| name | data-extractor |
| description | Extract numerical data from scientific figure images using Claude vision + OpenCV calibration. Supports 26+ plot types including bar charts, scatter plots, forest plots, Kaplan-Meier curves, box plots, and more. |
| version | 0.1.0 |
| metadata | {"openclaw":{"requires":{"bins":["python3"],"env":["ANTHROPIC_API_KEY"],"config":[]},"always":false,"emoji":"📊","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"install":[{"kind":"pip","package":"anthropic","bins":[]},{"kind":"pip","package":"opencv-python-headless","bins":[]},{"kind":"pip","package":"numpy","bins":[]},{"kind":"pip","package":"Pillow","bins":[]},{"kind":"pip","package":"pydantic","bins":[]},{"kind":"pip","package":"fastapi","bins":[]},{"kind":"pip","package":"uvicorn","bins":[]}]}} |
📊 Data Extractor
You are the Data Extractor, a ClawBio skill for digitizing scientific figures. Your role is to extract numerical data from plot images for meta-analyses and systematic reviews.
When to Use This Skill
Route to this skill when the user:
- Provides an image file (PNG, JPG, TIFF) containing a scientific figure
- Asks to "extract data from a figure", "digitize a plot", "read values from a chart"
- Mentions "meta-analysis data extraction" or "figure digitization"
- Wants to convert a bar chart, scatter plot, or other figure to CSV/JSON
Capabilities
Supported Plot Types (26)
scatter, bar, line, box, violin, histogram, heatmap, forest, kaplan_meier,
dot_strip, stacked_bar, funnel, roc, volcano, waterfall, bland_altman,
paired, bubble, area, dose_response, manhattan, correlation_matrix,
error_bar, table, other
Pipeline (4 phases)
- Panel Detection — Identify sub-panels in multi-panel figures (Claude vision)
- Pre-Analysis — Identify axes, scale (linear/log), legend entries, error bars (Claude tool calling)
- CV Calibration + Extraction — OpenCV detects markers/bars at pixel level, Claude extracts numerical data with calibration context
- Validation — Heuristic checks for axis range, series count, error bar polarity
Output Formats
- CSV — One row per data point with series name, x/y values, error bars
- JSON — Structured ExtractedData objects with full metadata
- Web UI — Interactive table + SVG preview with editable cells
Usage
CLI
python data_extractor.py --image figure.png --output results/
python data_extractor.py --web --port 8765
python data_extractor.py --demo
API (importable)
from api import run
result = run(options={"image_path": "figure.png", "output_dir": "results/"})
Web UI
Launch with --web flag. Upload images, draw boxes around plots, extract and edit data interactively.
Input Formats
- PNG, JPG, JPEG, TIFF image files
- Screenshots from papers, posters, slides
- Multi-panel composite figures (auto-detected and split)
Notes
- Requires ANTHROPIC_API_KEY environment variable
- Uses Claude Sonnet for pre-analysis/detection, Claude Opus for extraction
- OpenCV calibration improves accuracy for scatter/bar plots with clear markers
- Error bars are reported as ± extent (delta from mean), not absolute positions
