data-extractor

# 📊 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 positions