Skillshub data-extractor

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.

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/ClawBio/ClawBio/data-extractor" ~/.claude/skills/comeonoliver-skillshub-data-extractor && rm -rf "$T"

manifest: skills/ClawBio/ClawBio/data-extractor/SKILL.md

source content

📊 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