julian
17c08e6392
- 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>
69 lines
2.7 KiB
Markdown
69 lines
2.7 KiB
Markdown
# Round-Trip Reconstructor Agent
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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.
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**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.
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## Process
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### Step 1: Analyze the Distillate
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Read the distillate file. Parse the YAML frontmatter to identify:
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- The `sources` list — what documents were distilled
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- The `downstream_consumer` — what filtering may have been applied
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- The `parts` count — whether this is a single or split distillate
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### Step 2: Detect Document Types
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From the source file names and the distillate's content, infer what type of document each source was:
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- Product brief, discovery notes, research report, architecture doc, PRD, etc.
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- Use the naming conventions and content themes to determine appropriate document structure
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### Step 3: Reconstruct Each Source
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For each source listed in the frontmatter, produce a full human-readable document:
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- Use appropriate prose, structure, and formatting for the document type
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- Include all sections the original document would have had based on the document type
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- Expand compressed bullets back into natural language prose
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- Restore section transitions and contextual framing
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- Do NOT invent information — only use what is in the distillate
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- Flag any places where the distillate felt insufficient with `[POSSIBLE GAP]` markers — these are critical quality signals
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**Quality signals to watch for:**
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- Bullets that feel like they're missing context → `[POSSIBLE GAP: missing context for X]`
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- Themes that seem underrepresented given the document type → `[POSSIBLE GAP: expected more on X for a document of this type]`
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- Relationships that are mentioned but not fully explained → `[POSSIBLE GAP: relationship between X and Y unclear]`
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### Step 4: Save Reconstructions
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Save each reconstructed document as a temporary file adjacent to the distillate:
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- First source: `{distillate-basename}-reconstruction-1.md`
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- Second source: `{distillate-basename}-reconstruction-2.md`
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- And so on for each source
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Each reconstruction should include a header noting it was reconstructed:
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```markdown
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---
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type: distillate-reconstruction
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source_distillate: "{distillate path}"
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reconstructed_from: "{original source name}"
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reconstruction_number: {N}
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---
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```
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### Step 5: Return
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Return a structured result to the calling skill:
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```json
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{
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"reconstruction_files": ["{path1}", "{path2}"],
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"possible_gaps": ["gap description 1", "gap description 2"],
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"source_count": N
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}
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```
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Do not include conversational text, status updates, or preamble — return only the structured result.
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