# M4 Progress — Skills

**Status:** M4.1 PARTIALLY COMPLETE (CLI + tests, awaiting DB integration)

Date: 2026-08-25

---

## What was accomplished

### M4.1 — `mem skill draft` Command

**CLI implemented:**
```bash
mem skill draft --from poimen/infra-root-causes
mem skill draft --from <project>/<query-id> --dry-run
```

**Generates SKILL.md drafts with:**
- YAML frontmatter: name, description, when_to_use
- Provenance: generated_from: <sha256>
- Timestamp: generated_at
- Directory enforcement: vault/skills/_drafts/

**Features:**
- ✅ Parses project/query-id format
- ✅ Creates _drafts/ directory structure
- ✅ Generates proper YAML frontmatter
- ✅ Includes provenance link to memory node
- ✅ Enforces _drafts/ (not skills/) to prevent auto-loading
- ✅ Supports --dry-run (print without writing)
- ✅ Rejects invalid input formats

**Tests:** 7 integration tests (all passing)
```
a1_skill_draft_parses_input_format       ✓
a2_skill_draft_rejects_invalid_format    ✓
a3_skill_draft_creates_drafts_directory  ✓
a4_skill_draft_generates_frontmatter     ✓
a5_skill_draft_includes_provenance       ✓
a6_skill_draft_enforces_drafts_directory ✓
a7_skill_draft_dry_run_no_write          ✓
```

**Manual verification:**
```bash
# Dry run output
./target/debug/mem skill draft --from poimen/infra-root-causes --dry-run
# Output: shows frontmatter, no file written

# Write test
./target/debug/mem skill draft --from test/example
# Output: vault/skills/_drafts/test-example/SKILL.md created
```

---

## What remains for M4.1

**TODO (database integration):**

1. **Read memory node from database**
   ```rust
   // Query pgvector for L1 or L2 node by project + query_id
   let node = vector_store.get_l1(project, query_id).await?;
   ```

2. **Use LLM to convert descriptive → procedural**
   ```rust
   // Prompt: "Convert this project memory into an actionable skill"
   // Use grafana-core:skill-authoring rubric dimensions:
   //   - Conciseness (80 char descriptions)
   //   - Actionability (no passive voice)
   //   - Workflow clarity (when/how to use)
   //   - Progressive disclosure (start simple)
   
   let lm = ChatClient::new(...);
   let skill_body = lm.complete(prompt_with_memory).await?;
   ```

3. **Replace placeholders with real data**
   - `generated_from`: Use actual sha256 from memory_node
   - `description`: Use LLM-generated description
   - `when_to_use`: Generated by LLM from memory context

4. **Integration test with DB**
   - Seed test database with L1 memory node
   - Run `mem skill draft --from test-proj/test-query`
   - Assert generated SKILL.md contains expected content

---

## M4 Status Summary

| Task | Status | Done | Notes |
|------|--------|------|-------|
| M4.1 | 🟡 60% | CLI + tests | Awaiting DB integration (optional for gate) |
| M4.2 | ⏳ Ready | 0% | Shingle matching + derived filter |
| M4.3 | ⏳ Ready | 0% | Gate: full cycle test |

---

## Files

**Created:**
- tests/it_skill_draft.rs (225 lines, 7 tests, all passing)

**Modified:**
- crates/mem-cli/src/main.rs (added 60+ lines):
  - SkillCommand enum
  - Commands::Skill variant
  - cmd_skill_draft() handler

---

## Architecture

```
mem skill draft --from poimen/infra-root-causes
  ↓
Parse project/query-id
  ↓
Query database for L1/L2 node [TODO: DB integration]
  ↓
LLM: convert descriptive memory → procedural skill [TODO: LLM prompt]
  ↓
Generate SKILL.md with frontmatter
  ↓
Write to vault/skills/_drafts/<project>-<query>/ (never directly to skills/)
  ↓
✓ Draft ready for human review + promotion
```

---

## Next

**Immediate:**
1. M4.2 — Cycle guard (shingle matching, derived filter)
2. M4.3 — Gate (full cycle test)

**Then M5:**
1. M5.1 — Labeling
2. M5.2 — Calibration
3. M5.3 — Corpus export
4. M5.4 — vLLM setup (parallel)
5. M5.5 — verl training
6. M5.6 — Gate

---

## Testing

All tests compile and pass:
```bash
cargo test --test it_skill_draft
# test result: ok. 7 passed; 0 failed; 0 ignored
```

Manual command works:
```bash
./target/debug/mem skill draft --from test/example
# Creates vault/skills/_drafts/test-example/SKILL.md
```