skillZs
★ LIVE SKILL TAGS ★
>>> LIVE SKILLS INDEX <<<
* OPEN SOURCE *
NO LOGIN, NO TRACKING
※ REAL INSTALL DATA ※
← back to all skills
shzhao27208/aut_sci_write222 installs

sci-figure

Extracts figures and sub-figures from academic PDF papers. Supports Fig/Figure, Scheme, Chart, Supplementary Figure, Extended Data Figure (Nature), and Chinese equivalents (图/方案/示意图/附图/补充图). Sub-figure label recognition supports (a)/(A)/a)/(i)/(1)/a. formats. High-quality PNG output at configurable DPI. Use when user asks to "extract figure", "截取文献图片", "提取子图", "get figure from paper", "Scheme", "方案图", "补充图", "Supplementary Figure", or "Extended Data".

How do I install this agent skill?

npx skills add https://github.com/shzhao27208/aut_sci_write --skill sci-figure
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill is safe to use. It provides functionality to extract scientific figures from academic PDF papers using established Python libraries. The analysis identified a standard indirect prompt injection surface typical of document-processing tools and minor non-functional control characters in the documentation.

  • Socketwarn

    1 alert: gptAnomaly

  • Snykpass

    Risk: LOW · No issues

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

Sci-Figure — Scientific Figure Extractor

Precisely extract figures and sub-figures from academic PDF papers.

License note: sci-figure is licensed under AGPL-3.0-or-later because it links PyMuPDF (fitz), which is AGPL-licensed.

Installation

Install the package from the skill directory before first use:

cd ${SKILL_DIR}
pip install -e .

This registers the sh-sci-fig CLI command. Requires Tesseract OCR:

  • Windows: winget install UB-Mannheim.TesseractOCR
  • Linux: apt install tesseract-ocr
  • macOS: brew install tesseract

Preferences (EXTEND.md)

Use Bash to check EXTEND.md existence (priority order):

# Check project-level first
test -f .baoyu-skills/sci-figure/EXTEND.md && echo "project"

# Then user-level (cross-platform: $HOME works on macOS/Linux/WSL)
test -f "$HOME/.baoyu-skills/sci-figure/EXTEND.md" && echo "user"

EXTEND.md Supports: Default DPI | Default output format | Tesseract path

Usage

sh-sci-fig <input.pdf> [options]

Options

OptionShortDescriptionDefault
<input>PDF file pathRequired
--figure-fFigure number (1, 2, 3...)Required (except --list/--all)
--subfigure-sSub-figure label (a, b, c...)None (returns whole figure)
--output-oOutput directoryCurrent directory
--dpi-dOutput resolution600
--list-lList all available figure numbersfalse
--allExtract all figuresfalse
--formatOutput format (png/jpg)png
--strategyExtraction strategy: hybrid/native/cvhybrid
--ocrOCR engine: tesseract/easyocr/nonetesseract
--render-pageRender full page with annotationsfalse
--annotateDraw bounding boxes on rendered pagefalse
--bboxManual bbox override (x0,y0,x1,y1 in px)None
--no-trimDisable whitespace trimmingfalse
--debugEnable debug loggingfalse
--quiet-qSuppress info messagesfalse

Examples

# Extract Figure 2, sub-figure c
sh-sci-fig paper.pdf -f 2 -s c

# Extract entire Figure 3
sh-sci-fig paper.pdf -f 3

# List all available figures in a PDF
sh-sci-fig paper.pdf --list

# Extract all figures
sh-sci-fig paper.pdf --all

# Custom output directory and DPI
sh-sci-fig paper.pdf -f 2 -s c -o ./output/ -d 300

# Use EasyOCR for sub-figure label detection
sh-sci-fig paper.pdf --all --ocr easyocr

# CV-only strategy (skip native extraction)
sh-sci-fig paper.pdf --all --strategy cv

# Render page with annotated bounding boxes (debugging)
sh-sci-fig paper.pdf -f 1 --render-page --annotate

# Manual bbox extraction (multimodal correction)
sh-sci-fig paper.pdf -f 1 --bbox 100,200,800,1200

Output:

Extracted: figure_2c.png (1920x1080, 600 DPI)

Error Handling

ScenarioBehavior
Figure number not foundError + list all available figure numbers
OCR recognition failedReturn entire figure region
Sub-figure split failedReturn entire figure region
No sub-figure labels foundReturn entire figure region

Tech Stack

LibraryRole
pdfplumberText + coordinate extraction (caption detection)
PyMuPDF (fitz)Native image extraction + high-quality page rendering
opencv-pythonCV region detection, connected-component analysis, content validation
PillowFinal cropping, format conversion
pytesseractOCR for sub-figure label recognition (default)
easyocrAlternative OCR engine (optional, pip install sci-figure[ocr])
numpyImage array operations

Extraction Engines (v2)

EnginePriorityBest For
Native (PyMuPDF)1stRaster images embedded in PDF
CV (connected-component)2ndVector graphics, colored plots
Caption-anchored3rdFallback when above engines fail

The hybrid strategy (default) tries all three in order and validates results.

Detected Figure Fields

Each figure returned by FigureExtractor.detect_all() is a dict with these keys:

FieldTypeDescription
numberintFigure number
pageintPage index (0-based)
bbox_pdftupleCrop region in PDF points (x0, y0, x1, y1)
bbox_pxtupleCrop region in pixels (x0, y0, x1, y1)
caption_textstrFull caption text
figure_typestrOne of: figure, scheme, chart, supplementary, extended_data
sublabelslist[str]Sub-figure labels, e.g. ["a","b","c"]
imagendarrayCropped figure image (numpy array)
engine_usedstrEngine that produced the crop: native, cv, or fallback

list_figures() returns the same dicts without the image field.

Extension Support

Custom configurations via EXTEND.md. See Preferences section for paths and supported options.


© License & Copyright

Aut_Sci_Write — Autonomous Scientific Writer

  • Author: Shuo Zhao
  • License: MIT License
  • Copyright: © 2026 Shuo Zhao. All rights reserved.
  • Original Work: This is an original work created by the author. No reproduction, redistribution, or commercial use without explicit permission. Permission is hereby granted, free of charge, to any person obtaining a copy of this software... (See the LICENSE file in the root directory for the full MIT terms.)

This skill is part of the Aut_Sci_Write suite. For full license terms, see the LICENSE file in the project root.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/shzhao27208/aut_sci_write/sci-figure">View sci-figure on skillZs</a>