chore: initial monorepo scaffold + WDS Phase 1+2 artifacts

- Nx 22.7 monorepo (pnpm 11.1, TypeScript 5.9, Node 24)
- apps/api: NestJS 11 (CJS conforme CODING-RULES.md PGD-DB-004)
- apps/web: React 19 + Vite 8 (ESM)
- libs/shared/api-interface: Zod contract base
- Docker Compose dev: Postgres 18, Valkey 8, MinIO, Mailpit
- WDS artifacts:
  - design-artifacts/A-Product-Brief/ (5 docs canônicos + 16 dialogs)
  - design-artifacts/B-Trigger-Map/ (hub + 4 personas + feature impact)
- Stack canon: STACK.md v2.2 + CODING-RULES.md v2.0 + brand.md
- AGENTS.md + README.md como entrada para devs/agentes

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.agents/skills/bmad-distillator/agents/round-trip-reconstructor.md

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+# Round-Trip Reconstructor Agent
+
+Act as a document reconstruction specialist. Your purpose is to prove a distillate's completeness by reconstructing the original source documents from the distillate alone.
+
+**Critical constraint:** You receive ONLY the distillate file path. You must NOT have access to the original source documents. If you can see the originals, the test is meaningless.
+
+## Process
+
+### Step 1: Analyze the Distillate
+
+Read the distillate file. Parse the YAML frontmatter to identify:
+- The `sources` list — what documents were distilled
+- The `downstream_consumer` — what filtering may have been applied
+- The `parts` count — whether this is a single or split distillate
+
+### Step 2: Detect Document Types
+
+From the source file names and the distillate's content, infer what type of document each source was:
+- Product brief, discovery notes, research report, architecture doc, PRD, etc.
+- Use the naming conventions and content themes to determine appropriate document structure
+
+### Step 3: Reconstruct Each Source
+
+For each source listed in the frontmatter, produce a full human-readable document:
+
+- Use appropriate prose, structure, and formatting for the document type
+- Include all sections the original document would have had based on the document type
+- Expand compressed bullets back into natural language prose
+- Restore section transitions and contextual framing
+- Do NOT invent information — only use what is in the distillate
+- Flag any places where the distillate felt insufficient with `[POSSIBLE GAP]` markers — these are critical quality signals
+
+**Quality signals to watch for:**
+- Bullets that feel like they're missing context → `[POSSIBLE GAP: missing context for X]`
+- Themes that seem underrepresented given the document type → `[POSSIBLE GAP: expected more on X for a document of this type]`
+- Relationships that are mentioned but not fully explained → `[POSSIBLE GAP: relationship between X and Y unclear]`
+
+### Step 4: Save Reconstructions
+
+Save each reconstructed document as a temporary file adjacent to the distillate:
+- First source: `{distillate-basename}-reconstruction-1.md`
+- Second source: `{distillate-basename}-reconstruction-2.md`
+- And so on for each source
+
+Each reconstruction should include a header noting it was reconstructed:
+
+```markdown
+---
+type: distillate-reconstruction
+source_distillate: "{distillate path}"
+reconstructed_from: "{original source name}"
+reconstruction_number: {N}
+---
+```
+
+### Step 5: Return
+
+Return a structured result to the calling skill:
+
+```json
+{
+  "reconstruction_files": ["{path1}", "{path2}"],
+  "possible_gaps": ["gap description 1", "gap description 2"],
+  "source_count": N
+}
+```
+
+Do not include conversational text, status updates, or preamble — return only the structured result.