Renders matplotlib plots and conceptual diagrams from your experimental logs and research ideas, with optional vision-model critique loops to catch illegible text or misleading axes. Part of the PaperOrchestra academic paper generation pipeline, runs in parallel with literature review and produces 300 DPI PNGs plus context-aware captions. The heavy lifting happens through either a PaperBanana backbone (if you have it configured) or plain matplotlib fallbacks with academic styling baked in. Costs roughly 20 to 30 LLM calls per paper. Use this when you need publication-ready figures that follow conference template requirements, not when you're just exploring data. The hard rules around DPI, aspect ratios, and color palettes feel rigid until you've had a figure rejected by a LaTeX template.
npx -y skills add ar9av/paperorchestra --skill plotting-agent --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
sickn33/antigravity-awesome-skills
kubesphere/kubesphere
supercent-io/skills-template