Borrowing it
Nothing to install: this file belongs to voxel51/fiftyone-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/voxel51/fiftyone-skills/main/AGENTS.mdgit 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/instructions/voxel51/fiftyone-skills/agents-md)<a href="https://agentmods.dev/instructions/voxel51/fiftyone-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/voxel51/fiftyone-skills/agents-md/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/instructions/voxel51/fiftyone-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/voxel51/fiftyone-skills/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.05844 | $0.05844 |
| Opus 5 | $0.02922 | $0.02922 |
| Sonnet 5 | $0.01169 | $0.01169 |
| Haiku 4.5 | $0.00584 | $0.00584 |
Grade A, and why
fiftyone-skills AGENTS.md 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 9d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 598 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FiftyOne Skills - Agent Instructions
This repository contains skills for computer vision workflows using FiftyOne and the FiftyOne MCP Server.
Available Skills
FiftyOne Dataset Import (fiftyone-dataset-import/)
When to use: User wants to import datasets from local files, Hugging Face Hub, or any supported format (COCO, YOLO, VOC, KITTI, etc.), including multimodal grouped datasets.
Instructions: Load the skill file at skills/fiftyone-dataset-import/SKILL.md
Key requirements:
- FiftyOne MCP server must be running
@voxel51/ioplugin for importing data@voxel51/utilsplugin for dataset managementhuggingface_hubpackage for HF Hub imports
Workflow summary:
- Scan directory or HF Hub to detect media and labels
- Auto-detect format (COCO, YOLO, VOC, parquet, FiftyOne, etc.)
- Confirm findings with user
- Create dataset and import samples
- For HF Hub: use
load_from_hub()orsnapshot_download() - Validate import count
- Launch App to view
Supported sources:
- Local directories with media files
- COCO, YOLO, VOC, KITTI, CVAT annotations
- Hugging Face Hub (FiftyOne-formatted, parquet, or raw formats)
- Multimodal grouped datasets (autonomous driving)
FiftyOne Dataset Export (fiftyone-dataset-export/)
When to use: User wants to export datasets to standard formats, share on Hugging Face Hub, convert between formats, or create training data archives.
Instructions: Load the skill file at skills/fiftyone-dataset-export/SKILL.md
Key requirements:
- FiftyOne MCP server must be running
@voxel51/ioplugin for exporting datahuggingface_hubpackage for HF Hub exports
Workflow summary:
- Load dataset and review with
dataset_summary() - Confirm export format and destination
- For local: use
export_samplesoperator - For HF Hub: use
push_to_hub()function - Verify exported file counts
Supported destinations:
- Local directories (COCO, YOLO, VOC, CVAT, CSV, etc.)
- Hugging Face Hub (public or private repos)
- FiftyOne Dataset format (full backup with brain runs)
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.
- 9d ago First seen · 598 lines · 5,844 tokens per session scan A 741985aa3152
fiftyone-skills AGENTS.md is an instructions file published in the GitHub repository voxel51/fiftyone-skills (39 stars, last pushed 23d ago), licensed Apache-2.0. It adds 5,844 tokens to every session, about $0.0292 per session on Opus 5. 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.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.