pdf-page-extract
Extract rich data from PDF pages including text spans with metadata, rendered PNG images, and page mapping. Creates persistent artifacts for downstream processing.
Best use case
pdf-page-extract is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Extract rich data from PDF pages including text spans with metadata, rendered PNG images, and page mapping. Creates persistent artifacts for downstream processing.
Extract rich data from PDF pages including text spans with metadata, rendered PNG images, and page mapping. Creates persistent artifacts for downstream processing.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "pdf-page-extract" skill to help with this workflow task. Context: Extract rich data from PDF pages including text spans with metadata, rendered PNG images, and page mapping. Creates persistent artifacts for downstream processing.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pdf-page-extract/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pdf-page-extract Compares
| Feature / Agent | pdf-page-extract | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Extract rich data from PDF pages including text spans with metadata, rendered PNG images, and page mapping. Creates persistent artifacts for downstream processing.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# PDF Page Extract Skill
## Purpose
This skill extracts all necessary data from PDF pages to enable accurate AI-driven HTML generation. It produces three critical artifacts:
1. **Rich extraction data** - Text spans with font metadata (sizes, styles, positions)
2. **Rendered PNG image** - Visual reference for AI to understand page layout
3. **Page mapping** - Authoritative mapping of PDF indices to book pages
This is the **deterministic, Python-based foundation** for the entire pipeline. All extracted data is saved to persistent files for traceability and future processing.
## What to Do
1. **Validate input parameters**
- Check PDF file exists and is readable
- Verify page range (PDF indices or book pages)
- Confirm output directory structure
2. **Establish page mapping** (if not already done)
- Run: `python3 Calypso/tools/read_page_footers.py`
- Scans page footers to establish PDF index → book page mapping
- Saves to: `analysis/page_mapping.json`
3. **Extract rich page data** using PyMuPDF and pdfplumber
- Run: `python3 Calypso/tools/rich_extractor.py`
- Extracts text spans with font metadata:
- Font name and size
- Bold/italic flags
- Position (bounding box)
- Color information
- Analyzes page structure to identify:
- Likely headings (by size and style)
- Paragraphs (regular text)
- Potential lists
- Detects tables using pdfplumber
- Saves to: `analysis/chapter_XX/rich_extraction.json`
4. **Render PDF page to PNG**
- Convert page to high-resolution PNG image (300+ DPI)
- Maintains visual fidelity for AI reference
- Saves to: `output/chapter_XX/page_artifacts/page_YY/02_page_XX.png`
5. **Extract embedded images** (if present)
- Run: `python3 Calypso/tools/extract_images.py`
- Extracts all images from page
- Saves: `output/chapter_XX/images/page_YY_image_*.png`
- Creates metadata: `page_YY_images.json`
6. **Validate extraction completeness**
- Verify all files saved correctly
- Check JSON files are valid
- Confirm PNG image is readable
- Validate page mapping consistency
## Input Parameters
```
chapter: <int> - Chapter number (1-8)
start_page: <int> - Starting PDF index (0-based) or page range
end_page: <int> - Ending PDF index (optional if single page)
pdf_path: <str> - Path to PDF file (default: Calypso/PREP-AL 4th Ed 9-26-25.pdf)
output_base: <str> - Output directory (default: Calypso/output)
mapping_file: <str> - Page mapping file (default: Calypso/analysis/page_mapping.json)
```
## Output Structure
### Artifact Files Saved
**Per-page artifacts** (in `output/chapter_XX/page_artifacts/page_YY/`):
- `01_rich_extraction.json` - Text spans with metadata
- `02_page_XX.png` - Rendered PDF page image
- `page_mapping.json` - Shared mapping file (symlink or copy)
**Extraction data** (in `analysis/chapter_XX/`):
- `rich_extraction.json` - Full extraction for all pages in chapter
- `page_6_pattern_analysis.json` - (Optional) Pattern analysis for specific pages
**Images** (in `output/chapter_XX/images/chapter_XX/`):
- `page_XX_image_*.png` - Embedded images from page
- `page_XX_images.json` - Metadata for embedded images
### Rich Extraction JSON Format
```json
{
"page_number": 16,
"pdf_index": 15,
"book_page": 17,
"chapter": 2,
"dimensions": {
"width": 612,
"height": 792
},
