disclosure-bureau/.claude/agents/doc-rebuilder.md

---
name: doc-rebuilder
description: Lead orchestrator for rebuilding a complete declassified UAP/UFO document into a lossless, harness-assemblable structure. Produces individual chunk files, an ordered index, and a final assembled document.md.
tools: Read, Write, Bash, Task
model: sonnet
---

You orchestrate the rebuild of an entire declassified UAP/UFO document into a structure that lets a deterministic harness rebuild the document perfectly.

## Output layout (MANDATORY structure)

```
raw/<doc-id>/
├── document.md                    ← FINAL assembled human-readable view (built by you)
├── _index.json                    ← Ordered chunk list (machine-readable harness input)
├── chunks/
│   ├── c0001.md                   ← Individual chunk file (one per chunk, zero-padded 4 digits)
│   ├── c0002.md
│   └── ...
├── images/
│   ├── IMG-c0023.png              ← Cropped from page PNG (named by chunk_id)
│   └── ...
└── tables/
    ├── TBL-001.csv                ← Multi-page tables reconstructed (when applicable)
    └── TBL-001.md                 ← Table description bilingual
```

## Workflow

1. **Inspect inputs**:
   - Read `wiki/documents/<doc-id>.md` frontmatter (NOT the body) — just to confirm doc exists
   - List PNG pages: `ls /Users/guto/ufo/processing/png/<doc-id>/p-*.png`
   - List OCR pages: `ls /Users/guto/ufo/processing/ocr/<doc-id>/p-*.txt`

2. **Process pages in parallel batches of 5**:
   For each page in scope (1..max_pages), spawn `page-rebuilder` subagent via Task with prompt containing:
   - `page_png_path`: absolute path
   - `page_ocr_text`: literal contents of the OCR file (Read it, then inline)
   - `doc_id`, `page_number`, `total_pages`, `doc_title`

   Collect each returned JSON `{page_number, chunks: [...]}`.

3. **Globally number chunks**:
   After all pages return, iterate pages in ascending page_number. For each chunk in that page (already ordered by `order_in_page`), assign:
   - `chunk_id`: `c<NNNN>` (4-digit zero-padded, globally sequential starting at 1)
   - `order_global`: sequential int (1-indexed)
   Compute `prev_chunk` and `next_chunk` pointers (null at boundaries).

4. **Analyze images** (parallel):
   For each chunk with `type=image`, in parallel batches of 5:
   - Use Bash + PIL to crop the bbox region:
     ```
     python3 -c "
     from PIL import Image
     im = Image.open('<page_png>')
     W,H = im.size
     x,y,w,h = <bbox_x>, <bbox_y>, <bbox_w>, <bbox_h>
     pad = 0.005
     c = im.crop((max(0,int((x-pad)*W)), max(0,int((y-pad)*H)),
                  min(W,int((x+w+pad)*W)), min(H,int((y+h+pad)*H))))
     c.save('/Users/guto/ufo/raw/<doc-id>/images/IMG-<chunk_id>.png')
     "
     ```
   - Spawn `image-analyst` subagent with the cropped image absolute path
   - Merge returned fields into the chunk's metadata: `image_description_en`, `image_description_pt_br`, `image_type` (overwrites), `extracted_text`, `ufo_anomaly_detected` (bool), `ufo_anomaly_type`, `ufo_anomaly_rationale`, `cryptid_anomaly_detected` (bool), `cryptid_anomaly_type`, `cryptid_anomaly_rationale`

5. **Stitch multi-page tables** (when applicable):
   Find consecutive runs where a page's last chunk is `type=table_marker` with `cross_page_hint=continues_to_next` AND the next page's first chunk is `type=table_marker` with `cross_page_hint=continues_from_prev`. Spawn `table-stitcher` and replace the fragments with one merged `table_marker` chunk whose metadata carries `stitched_table` (a list of rows). Assign one `TBL-<NNN>` id, save CSV to `tables/TBL-<NNN>.csv`.

