Agent Skill
Figure Composer skill
figure-composeraipoch/open-scienceresources/skills/figure-composer/SKILL.md
Compose one publication-grade multi-panel figure. Start from a one-line claim plus immutable data Artifact Version references, or inspect an existing figure and draft its outline directly. Plan a 12-column panel outline, delegate one worker per panel, compose and inspect the result, then run at most three adversarial review rounds while regenerating only affected panels. For a standalone plot use `figure-style`; for whole-paper figure ordering use `paper-narrative`.
always on
93 tokname + description
once used
2,531 tokSKILL.md body
bundled files
21 scripts
repo stars
5.3kGitHub
Install the Figure Composer skill
Skills CLI (Claude Code, Codex, Cursor and others)
npx skills add https://github.com/aipoch/open-science --skill figure-composer
Claude Code, by hand (all projects)
git clone --depth 1 https://github.com/aipoch/open-science.git /tmp/open-science mkdir -p ~/.claude/skills cp -R /tmp/open-science/resources/skills/figure-composer ~/.claude/skills/figure-composer
For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.
What it costs your context
Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.
| When | What loads | Cost |
|---|---|---|
| Every session | Name and description | 93 tok |
| When the agent uses it | The SKILL.md instructions | 2,531 tok |
| Only if opened | 2 bundled files (18 KB) | varies |
Until it is used, figure-composer costs about 15× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 25 skills from aipoch/open-science would load 2,130 tokens of descriptions. Skills vs MCP study
Checks
- Ships scripts the agent can run. Read them before installing.
Checked 2026-10-22 · method
What's inside
The headings of its SKILL.md. Read the full file on GitHub.
- Figure Composer — narrative → panels → compose → adversarial loop
- Open-Science Notebook call
- Inputs
- Entry points
- 1. Narrative → panel outline
- 2. Fan out panel workers
- 3. Compose and bind the producer Run
- 3.5 Look before review
- 4. Adversarial review loop
- Anti-patterns
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