本文へ移動
cccskills
無料GitHub で公開

skill-card-generator

Use only to generate or update a governance skill card for a specified existing agent skill directory. Do not use for explaining, listing, comparing, or discussing skill capabilities.

インストール方法を見る

含まれるファイル(14)

  • SKILL.md6.3 KB
  • BENCHMARK.md3.7 KB
  • evals/evals.json11.6 KB
  • references/catalog/limitations.json544 B
  • references/catalog/risks.json405 B
  • references/skill-card.md.j25.7 KB
  • references/style-guide.md22.6 KB
  • scripts/discover_assets.py31.8 KB
  • scripts/render_card.py14.9 KB
  • scripts/validate_submission.py4.4 KB
  • Skill Card Generator Card2.7 KB
  • Skill Card Generator License29.8 KB
  • skill-card.md3.9 KB
  • skill.oms.sig6.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Generate Skill Card

Skill directory to analyze: $ARGUMENTS

Purpose

Create a draft NVIDIA governance skill card for an existing agent skill. The skill gathers source signals, guides the agent to build a grounded JSON context, renders a deterministic markdown card, and checks that human-review markers were removed before submission.

Use this when:

  • A skill directory already exists and needs a new governance card.
  • A changed skill needs its existing card refreshed.
  • A skill owner is preparing legal/safety review material.

Do NOT use for:

  • Explaining, listing, comparing, or discussing skills or skill capabilities.
  • Creating or rewriting the source skill itself.
  • Generating cards for non-skill assets such as models, datasets, containers, or full systems.
  • Signing, publishing, or approving a skill card.
  • Replacing required human legal, safety, or owner review.

Prerequisites

  • Python 3 is available.
  • jinja2 is installed before running render_card.py.
  • The target path is a skill directory containing SKILL.md or skill.md.
  • The agent can write a temporary context JSON file and the rendered card output.
  • Runtime permissions allow reads from target_skill_directory plus this skill's references/ and scripts/, writes only to the target skill directory or /tmp/, and shell execution only for the three scripts listed below.

Instructions

  1. First, read this SKILL.md completely before running any script.
  2. Resolve the target skill directory from $ARGUMENTS; if omitted, use the current working directory.
  3. Stay within the declared permission scope. Do not read .env, credential files, hidden auth folders, or unrelated repo files; do not write outside the target skill directory or /tmp/.
  4. Run scripts/discover_assets.py against the target. Use the structured signal summary first; if output is truncated, read only targeted files or small excerpts.
  5. Build a context JSON file from the structured signal summary first, then from extracted file contents only when needed.
  6. Populate credential_requirements with only two fields: requires_api_key_or_credential and credential_types. Ground the classification in SKILL.md prose documentation — not script inspection alone. For credential_types, use the controlled vocabulary in references/style-guide.md. Never include credential values, assignments, or raw environment variable names.
  7. Follow references/style-guide.md for every context field. Use HUMAN-REQUIRED only when no source supports a truthful value.
  8. Render the card with scripts/render_card.py and fix any schema errors before proceeding.
  9. Review the card manually, remove resolved VERIFY and SELECT markers, then run scripts/validate_submission.py.
  10. Before finishing, confirm the rendered card has no unrendered {{ ... }} or {% ... %} template fragments.

Available Scripts

ScriptPurposeArguments
scripts/discover_assets.pyExtracts skill files, repo signals, style guide, and template into one discovery report.<skill_directory>
scripts/render_card.pyValidates context JSON and renders the skill card from the Jinja template.--context <context.json> --template <skill-card.md.j2> --out <output.md>
scripts/validate_submission.pyFails if the rendered card still contains VERIFY or SELECT review markers.<rendered-card.md>

Examples

Discover signals for a target skill:

run_script("scripts/discover_assets.py", args=["/path/to/target-skill"])

Render a card from the completed context:

run_script(
  "scripts/render_card.py",
  args=[
    "--context", "/tmp/target-skill-context.json",
    "--template", "references/skill-card.md.j2",
    "--out", "/path/to/target-skill/target-skill-card.md"
  ]
)

Validate the reviewed card before submission:

run_script("scripts/validate_submission.py", args=["/path/to/target-skill/target-skill-card.md"])

Limitations

  • The generated card is a draft and must be reviewed by a human owner.
  • Discovery is limited to local files and repo metadata visible from the target path.
  • The renderer validates required context shape, not the legal or safety correctness of field values.
  • Canned limitation and risk catalogs are starting points; remove entries that do not apply.

Troubleshooting

ErrorCauseSolution
directory not foundThe target path is wrong or not mounted in the workspace.Re-run discovery with the absolute path to the skill directory.
jinja2 not installedThe renderer dependency is missing.Install jinja2, then re-run render_card.py.
Context validation failedRequired fields are missing or typed incorrectly.Fix the context JSON using references/style-guide.md.
Unresolved marker failureVERIFY or SELECT markers remain after review.Confirm each marked field, prune catalog entries, then re-run validate_submission.py.

Files in this skill

  • SKILL.md - this file (orchestration)
  • references/style-guide.md - per-context-field guidance
  • references/skill-card.md.j2 - exact card layout
  • references/Skill Card Generator License.txt - license text for this skill package
  • references/catalog/limitations.json - canned technical-limitations catalog
  • references/catalog/risks.json - canned risk-management catalog
  • scripts/discover_assets.py - discovery and signal extraction
  • scripts/render_card.py - Jinja renderer with context validation
  • scripts/validate_submission.py - pre-submission marker validator

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5652026年10月10日 更新

Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5652026年10月10日 更新

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5652026年10月10日 更新

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5652026年10月10日 更新

Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5652026年10月10日 更新

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

日本語の概要は準備中です。原文の説明を表示しています。

NVIDIA/skills3,5652026年10月10日 更新

NVIDIA のスキルをすべて見る

このスキルの問題を報告する