huggingface
Skill93 starsChanged 3 months agoMITView on GitHub
Inspect and manage robotics datasets, models, Jobs, and Spaces on Hugging Face.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- G1Joshi/Agent-Skillstheir own version, first seen Oct 13, 2026
- mediause/agent-skillstheir own version, first seen May 11, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- G1Joshi/Agent-Skills★ 15same name
- mediause/agent-skills★ 9same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository32 other files it gives its agents.
- AGENTS.mdAGENTS.md
- learnings/AGENTS.mdAGENTS.md
- skills/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- learnings/CLAUDE.mdCLAUDE.md
- skills/CLAUDE.mdCLAUDE.md
- app-publishingSkill
- architectSkill
- cloud-runSkill
- dataSkill
- environmentsSkill
- foxgloveSkill
- gazeboSkill
- gemini-roboticsSkill
- integrationSkill
- isaac-labSkill
- isaac-simSkill
- learning-loopSkill
- lerobotSkill
- live-demoSkill
- miningSkill
- mujocoSkill
- navigationSkill
- rerunSkill
- ros2Skill
- runpodSkill
- rviz2Skill
- simulationSkill
- skill-authorSkill
- test-assetsSkill
- testingSkill
- visualizationSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
Check the installed hf CLI and the relevant command group's live help.
- Distinctive: line 24
Use an existing authenticated identity or a secure browser/device login.
- Distinctive: line 26
Before creating or uploading, establish the destination owner, repository type, visibility, local source, changed paths, and whether …
- Distinctive: line 36
After a mutation, inspect the remote result rather than trusting the local command's intent.
- Distinctive: line 48
Use the current Hugging Face documentation and live CLI help as the source of truth for volatile flags and services.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Hugging Face
The Hub is an artifact boundary. Know exactly what is crossing it, under which identity and revision, before transferring data or changing remote state.
Inspect first
- Check the installed
hfCLI and the relevant command group's live help. Commands and hosted services evolve faster than this skill. - Inspect the repository card, file tree, revision, license, access state, and transfer size before downloading or planning around an artifact.
- For robot data, also verify embodiment, task, observation/action schema, cameras, timing, episode structure, and format version.
- Pin an immutable revision when the artifact feeds a reproducible build, fixture, evaluation, or training run.
Treat remote changes as real changes
- Use an existing authenticated identity or a secure browser/device login. Do not place tokens in prompts, source, shell history, or logs.
- Before creating or uploading, establish the destination owner, repository type, visibility, local source, changed paths, and whether deletion is involved.
- Starting Jobs is paid remote compute. Establish the current hardware, command, timeout, outputs, secrets, and cost exposure before submission.
- Inspect a Space's metadata, build logs, and runtime logs before restarting or changing hardware.
- A Space is an optional hosted presentation layer, not a requirement for using Hub datasets or models. Most Docker Spaces can also be run locally; keep the application portable when self-hosting or another service is plausible.
- After a mutation, inspect the remote result rather than trusting the local command's intent.
Go deeper only when needed
- For current CLI shapes, Dataset Viewer endpoints, and mutation preflights, read references/hub-operations.md.
- LeRobot owns dataset structure, processors, training, and evaluation; this skill owns the Hub boundary around those artifacts.
- Data owns source selection. Use integration for service packaging, network, health, and self-hosting boundaries; use the relevant deployment skill for provider mechanics when a Space is not the chosen runtime.
- Use the current Hugging Face documentation and live CLI help as the source of truth for volatile flags and services.
Done
- The intended artifact, revision, identity, destination, and remote result are all verified, with no credential exposed and no unapproved paid or destructive action.
Does it work?
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.