Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add plugins/neuromechanist/research-skills/figures/plugin marketplace add neuromechanist/research-skillsgit clone --depth 1 https://github.com/neuromechanist/research-skillsGrade A, and why
figures scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
{
"name": "figures",
"version": "0.11.0",
"description": "Publication-quality figures plugin. Seven skills + one QA agent. scientific-figure: svgutils composer with pre-export font-size validation against Nature/Science/Cell/PNAS minima and Inkscape/cairosvg export. transparent-icons: flat scientific icons via Codex CLI or OpenAI Images API with Pillow-threshold or rembg/BiRefNet transparency post-process. svg-figure: hand-authored or programmatic SVG schematics with text-alignment, arrow-pattern, and palette-compliance guidelines, plus an Illustrator/Affinity editor-handoff reference and the editor_prep.py handoff pass. svg-primitives: mm-precise SVG builder in Python; auto-fits text boxes from measured font metrics, snaps arrow endpoints to box edges via svgpathtools intersection, emits <marker orient='auto'> for tangent-correct arrowheads on curves and orthogonal turns, supports straight/cubic/orthogonal-h/orthogonal-v routing plus multi-waypoint paths and rounded corners, and ships Bracket/Annotation/Group primitives. ai-full-figure: AI-generated pictorial substrate plus programmatic label/arrow/scale-bar overlay; hard-ceiling rules route complex figures back to scientific-figure or svg-figure. plot-styling: library decision tree (matplotlib / seaborn / plotnine / plotly / PyVista) with SciencePlots recipes for Nature, IEEE, Science, Cell, PNAS, APS. figure-qa agent: type-dispatching QA across SVG / raster / plot-script / composed-figure inputs with strict programmatic-vs-VLM separation.",
"author": {
"name": "Seyed Yahya Shirazi",
"email": "[email protected]"
},
"license": "BSD-3-Clause",
"skills": "./skills/",
"keywords": ["figures", "publication", "svgutils", "icons", "gpt-image", "scientific", "qa", "matplotlib"]
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 13 lines scan A f79f4e39c2fd
figures is a plugin published in the GitHub repository neuromechanist/research-skills (43 stars, last pushed 2d ago), licensed BSD-3-Clause. Its token cost is not measured: this kind of file is read by the harness, not the model. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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