Use when the user has scientific data (or a prompt alluding to scientific data) and wants a publication-quality figure made from it. A generator drafts and renders a figure that lands a frozen communication goal; an adversarial critic critiques it hard and grades it 1-5 per axis against a fixed rubric (message, aesthetic, clarity, integrity, and a conditional domain-completeness axis), aggregates to 0-100, and decides pass; the generator revises against the critic's findings until the grade clears a threshold or the budget is hit. Both roles may consult the literature (Semantic Scholar + arXiv) to verify domain content (e.g. a pathway figure's gene set, or a benchmark's reported numbers), and conform to / grade against a named journal's figure spec fetched via web search. Not for writing a paper or analyzing a dataset, and not for editing an existing finished image — this renders a figure from data/brief and iterates on it.
$npx -y skills add gaasher/agent-loop-skills --skill scientific-figureInstalls into the current project.
Run `npx skills use "https://github.com/gaasher/agent-loop-skills" --skill "gaasher/agent-loop-skills/scientific-figure"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/gaasher/agent-loop-skills" that are relevant to the current task. Run `npx skills add "https://github.com/gaasher/agent-loop-skills"` and select the relevant skills, then follow their instructions.