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NguyenHuy190303NguyenHuy190303/EEP/skills/cards-readme/SKILL.md

cards-readme

Skill2 starsChanged 21 days agoMITView on GitHub

Create or overhaul a Hugging Face model card, dataset card or GitHub README, with a first-screen quickstart and every claim traced to its source.

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Only when a chart answers the reader's question: charts, with values from the source table and real rendering/palette checks.

    line 16

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Cards and READMEs

Collect the actual artifact facts before writing. Omit unsupported claims and empty template sections. Every metric needs its dataset, definition and source; preserve license and provenance uncertainty. Quickstart and useful capability information belong on the first screen.

Select the artifact and optional detail

  • Model card: existing references/modelcard_spec.md and references/modelcard_template.md.
  • Dataset card: existing references/datasetcard_spec.md, references/datasetcard_template.md and dataset quality. Media/schema order belongs in the parquet, not just its card. Keep label provenance and reproduction evidence.
  • GitHub README: relevant section of existing references/patterns.md; no Hub-only metadata workflow.
  • For composition/first-screen choices: card composition.
  • Only when a chart answers the reader's question: charts, with values from the source table and real rendering/palette checks.
  • For benchmark claims: benchmark evidence, including what the result does not prove.

Use workflow and gates for collection, validation and final delivery, running only the checks relevant to the artifact. Done when check_card.py passes, every metric names its dataset, definition and source, and the rendered page (plus the dataset viewer and schema, for a dataset) was inspected. An upload or other external write needs the user's authorization for that action. Anchor script paths at this skill's root.

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Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

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  • It worked
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  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

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.

Check your setup, free