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Academic Plotting for ML Papers

Makes publication-style architecture diagrams and data charts for ML conference papers

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
4 files

What it does

When installed, the agent follows a structured playbook for turning a paper's method description or experiment results into figures. For diagrams (architecture, pipelines) it writes detailed image-generation prompts for Google's Gemini image model, using a consistent visual style. For data (bar, line, scatter, heatmap charts) it picks a suitable chart type and writes matplotlib/seaborn code, highlighting the paper's own method in a distinct colour.

Great for

  • ML researchers preparing figures for NeurIPS, ICML, ICLR or similar venues
  • Students writing a paper who need consistent, publication-style charts from their results
  • Anyone who wants a Python plotting script generated from a table of numbers

Skip it if

  • Final, camera-ready figures without review: generated diagrams can misstate components or labels, so the author must check every element against the paper
  • Non-academic presentation graphics where the academic style conventions do not matter
  • Work involving confidential unpublished results sent to an external image service, since diagram generation relies on Gemini, which is a network call

Try asking

  • “Here is my method section; draw an architecture diagram of the Planner, Executor and Verifier components.”
  • “These are my MMLU and HumanEval results for three models; make a grouped bar chart highlighting our method.”
  • “Plot the training loss curves from this CSV for the ablation study in the paper.”
  • “Create a consistent hand-drawn style overview figure for my NeurIPS submission.”
Step-by-step guide: how to use Academic Plotting for ML PapersInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 4 original files, the MIT licence and an attribution note.

    academic-plotting.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip academic-plotting.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip academic-plotting.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Provide the actual paper text or exact numbers rather than a vague request, so labels and values match your paper.
  • Choose one visual style per paper and reuse it so all figures stay consistent.
  • Always verify generated diagrams and charts against your source data before submission.

What's inside

  • references/data-visualization.md
  • references/diagram-generation.md
  • references/style-guide.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.