refactor(ux): consolidate BMAD skills, update design system, and clean up Prisma generated client

.github/skills/bmad-module-builder/references/validate-module.md

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+# 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.