Academic figure paper analyzer

Skill Azhi-ss/academic-figure-skills/academic-figure-paper-analyzer

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-paper-analyzer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Figure plan for academic papers — section-to-figure mapping, types, counts, and priority. Use when the user wants paper figure planning, 论文配图规划, or which figures a paper needs.

SKILL.md

3.7 KB, as published. Nobody here has run it

Academic Paper Analyzer & Figure Planner

Produce an executable Figure Plan. No palette tables here — hand venue/domain/figure types to color-expert later.

Missing info: → ../docs/missing-info-policy.md

Input Contract

Prefer: paper PDF/LaTeX/Word, section drafts, abstract, method/experiments, repo quick-understanding doc, extracted architecture notes
Minimum: title+abstract, or one method/experiment section, or a repo understanding doc
Missing: partial plan with 推断 / 待确认

Output Contract — Figure Plan

paper overview (topic, contributions)
completeness block
per-section figure recommendations
priority ranking (must / strong / nice)
palette: style family hint (classic vs pastel) + venue/domain/module-count — not hex tables; see ../docs/palettes.md

Steps

Step 1: Parse structure

Map sections: Intro, Method (+ sub), Experiments, Analysis. Note missing sections.

Done when: section list exists and each is marked present / absent / partial.

Step 2: Mark figure-worthy content

content	figure type	priority
end-to-end pipeline	Overall Framework	must
network / layer structure	Network Architecture	must
novel module / mechanism	Module Detail	must
method variants / baselines	Comparison / Ablation	strong
representation / attention behavior	Data Behavior	strong / medium
dense math or loss	Module Detail	strong
curves / t-SNE / heatmaps	Data Behavior	medium

Done when: every must-level contribution has at least one figure entry or an explicit “insufficient evidence” note.

Step 3: Count and prioritize

paper class	typical count
top-conference long	6–8
short / workshop	4–5
journal	8–12
arXiv tech report	5–7 flexible

Done when: total count + must/strong/nice table is filled.

Step 4: Emit Figure Plan report

Include per-section: type × count, why, must-appear visual elements, aspect ratio hint.

type	aspect	core elements
Overall Framework	16:9	input → stages → output; innovation callouts
Network Architecture	16:9 / 3:2	layers, dims, residuals
Module Detail	4:3	central mechanism, ops (⊗ ⊕ σ), sparse formula
Comparison / Ablation	16:9	N×M grid, ours highlighted
Data Behavior	4:3 / 1:1	multi-panel heatmaps / curves / embeddings

Done when: report matches Output Contract and completeness block is honest.

Domain packs (optional cues)

CV: framework + arch + module + visual comparison + attention maps
NLP: framework + transformer arch + attention module + metrics + embeddings
RL/Robotics: state→policy→action loop + networks + trajectories
Medical: imaging pipeline + U-Net/ViT + qualitative grid + ROC/features

Sparse-input cases

materials	plan style
title + abstract only	high-level types only; no fake submodules
method without experiments	plan method figures; results as placeholders
partial sections	local plan; separate covered vs uncovered
only repo understanding doc	system-centric draft; flag narrative review needed

Stop

Stop when the Figure Plan for available materials is delivered. Do not generate prompts unless the user asks.