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academic-plotting SKILL.md skillA

This file is byte-identical to the first copy the registry indexed, one of 3 indexed copies. Same content hash, so the same checks give both the same grade.

academic-plotting from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned

mdr add orchestra-research/ai-research-skills/academic-plotting@v1.0.0

Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.

Audit grade: A, 17 of 17 checks passed
Kind: SKILL.md skill
Repository: orchestra-research/ai-research-skills
Path: 20-ml-paper-writing/academic-plotting/SKILL.md
Versions recorded: 1
Latest version: v1.0.0, committed Mar 25, 2026
Size: 21,149 bytes
Repository stars: 13,718
License: MIT

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Academic Plotting for ML Papers

Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:

1. **Diagram figures** (architecture, system design, workflows, pipelines) — AI image generation via Gemini
2. **Data figures** (line charts, bar charts, scatter plots, heatmaps, ablations) — matplotlib/seaborn

## When to Use Which Workflow

| Figure Type | Tool | Why |
|-------------|------|-----|
| Architecture / system diagram | Gemini (Workflow 1) | Complex spatial layouts with boxes, arrows, labels |
| Workflow / pipeline / lifecycle | Gemini (Workflow 1) | Multi-step processes with connections |
| Bar chart, line plot, scatter | matplotlib (Workflow 2) | Precise numerical data, reproducible |
| Heatmap, confusion matrix | matplotlib/seaborn (Workflow 2) | Structured grid data |
| Ablation table as chart | matplotlib (Workflow 2) | Grouped bars or line comparisons |
| Pie / donut chart | matplotlib (Workflow 2) | Proportional data (use sparingly in ML papers) |
| Training curves | matplotlib (Workflow 2) | Loss/accuracy over steps/epochs |
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add orchestra-research/ai-research-skills/academic-plotting@sha256:6613653d8109dcae

The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

[![mdr](https://markdownregistry.com/badge/art_2fur6pjmabjvchit.svg)](https://markdownregistry.com/a/art_2fur6pjmabjvchit)

3 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Mar 25, 2026.

version	committed	commit	size	audit
v1.0.0 latest	2026-03-25	6710bc7	21,149 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (21149 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_2fur6pjmabjvchit
GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/academic-plotting
GET https://markdownregistry.com/api/v1/blob/6613653d8109dcae36596b37d7414d5ae394a18f81543365222286d04485fd83

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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