create-annotated-pdf
Generate annotated PDFs from extraction pipeline run data. Overlays color-coded bounding boxes (text, tables, figures, equations) from S02/S05/S06 onto the original PDF. Supports single PDF, run directory, batch from blacklist, and interactive review server.
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- grahama1970/agent-stack-public
- 최근 소스 활동
- 2026년 9월 24일 15:51
- 감지된 SKILL.md 언어
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소스 지침 · 읽기 전용 미리보기- name
- create-annotated-pdf
- description
- Generate annotated PDFs from extraction pipeline run data. Overlays color-coded bounding boxes (text, tables, figures, equations) from S02/S05/S06 onto the original PDF. Supports single PDF, run directory, batch from blacklist, and interactive review server.
- triggers
- ["annotate pdf","annotated pdf","show me the annotations","show extraction boxes","give me the annotated pdf","review extraction","spot check extraction","overlay bboxes","visualize extraction","create annotated pdf","review pdf annotations","review failed pdfs","batch annotate"]
- metadata
- {"short-description":"Annotated PDFs from extraction pipeline data"}
- provides
- ["create-annotated-pdf"]
- composes
- ["task-monitor","agentic-evals"]
- disciplines
- ["extraction","content-creation"]
# create-annotated-pdf
Generate annotated PDFs showing what the extraction pipeline detected. Color-coded
bounding boxes for text blocks, tables, figures, equations overlaid on the original PDF.
## Quick Start
### Single PDF (from run directory)
```bash
cd /path/to/extractor
uv run python -m extractor.pipeline.tools.render_annotated_pdf from-run \
--pdf /path/to/original.pdf \
--run-dir /path/to/run_directory \
--out /tmp/annotated.pdf \
--export-pages --png-dpi 144
```
### Single PDF (by stem — auto-discovers run dir)
```bash
cd /path/to/agent-skills/skills/create-annotated-pdf
uv run --script annotate_from_stem.py <stem> [--out /tmp/annotated.pdf] [--png]
```
### Batch (all blacklisted/failed PDFs)
```bash
uv run --script annotate_from_stem.py --batch-blacklist [--out-dir /tmp/annotated_failures/]
```
### Interactive Review Server
```bash
# Terminal 1: API server
cd /path/to/extractor/prototypes/tabbed/api
uv run --script review_server.py
# Terminal 2: Frontend
cd /path/to/extractor/prototypes/tabbed/html
VITE_REVIEW_API=http://127.0.0.1:8003 npm run dev
# Open http://localhost:8080/review
```
## What Gets Annotated
| Stage | Element | Color | Source File |
|-------|---------|-------|-------------|
| S02 | Text blocks | Gray | `02_marker_blocks.json` |
| S02 | Section headers | Orange | `02_marker_blocks.json` |
| S02 | Equations | Purple | `02_marker_blocks.json` |
| S05 | Tables | Blue | `05_tables.json` |
| S06 | Figures | Green | `06_figures.json` |
## Outputs
- **Annotated PDF**: Original PDF with colored bbox rectangles and type labels
- **PNG pages**: Per-page raster images (optional, `--export-pages`)
- **Tabbed-compatible JSON**: Normalized (0-1) box coordinates for interactive editing
- **Corrections JSONL**: Human review corrections saved per-stem
## Run Directory Discovery
The skill searches these locations (NVMe first, then HDD):
1. `$PI_HOME/skills/review-pdf/extracted_runs_staging/`
2. `$ARTIFACT_STORAGE_ROOT/skills/review-pdf/extracted_runs/`
Run directories are named `{stem}_{hash10}` and contain stage outputs as subdirectories.
## Integration Points
- **`/learn-datalake`**: Blacklist provides failed PDF list for batch annotation
- **`/create-classifier`**: Corrections feed back as ground truth training labels
- **`/pdf-lab`**: Convergence loop can request annotated PDFs for diagnosis
- **`/review-pdf`**: Run directories are the primary data source
- **`/memory`**: Corrections can be stored as extraction quality signals
## Dependencies
- `pymupdf` (fitz) — PDF rendering and annotation
- `fastapi` + `uvicorn` — Review server (optional, for interactive mode)
- Tabbed prototype (`prototypes/tabbed/html`) — Interactive UI (optional)
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