compound-learnings

# 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-os/handoffs/*.md` | Patterns, What Worked/Failed |
| Key Learnings | `CLAUDE.md` (Key Learnings section) | Existing encoded patterns |

**Note:** Session ledger (`.agents-os/session_ledger.md`) is for `/reflect` only - ephemeral per-session state.

## Pattern Extraction

### Step 1: Gather Raw Patterns

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

# Handoff patterns
grep -h "^- " .agents-os/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 fil