with path frontmatter.
3. If existing fragment updated: Ensure memory index entry reflects the updated content (add new entry or update existing one)
4. If decision file updated: Verify corresponding .claude/rules/ entry exists for path trigger

Learnings Quality Criteria

Principle-level (consolidate): ✅

• States a general constraint or pattern
• Applies beyond the specific incident
• Example: "Always load skill context before editing"

Incident-specific (reject/revise): ❌

• Describes what happened, not what to do
• Narrow to one case, not generalizable
• Example: "Edited skill without loading it" → revise to principle

Meta-learnings (use sparingly):

• Rules about rules — only when behavioral constraint required
• Example: "Soft limits normalize deviance" → consolidate if recurrent

Title Format Requirements

Titles must follow the validation rules enforced by precommit:

• Prefix required: Start with When or How to
• Min 2 content words after prefix: ## When choosing review gate (2 content words — pass). ## When testing (1 content word — fail)

Anti-pattern examples:

• ## transformation table → missing prefix, jargon title
• ## When testing → prefix OK, but only 1 content word
• ## How encode → must be How to, not bare How

Correct pattern examples:

• ## When choosing review gate — situation at decision point
• ## How to encode file paths — procedural knowledge
• ## When haiku rationalizes test failures — specific behavioral pattern

Titles become /when and /how triggers mechanically — name after the activity at the decision point, not the outcome or jargon.

Staging Retention Guidance

Keep in staging (do not consolidate):

• Entries < 7 active days old (insufficient validation)
• Entries with pending cross-references (depend on other work)
• Entries under active investigation

Consolidate:

• Entries ≥ 7 active days with proven validity
• Entries that have been applied consistently
• Entries referenced by multiple sessions

Drop (remove from staging):

• Superseded by newer entry on same topic
• Contradicted by subsequent work
• Incident-specific without generalizable principle

5. Document

Commit: Update [file]: [what]\n\n- [change 1]\n- [change 2]\n- [rationale] Handoff (if session.md): Note update, explain significance, reference commit

Rule Management

For guidance on tiering, budgeting, and maintaining rules in CLAUDE.md, see references/rule-management.md.

Tool Constraints

• Read current docs • Edit existing (preferred) • Write new only • Bash git ops
• Minimal scope/diffs • Preserve structure • Verify actionable+concrete • Imperative tone

Common Patterns

For detailed usage patterns (error handling, workflow improvement, design decisions, pending learnings), see examples/codify-patterns.md.