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。
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。
Skill directory to analyze: $ARGUMENTS
Create a draft NVIDIA governance skill card for an existing agent skill. The skill gathers source signals, guides the agent to build a grounded JSON context, renders a deterministic markdown card, and checks that human-review markers were removed before submission.
Use this when:
Do NOT use for:
jinja2 is installed before running render_card.py.SKILL.md or skill.md.target_skill_directory plus this skill's references/ and scripts/, writes only to the target skill directory or /tmp/, and shell execution only for the three scripts listed below.SKILL.md completely before running any script.$ARGUMENTS; if omitted, use the current working directory..env, credential files, hidden auth folders, or unrelated repo files; do not write outside the target skill directory or /tmp/.scripts/discover_assets.py against the target. Use the structured signal summary first; if output is truncated, read only targeted files or small excerpts.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.references/style-guide.md for every context field. Use HUMAN-REQUIRED only when no source supports a truthful value.scripts/render_card.py and fix any schema errors before proceeding.scripts/validate_submission.py.{{ ... }} or {% ... %} template fragments.| Script | Purpose | Arguments |
|---|---|---|
scripts/discover_assets.py | Extracts skill files, repo signals, style guide, and template into one discovery report. | <skill_directory> |
scripts/render_card.py | Validates 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.py | Fails if the rendered card still contains VERIFY or SELECT review markers. | <rendered-card.md> |
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"])
| Error | Cause | Solution |
|---|---|---|
directory not found | The target path is wrong or not mounted in the workspace. | Re-run discovery with the absolute path to the skill directory. |
jinja2 not installed | The renderer dependency is missing. | Install jinja2, then re-run render_card.py. |
Context validation failed | Required fields are missing or typed incorrectly. | Fix the context JSON using references/style-guide.md. |
| Unresolved marker failure | VERIFY or SELECT markers remain after review. | Confirm each marked field, prune catalog entries, then re-run validate_submission.py. |
SKILL.md - this file (orchestration)references/style-guide.md - per-context-field guidancereferences/skill-card.md.j2 - exact card layoutreferences/Skill Card Generator License.txt - license text for this skill packagereferences/catalog/limitations.json - canned technical-limitations catalogreferences/catalog/risks.json - canned risk-management catalogscripts/discover_assets.py - discovery and signal extractionscripts/render_card.py - Jinja renderer with context validationscripts/validate_submission.py - pre-submission marker validator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。
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。
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。
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。
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。
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。
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。