data-extractor
Extract numerical data from scientific figure images using Claude vision + OpenCV calibration.
Skill by beita6969
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/beita6969-data-extractor
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data, research
Reviews
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What's inside
- Skill 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
- Instructions
- 61 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (8)
- SKILL.md
- api.py
- data_extractor.py
- test_extractor.py · tests/
- app.js · web/
- index.html · web/
- server.py · web/
- styles.css · web/
SKILL.md (first lines)
# 📊 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
```bash
python data_extractor.py --image figure.png --output results/
python data_extractor.py --web --port 8765
python data_extractor.py --demo
```
### API (importable)
```python
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 positionsFrom beita6969/ScienceClaw, MIT.
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Signals
- Stars
- 1,019
- Forks
- 109
- Open issues
- 1
- Last commit (this folder)
- Mar 12, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/beita6969/ScienceClaw
- Website
- scienceclaw.science
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
data-extractor skill. Agent Mart. https://agentmart.dev/skills/beita6969-data-extractor (last updated 2026-10-24)