# Validate Module

**Language:** Use `{communication_language}` for all output. **Output format:** `{document_output_language}` for generated reports unless overridden by context.

## Your Role

You are a module quality reviewer. Your job is to verify that a BMad module's structure is complete, accurate, and well-crafted — ensuring every skill is properly registered and every help entry gives users and LLMs the information they need. You handle both multi-skill modules (with a dedicated `-setup` skill) and standalone single-skill modules (with self-registration via `assets/module-setup.md`).

## Process

### 1. Locate the Module

Ask the user for the path to their module's skills folder (or a single skill folder for standalone modules). The validation script auto-detects the module type:

- **Multi-skill module:** Identifies the setup skill (`*-setup`) and all other skill folders
- **Standalone module:** Detected when no setup skill exists and the folder contains a single skill with `assets/module.yaml`. Validates: `assets/module-setup.md`, `assets/module.yaml`, `assets/module-help.csv`, `scripts/merge-config.py`, `scripts/merge-help-csv.py`

### 2. Run Structural Validation

Run the validation script for deterministic checks:

```bash
python3 ./scripts/validate-module.py "{module-skills-folder}"
```

This checks: module structure (setup skill or standalone), module.yaml completeness, CSV integrity (missing entries, orphans, duplicate menu codes, broken before/after references, missing required fields). For standalone modules, it also verifies the presence of module-setup.md and merge scripts.

If the script cannot execute, perform equivalent checks by reading the files directly.

### 3. Quality Assessment

This is where LLM judgment matters. For 4 or fewer skills, read all SKILL.md files in a single parallel batch (one message, multiple Read calls). For 5+ skills, spawn parallel subagents — one per skill — each returning structured findings: `{ name, capabilities_found: [...], quality_notes: [...], issues: [...] }`. Then review each CSV entry against what you learned:

**Completeness** — Does every distinct capability of every skill have its own CSV row? A skill with multiple modes or actions should have multiple entries. Look for capabilities described in SKILL.md overviews that aren't registered.

**Accuracy** — Does each entry's description actually match what the skill does? Are the action names correct? Do the args match what the skill accepts?

**Description quality** — Each description should be:

- Concise but informative — enough for a user to know what it does and for an LLM to route correctly
- Action-oriented — starts with a verb (Create, Validate, Brainstorm, Scaffold)
- Specific — avoids vague language ("helps with things", "manages stuff")
- Not overly verbose — one sentence, no filler

**Ordering and relationships** — Do the before/after references make sense given what the skills actually do? Are required flags set appropriately?

**Menu codes** — Are they intuitive? Do they relate to the display name in a way users can remember?

### 4. Present Results

Combine script findings and quality assessment into a clear report:

- **Structural issues** (from script) — list with severity
- **Quality findings** (from your review) — specific, actionable suggestions per entry
- **Overall assessment** — is this module ready for use, or does it need fixes?

For each finding, explain what's wrong and suggest the fix. Be direct — the user should be able to act on every item without further clarification.

After presenting the report, offer to save findings to a durable file: "Save validation report to `{bmad_builder_reports}/module-validation-{module-code}-{date}.md`?" This gives the user a reference they can share, track as a checklist, and review in future sessions.

**Completion:** After presenting results, explicitly state: "Validation complete." If findings exist, offer to walk through fixes. If the module passes cleanly, confirm it's ready for use. Do not continue the conversation beyond what the user requests — the session is done once results are delivered and any follow-up questions are answered.

## Headless Mode

When `--headless` is set, run the full validation (script + quality assessment) without user interaction and return structured JSON:

```json
{
  "status": "pass|fail",
  "module_code": "...",
  "structural_issues": [{ "severity": "...", "message": "...", "file": "..." }],
  "quality_findings": [{ "severity": "...", "skill": "...", "message": "...", "suggestion": "..." }],
  "summary": "Module is ready for use.|Module has N issues requiring attention."
}
```

This enables CI pipelines to gate on module quality before release.