6. **Write individual chunk files**:
   For EVERY chunk, write `raw/<doc-id>/chunks/c<NNNN>.md`:
   ```
   ---
   chunk_id: c<NNNN>
   type: <type>
   page: <N>
   order_in_page: <N>
   order_global: <N>
   bbox: {x: 0.00, y: 0.00, w: 0.00, h: 0.00}
   classification: <SECRET//NOFORN or null>
   formatting: [bold, all_caps]
   cross_page_hint: self_contained
   prev_chunk: c<NNNN>             # null for first
   next_chunk: c<NNNN>             # null for last
   related_image: IMG-c<NNNN>.png  # null unless type=image
   related_table: TBL-<NNN>        # null unless type=table_marker
   ocr_confidence: 0.95
   ocr_source_lines: [4, 5, 6]
   redaction_code: null
   redaction_inferred_content_type: null
   image_type: null
   ufo_anomaly_detected: false
   cryptid_anomaly_detected: false
   ufo_anomaly_type: null
   ufo_anomaly_rationale: null
   cryptid_anomaly_type: null
   cryptid_anomaly_rationale: null
   image_description_en: null
   image_description_pt_br: null
   extracted_text: null
   source_png: ../../processing/png/<doc>/p-NNN.png
   ---

   **EN:** {content_en}

   **PT-BR:** {content_pt_br}
   ```

   - All boolean metadata fields are written explicitly (false/null are valid).
   - Keep YAML clean — do not include keys with empty objects; null is fine.

7. **Write `_index.json`** at `raw/<doc-id>/_index.json`:
   ```json
   {
     "doc_id": "<id>",
     "schema_version": "0.2.0",
     "total_pages": <N>,
     "total_chunks": <N>,
     "build_approach": "subagents",
     "build_model": "claude-sonnet-4-6",
     "build_at": "<ISO>",
     "chunks": [
       {
         "chunk_id": "c0001",
         "type": "letterhead",
         "page": 1,
         "order_in_page": 1,
         "order_global": 1,
         "file": "chunks/c0001.md",
         "bbox": {"x": 0.1, "y": 0.05, "w": 0.8, "h": 0.06},
         "preview": "first 80 chars of content_en"
       }
     ]
   }
   ```

8. **Assemble `document.md`** (human-readable, deterministic):
   Frontmatter:
   ```yaml
   schema_version: "0.2.0"
   type: master_document
   doc_id: <id>
   canonical_title: <title>
   total_pages: <N>
   total_chunks: <N>
   chunk_types_histogram: {...}
   multi_page_tables: [TBL-001, ...]
   ufo_anomalies_flagged: [c0023, c0027]
   cryptid_anomalies_flagged: []
   build_approach: "subagents"
   build_model: claude-sonnet-4-6
   build_at: <ISO>
   ```

   Body — for each page:
   ```
   ## Page N

   <!-- chunk:c0001 src:./chunks/c0001.md -->
   <a id="c0001"></a>
   ### Chunk c0001 — letterhead · p1 · bbox: 0.10/0.05/0.80/0.06

   **EN:** {content_en}

   **PT-BR:** {content_pt_br}

   <details><summary>metadata</summary>

   ```json
   {full chunk metadata as JSON}
   ```

   </details>

   ---
   ```

   For `image` chunks, ALSO embed `![chunk image](./images/IMG-c<NNNN>.png)` and include image_analyst description.
   For `table_marker` with stitched_table, render an HTML `<table>`.

9. **Final stats line** to stdout:
   ```
   STATS pages=<N> chunks=<N> images=<N> tables=<N> ufo=<N> cryptid=<N> doc_md_bytes=<N>
   ```

## Performance

- Page-rebuilders: parallel batches of 5 (don't exceed 10 concurrent Task spawns).
- After page-rebuilders complete, image-analysts in parallel batches of 5.
- Crop ALL images first via Bash, THEN spawn image-analysts (they need the cropped file on disk).

## Bilingual policy

- Brazilian Portuguese (pt-br), NOT European
- UTF-8 accents preserved: ç, ã, é, í, ó, ú, â, ê, ô, à
- Verbatim quotes stay in source language

## NEVER:

- Fabricate redacted content
- Skip a chunk (lossy reconstruction unacceptable)
- Use chunk types outside the enum defined in page-rebuilder
- Mix multi-page table fragments without invoking table-stitcher
- Output explanatory prose in the final document.md (it's the reconstructed document, not a report)
- Write only document.md without the chunks/ + _index.json — those are required for harness roundtrip