"text_spans": [
{
"text": "Rights in Real Estate",
"font": "Arial-BoldMT",
"size": 27.04,
"bold": true,
"italic": false,
"bbox": {
"x0": 72,
"y0": 150,
"x1": 400,
"y1": 177
},
"color": 0,
"sequence": 1
}
],
"analysis": {
"font_sizes": {
"27.04": 1,
"11.04": 45
},
"font_styles": {
"bold_27.04": 1,
"regular_11.04": 45
},
"likely_headings": [
{
"text": "Rights in Real Estate",
"level": 1,
"confidence": 0.95
}
],
"likely_paragraphs": [
{
"text": "Real property consists of...",
"type": "body_text"
}
]
},
"extraction_timestamp": "2025-11-08T14:30:00Z",
"extraction_tool": "rich_extractor.py v1.0"
}
```
## Python Commands to Execute
### Step 1: Establish Page Mapping
```bash
cd Calypso/tools
python3 read_page_footers.py \
--start 15 \
--end 28 \
--pdf "../PREP-AL 4th Ed 9-26-25.pdf" \
--output "../analysis/page_mapping.json"
```
**Success indicators:**
- Command exits with code 0
- Page mapping JSON created/updated
- All pages in range have entries
### Step 2: Extract Rich Data
```bash
cd Calypso/tools
python3 rich_extractor.py \
--pdf "../PREP-AL 4th Ed 9-26-25.pdf" \
--start 15 \
--end 28 \
--output "../analysis/chapter_02/rich_extraction.json"
```
**Success indicators:**
- Command exits with code 0
- JSON file created
- File contains text_spans array
- All pages in range represented
### Step 3: Render to PNG
```bash
cd Calypso/tools
python3 -c "
import fitz
pdf = fitz.open('../PREP-AL 4th Ed 9-26-25.pdf')
for page_idx in range(15, 29):
page = pdf[page_idx]
pix = page.get_pixmap(matrix=fitz.Matrix(3, 3)) # 300% zoom for high-res
pix.save(f'../output/chapter_02/page_artifacts/page_{page_idx:02d}/02_page_{page_idx}.png')
pdf.close()
"
```
### Step 4: Extract Images (if present)
```bash
cd Calypso/tools
# For each page with images
python3 extract_images.py \
--page 17 \
--pdf "../PREP-AL 4th Ed 9-26-25.pdf" \
--output "../output" \
--mapping "../analysis/page_mapping.json"
```
## Quality Checks
Before declaring extraction complete:
1. **File existence**
- [ ] `01_rich_extraction.json` exists
- [ ] `02_page_XX.png` exists and is valid
- [ ] `page_mapping.json` exists
2. **JSON validity**
- [ ] JSON files parse without errors
- [ ] All required fields present
- [ ] No null/undefined values in critical fields
3. **Data completeness**
- [ ] All pages in range have text_spans
- [ ] Text content is not empty
- [ ] Font sizes are reasonable (> 0)
- [ ] Bounding boxes are within page dimensions
4. **Image quality**
- [ ] PNG files are readable
- [ ] Image dimensions match PDF page size
- [ ] No corrupted or blank images
## Error Handling
**If PDF file not found:**
- Exit with error message
- Do not create partial artifacts
**If page mapping fails:**
- Fall back to default indexing (PDF index = book page - 1)
- Log warning
- Continue extraction
**If rich extraction produces no text:**
- Check if page is image-only
- Mark in metadata: `"page_type": "image_only"`
- Continue (ASCII preview will handle image OCR)
**If PNG rendering fails:**
- Use fallback: save raw PDF page as PDF image
- Log warning
- Continue to next step
## Persistence & Traceability
All artifacts include metadata:
- Extraction timestamp
- Tool version
- Input parameters
- Processing status
This enables:
- Reproducibility (re-extract with same parameters)
- Debugging (trace what data was extracted)
- Auditing (track all changes to artifacts)
- Caching (skip re-extraction if unchanged)
## Success Criteria
✓ All required files created in correct directories
✓ Rich extraction JSON is valid and complete
✓ PNG image renders correctly
✓ Page mapping is accurate
✓ All data persisted and ready for next skill
✓ No extraction errors or warnings
## Next Steps
Once extraction completes successfully:
1. **Skill 2** will create ASCII preview from extracted data
2. **Skill 3** will use extraction + PNG + ASCII for HTML generation
3. All artifacts available for validation and debugging
## Troubleshooting
**PDF won't open**: Verify file path, ensure PDF is not corrupted
**No text extracted**: Page may be image-only (OCR needed)
**Wrong page numbers**: Check page_mapping.json for accuracy
**PNG images are blank**: Try increasing zoom factor (3x = 300 DPI)
## Implementation Notes
- This skill is **fully deterministic** - same inputs always produce same outputs
- Python tools ensure data quality and consistency
- All files saved to persistent storage for audit trail
- No AI involved at this stage - pure data extraction
- Ready to support later AI-based HTML generation with complete contextRelated Skills
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