---

name: compound-learnings description: Extract patterns from git history and session files, recommend artifacts (skill/rule/hook/agent) based on frequency thresholds allowed-tools: [Bash, Read, Glob, Grep]

Compound Learnings

Transform recurring patterns into durable artifacts. Use frequency-based thresholds to distinguish noise from signal.

Data Sources

Scan these locations for patterns:

Source	Command/Path	What to Extract
Git commits	git log --oneline -100	Repeated fix types, refactor patterns
Git commit bodies	git log -50 --format="%B---"	Lessons in commit descriptions
PR descriptions	gh pr list --state merged -L 20	Decisions, learnings
Handoffs	$MAIN_WORKTREE/.claude/handoffs/*.md	Patterns, What Worked/Failed
Key Learnings	CLAUDE.md (Key Learnings section)	Existing encoded patterns

Note: Session ledger (.claude/session_ledger.md) is for /reflect only - ephemeral per-session state.

Pattern Extraction

Step 1: Gather Raw Patterns

# Git patterns (look for repeated prefixes/types)
git log --oneline -100 | cut -d' ' -f2- | sort | uniq -c | sort -rn

# Handoff patterns
grep -h "^- " .claude/handoffs/*.md 2>/dev/null | sort | uniq -c | sort -rn

Step 2: Consolidate Similar Patterns

Before counting, normalize patterns:

• "Always validate X" + "Validate X before Y" → "Validate X"
• "Don't use Z" + "Avoid Z" + "Z causes issues" → "Avoid Z"

Group by semantic meaning, not exact wording.

Step 3: Apply Frequency Thresholds

Occurrences	Action	Rationale
1	Skip	Could be noise, one-off incident
2	Note	Emerging pattern, watch for recurrence
3+	Recommend	Clear pattern, suggest artifact
4+	Strong recommend	Encode immediately

Artifact Categorization

Use this decision tree to determine artifact type:

Is it a sequential workflow with distinct phases?
  YES → Consider COMMAND (user-invoked) or AGENT (autonomous)
    Does it need user interaction during execution?
      YES → COMMAND
      NO → AGENT
  NO ↓

Should it trigger automatically on file/context patterns?
  YES → SKILL (probabilistic, Claude MAY follow)
    Is enforcement critical (must happen every time)?
      YES → Consider HOOK instead (deterministic)
  NO ↓

Is it a simple rule or convention?
  YES → RULE (add to CLAUDE.md or review-topics.md)
    Project-specific? → review-topics.md
    Universal? → CLAUDE.md
  NO ↓

Does it enhance an existing agent's behavior?
  YES → AGENT UPDATE (modify existing agent)
  NO → Likely doesn't need encoding

Quick Reference

Artifact	When to Use	Example
Rule	Simple convention, always applies	"Use kebab-case for file names"
Skill	Knowledge/context for specific work	"Stimulus controller patterns"
Hook	Must enforce behavior deterministically	"Run linter before commit"
Command	User-invoked workflow with arguments	"/deploy --env staging"
Agent	Autonomous task, returns report	"security-review agent"

Output Format

Present findings as:

## Compound Learnings Analysis

### Strong Signal (4+ occurrences)
| Pattern | Count | Recommended Artifact | Rationale |
|---------|-------|---------------------|-----------|
| ... | ... | ... | ... |

### Emerging Patterns (2-3 occurrences)
| Pattern | Count | Potential Artifact | Notes |
|---------|-------|-------------------|-------|
| ... | ... | ... | ... |

### Recommended Actions
1. **[Artifact Type]**: `name` - description
   - Draft: [brief template or content]

Quality Checks

Before recommending an artifact, verify:

• Generality: Applies beyond the specific incidents where it was observed
• Specificity: Concrete enough to act on (not vague advice)
• Uniqueness: Doesn't duplicate existing CLAUDE.md rules or skills
• Correct Type: Matches the categorization decision tree

Integration with /learn

When invoked from /learn:

1. Locate main worktree for centralized handoffs
2. Gather patterns from git, PRs, and handoffs
3. Consolidate and count frequencies
4. Apply thresholds
5. Categorize recommended artifacts
6. Present findings with draft content
7. If approved, create artifacts using appropriate tools

Note: /reflect is for single-session analysis. /learn is for cross-session compound learning.