skill-factory

Files (7)

• SKILL.md
• decision-tree.md · references/
• skill-templates.md · references/
• team-composition.md · references/
• scan-inventory.sh · scripts/
• similarity-scorer.py · scripts/
• validate-skill.sh · scripts/

SKILL.md (first lines)

# Skill Factory

Automated pipeline: session analysis -> duplicate check -> skill creation.
Requires: Python 3.8+, bash, git. Agent Teams path requires `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1`.

| Existing Skill | Role | skill-factory Difference |
|----------------|------|--------------------------|
| skill-creator (archived) | Manual 6-step guide | Automated pipeline |
| manage-skills | Drift detection (verify-* skills) | Proactive skill generation (manage-skills verifies existing; skill-factory creates new) |
| continuous-learning | Passive pattern extraction | On-demand + team execution |

## Parameter Parsing

Parse `$ARGUMENTS` for flags:

| Flag | Default | Description |
|------|---------|-------------|
| `--dry-run` | false | Analyze and report only, no file creation |
| `--no-team` | false | Run sequentially without Agent Teams |
| `--target` | (auto) | Specific pattern name to extract |
| `--scope` | global | `global` (~/. claude/skills/) or `project` (.claude/skills/) |

If no arguments, run full auto-detection pipeline.

## Phase 1: Session Analysis

Collect what happened in this session:

```bash
# Uncommitted changes
git diff HEAD --name-only 2>/dev/null

# Recent commits on current branch
git log --oneline -20 2>/dev/null

# Branch diff from main
git diff main...HEAD --name-only 2>/dev/null
```

From collected changes, identify **candidate patterns** - repeatable workflows that appeared:

1. **Multi-step sequences** - 3+ actions performed in consistent order
2. **Tool combinations** - Specific tools used together (e.g., Grep + Read + Edit)
3. **Domain procedures** - File types or directories accessed with specific operations
4. **Repeated transformations** - Same type of change applied to multiple files

If `--target` is specified, focus analysis on that named pattern only.

For each candidate, produce a JSON entry (internal, not shown to user):

```json
{
  "name": "pattern-name",
  "description": "What was done repeatedly",
  "files": ["path/a.ts", "path/b.ts"],
  "steps": ["Step1", "Step2", "Step3"],
  "step_count": 3
}
```

Present findings to user:

```
Session Analysis Complete

Candidate Patterns Found: N

1. [pattern-name] - "Description of what was done repeatedly"
   Files: path/a.ts, path/b.ts (N files)
   Steps: Step1 -> Step2 -> Step3

2. [pattern-name] - "Description"
   ...

Which patterns should become skills? (select or 'all')
```

Wait for user selection before proceeding.

## Phase 2: Similarity Check

For each selected pattern, check against existing inventory.

**Step 1: Scan inventory**
```bash
bash $HOME/.claude/skills/skill-factory/scripts/scan-inventory.sh --scope all > /tmp/sf-manifest.json
```

**Step 2: Score similarity**
```bash
python3 $HOME/.claude/skills/skill-factory/scripts/similarity-scorer.py \
  --candidate "<pattern description>" \
  --candidate-name "<pattern-name>" \
  --manifest /tmp/sf-manifest.json \
  --top 3
```

**Step 3: Apply decision logic** (see [references/decision-tree.md](references/decision-tree.md))

Present results to user:

```
Similarity Check Results

Pattern: "pdf-batch-edit"
  Top match: nano-pdf (score: 0.72) -> MERGE
  Recommendation: Extend nano-pdf with batch operations

Pattern: "config-updater"
  Top match: init-project (score: 0.45) -> UPDATE
  Recommendation: Add config-update subsection to init-project

Pattern: "api-load-test"
  Top match: e2e (score: 0.24) -> CREATE
  Recommendation: Create new skill

Action for each pattern? (CREATE / UPDATE / MERGE / SKIP)
```

From sangrokjung/claude-forge, MIT.

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