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.
git clone --depth 1 https://github.com/voxel51/fiftyone-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/plugins/voxel51/fiftyone-skills/gemini-extension)<a href="https://agentmods.dev/plugins/voxel51/fiftyone-skills/gemini-extension"><img src="https://agentmods.dev/badge/plugins/voxel51/fiftyone-skills/gemini-extension/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/plugins/voxel51/fiftyone-skills/gemini-extension"><img src="https://agentmods.dev/badge/plugins/voxel51/fiftyone-skills/gemini-extension.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
fiftyone-skills 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": "fiftyone-skills",
"description": "FiftyOne computer vision dataset management with 80+ operators for import, export, inference, evaluation, visualization, and plugin development.",
"version": "1.0.3",
"contextFileName": "AGENTS.md",
"mcpServers": {
"fiftyone": {
"command": "fiftyone-mcp",
"args": [],
"cwd": "${workspacePath}"
}
}
}
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 · 14 lines scan A 137fac6687fe
fiftyone-skills is a plugin published in the GitHub repository voxel51/fiftyone-skills (39 stars, last pushed today), licensed Apache-2.0. 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 2026-09-10.
Other plugins, from other repositories
medsci-modeling
Clinical AI model research engineering: choose a paper-grounded architecture, scaffold a reproducible PyTorch training repo, validate the model's split and validation design, document it (Model Card / Datasheet), and evaluate models or LLMs/MLLMs on clinical tasks. Integrates MONAI / nnU-Net, never replaces them.
geoai
18 Agent Skills covering the full geospatial data science lifecycle: data engineering, remote sensing, Earth Engine, deep learning, spatial statistics, geostatistics, terrain, networks, LiDAR, trajectories, change detection, MCDA, PostGIS, cartography, and guarded ArcGIS Pro automation.
geoai-skills
GeoAI-powered skills for Claude Code: inspect geospatial files, download satellite imagery, search STAC catalogs, fetch Overture Maps data, process rasters, run AI object detection, and search session logs.
sciagent-skills
Life sciences computational skills for scientific AI agents — 197 skills covering genomics, proteomics, drug discovery, biostatistics, scientific computing, and scientific writing.
domino-claude-plugin
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams.
claude-autoresearch
Teaches Claude Code how to run autoresearch autonomous ML loops inside a nono security sandbox with GPU access.