ReddRadar
Agent skill
fixture-table
Generate PDF tables using ReportLab Table class for proper structure detection. Creates tables that Marker and other extractors can actually parse, unlike raw PDF drawing commands. Use when user needs detectable tables in test fixtures.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/fixture-table
Metadata
Additional technical details for this skill
- short description
- ReportLab tables for extraction testing (Marker-detectable)
SKILL.md
Fixture Table Skill
Generate PDF tables using ReportLab's Table class that extractors (Marker, Camelot) can detect.
Why This Exists
PyMuPDF raw drawing commands (draw_line, insert_text) create visual tables but not proper PDF table structures. Marker and other extractors cannot detect these as tables.
ReportLab's Table class creates proper PDF table structures with cell boundaries that extraction tools recognize.
Quick Start
cd .codex/skills/fixture-table
# Generate from preset (simple, medium, complex)
uv run generate.py preset simple
uv run generate.py preset complex --output my_table.pdf
# Generate with custom data
uv run generate.py generate --output custom.pdf \
--columns "ID,Name,Status" \
--rows '[["1","Alice","Active"],["2","Bob","Pending"]]'
# Borderless table
uv run generate.py generate --no-border --output borderless.pdf
# Thick borders (3pt)
uv run generate.py generate --line-width 3 --output thick.pdf
# Render as image (for scanned document testing)
uv run generate.py preset medium --as-image --dpi 200
# Generate all examples
uv run generate.py example
Commands
preset - Generate from preset complexity
uv run generate.py preset <name> [options]
| Preset | Description |
|---|---|
simple |
3x3 basic table |
medium |
8-row requirements matrix with 6 columns |
complex |
Multi-index headers with column spans |
generate - Create custom table
uv run generate.py generate [options]
Options:
| Option | Short | Description | Default |
|---|---|---|---|
--output |
-o |
Output PDF/PNG path | table_fixture.pdf |
--columns |
-c |
Comma-separated column headers | ID,Name,Value |
--rows |
-r |
JSON array of row data | 3 sample rows |
--spec |
-s |
JSON spec file path | - |
--title |
-t |
Table title text | - |
--style |
grid, plain, colored |
grid |
|
--border/--no-border |
Enable or disable borders | --border |
|
--line-width |
-w |
Border thickness in points | 1.0 |
--multi-index |
-m |
JSON multi-index headers | - |
--as-image |
-i |
Render as PNG image | False |
--dpi |
Image resolution | 150 |
example - Generate all examples
uv run generate.py example
Creates examples/ directory with all presets, styles, and border variations.
Multi-Index Tables
For hierarchical headers that span columns:
uv run generate.py generate \
--columns "ID,Q1,Q2,Q3,Q4,Total" \
--rows '[["A","10","20","30","40","100"]]' \
--multi-index '[[{"text":"","span":1},{"text":"2024 Quarters","span":4},{"text":"","span":1}]]'
Or in JSON spec:
{
"columns": ["ID", "Q1", "Q2", "Q3", "Q4", "Total"],
"rows": [["A", "10", "20", "30", "40", "100"]],
"multi_index": [
[{"text": "", "span": 1}, {"text": "2024 Quarters", "span": 4}, {"text": "", "span": 1}]
]
}
Scanned Document Simulation
Generate table as a rasterized PNG image (for testing OCR/VLM extraction):
# 150 DPI (default)
uv run generate.py preset medium --as-image
# Higher quality (300 DPI)
uv run generate.py preset complex --as-image --dpi 300 --output scanned_table.png
Verification
Test that Camelot detects the table:
uv run generate.py preset simple --output test.pdf
python -c "
import camelot
tables = camelot.read_pdf('test.pdf', pages='1', flavor='lattice')
print(f'Tables found: {len(tables)}')
if tables:
print(tables[0].df)
"
Dependencies
Self-contained via uv run - no pre-installation needed.
[project]
dependencies = [
"reportlab>=4.0.0",
"typer>=0.9.0",
"pymupdf>=1.23.0",
]
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