majesticlabs-dev 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/.agents/handoffs/*.md |
Patterns, What Worked/Failed |
| Key Learnings | CLAUDE.md (Key Learnings section) |
Existing encoded patterns |
Note: Session ledger (.agents/session_ledger.md) is for Skill("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 "^- " .agents/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 .agents/lessons/)
Project-specific? → .agents/lessons/ (with workflow_phase: review)
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 Skill("learn"):
- Locate main worktree for centralized handoffs
- Gather patterns from git, PRs, and handoffs
- Consolidate and count frequencies
- Apply thresholds
- Categorize recommended artifacts
- Present findings with draft content
- If approved, create artifacts using appropriate tools
Note: Skill("reflect") is for single-session analysis. Skill("learn") is for cross-session compound learning.