/data-extractor

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.
license: MIT
metadata:
  version: 0.1.0
  openclaw:
    requires:
      bins:
      - python3
      env:
      - ANTHROPIC_API_KEY
    always: false
    emoji: 📊
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: anthropic
    - kind: pip
      package: opencv-python-headless
    - kind: pip
      package: numpy
    - kind: pip
      package: Pillow
    - kind: pip
      package: pydantic
    - kind: pip
      package: fastapi
    - kind: pip
      package: uvicorn

📊 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)

1. **Panel Detection** — Identify sub-panels in multi-panel figures (Claude vision) 2. **Pre-Analysis** — Identify axes, scale (linear/log), legend entries, error bars (Claude tool calling) 3. **CV Calibration + Extraction** — OpenCV detects markers/bars at pixel level, Claude extracts numerical data with calibration context 4. **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 data_extractor_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