Academic figure architecture extractor

Skill Azhi-ss/academic-figure-skills/academic-figure-architecture-extractor

AI skills for academic paper figures: repo analysis, figure planning, colorblind palettes, JSON figure specs. Install: npx skills add Azhi-ss/academic-figure-skills

Install

npx -y skills add Azhi-ss/academic-figure-skills --skill academic-figure-architecture-extractor

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What its author says it does

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Architecture diagram analysis for academic PDFs or images — structure, components, and redraw parameters for prompt skills. Use when the user wants 架构图分析, extract figures from PDF, or architecture diagram breakdown.

SKILL.md

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Academic Figure Architecture Extractor & Analyzer

Turn paper PDFs or existing architecture images into a structured 架构图分析结果 that handoffs cleanly to color-expert and prompt skills.

Palettes: → ../docs/palettes.md (names only)
Missing info: → ../docs/missing-info-policy.md
Extractor: → scripts/extract_pdf_figures.py

Honest scope

Prefer user-supplied figure images when available.
PDF extraction is a real local helper (pdfimages / PyMuPDF / optional pdftoppm), not a trained detector.
Size filter is heuristic only; architecture vs photo/table is agent judgment.
Palette: recommend names from docs/palettes.md; hex via color-expert when needed.

Input Contract

Prefer: PDF path(s), figure images, domain, venue
Minimum: one PDF or one architecture image
Missing: analyze what exists; list blocked steps

Output Contract — 架构图分析结果

inventory (path/page, size, keep/drop reason)
per-kept-figure structure (components, hierarchy, flow, type)
recommended palette names
redraw parameters for academic-figure-prompt

Steps

Step 1: Obtain images

Images given → index paths.

PDF given → run the helper (from skill dir or repo root):

python3 academic-figure-architecture-extractor/scripts/extract_pdf_figures.py \
  /path/to/paper.pdf -o /tmp/arch-extract/paper

Useful flags:

flag	meaning
`--backend auto\|pdfimages\|pymupdf`	embedded-image backend
`--min-side 300`	drop tiny icons (default)
`--min-pixels 90000`	drop low-res crops
`--pages 4` or `--pages 1-3` or `--pages all`	also rasterize pages via pdftoppm
`--dpi 150`	raster DPI

Read extract-report.json in the out dir (kept / dropped / tools).

If both backends missing → ask user for exported figures; do not invent paths.

Done when: each candidate has a path or page reference, or a clear tool blocker is stated.

Step 2: Filter to architecture-like figures

Start from kept (size-ok). Agent reclassifies:

keep cues	drop cues
boxes + arrows, layered blocks	pure photos, scatter-only, dense tables
structured edges / modules	tiny icons already size-dropped

Unsure → keep + 待确认.

Done when: each image is keep / drop / uncertain with a one-line reason.

Step 3: Structure analysis

For each kept figure:

components (core vs auxiliary)
hierarchy / dataflow
type: Overall Framework / Network Architecture / Module Detail / Comparison
domain notes

Done when: every kept figure has type + component list + flow summary.

Step 4: Palette suggestion + redraw handoff

Map via docs/palettes.md (e.g. ≥4-module framework → Nature Blue; module detail → Blue Monochrome; comparison → ML TopConf Deep).

图类型: ...
核心组件: ...
配色方案名: ...
布局建议: 16:9 | 3:2 | 4:3
风格: white fill, colored borders, flat vector
标注要求: ...

Done when: each kept figure has redraw params + palette names (no hex tables).

Stop

Stop when the report is delivered, extraction is blocked pending user images, or the user only wanted inventory.