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Academic Paper Analyzer & Figure Planner

Academic Paper Analyzer & Figure Planner

github.com/azhi-ssDocuments17889 installsUpdated 3w ago
Repository

About Academic Paper Analyzer & Figure Planner

A skill that turns academic papers or repository notes into a prioritized, section-by-section plan for figures. It is intended for researchers planning paper visuals and marks gaps or uncertain inferences rather than inventing missing details.

Key features

  1. 01Section-by-section figure recommendations
  2. 02Priority ranking for figures
  3. 03Completeness and missing-information notes
  4. 04Figure types, visual elements, and aspect ratios
  5. 05Domain-specific planning cues

Use cases

  1. 01Planning figures from a paper draft
  2. 02Drafting visuals from a title and abstract
  3. 03Identifying figure gaps in a method or experiment section

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/azhi-ss/academic-figure-skills --skill academic-paper-analyzer-&-figure-planner

After installing, type/academic-paper-analyzer-&-figure-plannerin Claude Code to confirm it loaded.

Bundled filesreferences/palettes.mdreferences/missing-info-policy.md

Details

From the project README

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: → references/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: — not hex tables; see references/palettes.md
  • module_count_framework: (carried from repo Handoff when present)

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
contentfigure typepriority
end-to-end pipelineOverall Frameworkmust
network / layer structureNetwork Architecturemust
novel module / mechanismModule Detailmust
method variants / baselinesComparison / Ablationstrong
representation / attention behaviorData Behaviorstrong / medium
dense math or lossModule Detailstrong
curves / t-SNE / heatmapsData Behaviormedium

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

Step 3: Count and prioritize
paper classtypical count
top-conference long6–8
short / workshop4–5
journal8–12
arXiv tech report5–7 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.

typeaspectcore elements
Overall Framework16:9input → stages → output; innovation callouts
Network Architecture16:9 / 3:2layers, dims, residuals
Module Detail4:3central mechanism, ops (⊗ ⊕ σ), sparse formula
Comparison / Ablation16:9N×M grid, ours highlighted
Data Behavior4:3 / 1:1multi-panel heatmaps / curves / embeddings

Emit aspect_ratio per figure in the Figure Plan so prompt skills can copy it into the spec.

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
  • Protein / AI4Science: folding pipeline + Evoformer/module detail + MSA or structure behavior
  • GNN / Scientific computing: message-passing module + encoder-processor-decoder framework + rollout trajectories

Sparse-input cases

materialsplan style
title + abstract onlyhigh-level types only; no fake submodules
method without experimentsplan method figures; results as placeholders
partial sectionslocal plan; separate covered vs uncovered
only repo understanding docsystem-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.

Comments

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