No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Dsadd4 · Claude Skill · ★ 144
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AgentFigureGallery safe to install? View the security audit →
AgentFigureGallery AgentFigureGallery is a scientific plotting reference gallery for Claude Code, Codex, Cursor, and other coding agents. It lets an agent search real figure references, lets you mark examples as liked, rejected, or selected in a browser gallery, and exports those choices as a reference bundle for plotting code. Quick install for Codex: The script clones or updates the repository at , creates a Python virtual environment, installs the package, and installs the Codex skill wrapper. After this bootstrap install, run the CLI as , or activate the environment first: For an editable manual insta
| Stars | 144 |
| Forks | 1 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 64.9970834241718/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-05 |
| Created | 2026-05-01 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~16k |
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AgentFigureGallery is Drop-in scientific plotting skill for Claude Code, Codex, Cursor, and other coding agents.. It is categorized as a Claude Skill with 144 GitHub stars.
AgentFigureGallery is primarily written in Python. It covers topics such as agent-skill, agent-tools, ai-agent.
You can find installation instructions and usage details in the AgentFigureGallery GitHub repository at github.com/Dsadd4/AgentFigureGallery. The project has 144 stars and 1 forks, indicating an active community.
AgentFigureGallery is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AgentFigureGallery on Agent Skills Hub include FigMirror, compose-skill, Overture. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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