Skill Inspector
NVIDIA/SkillSpector
Decides whether an agent skill is safe to install by combining a SkillSpector static scan with the agent's own source review, ending in APPROVE, CAUTION or REJECT.
Run tao-daft validate to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors.
$ npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills tao-validate-dataset-format --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tao-validate-dataset-format .claude/skills/tao-validate-dataset-format && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tao-validate-dataset-format" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-format into .claude/skills/tao-validate-dataset-format/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-validate-dataset-format", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-formatType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills tao-validate-dataset-format --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tao-validate-dataset-format .agents/skills/tao-validate-dataset-format && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tao-validate-dataset-format" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-format into .agents/skills/tao-validate-dataset-format/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-validate-dataset-format", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills tao-validate-dataset-format --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tao-validate-dataset-format .cursor/skills/tao-validate-dataset-format && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tao-validate-dataset-format" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-format into .cursor/skills/tao-validate-dataset-format/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-validate-dataset-format", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/tao-validate-dataset-format--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills tao-validate-dataset-format --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tao-validate-dataset-format .gemini/skills/tao-validate-dataset-format && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tao-validate-dataset-format" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-format into .gemini/skills/tao-validate-dataset-format/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-validate-dataset-format", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills tao-validate-dataset-formatInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tao-validate-dataset-format .github/skills/tao-validate-dataset-format && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tao-validate-dataset-format" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-format into .github/skills/tao-validate-dataset-format/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-validate-dataset-format", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills tao-validate-dataset-format --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tao-validate-dataset-format .opencode/skills/tao-validate-dataset-format && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tao-validate-dataset-format" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-validate-dataset-format into .opencode/skills/tao-validate-dataset-format/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-validate-dataset-format", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
tao-validate-dataset-formatRun tao-daft validate to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors.
Tao Validate Dataset Format is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run tao-daft validate to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do not use for non-DAFT formats. Use when the user asks to validate a DAFT dataset, check DAFT schema, validate a TAO dataset format, or run tao-daft validate.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files (for example `BENCHMARK.md`, `config/skillspector-baseline.yaml` and `evals/evals.json`). Compatibility notes: Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).
It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadBashFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pippythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).
From compatibility in the SKILL.md frontmatter.
Tao Validate Dataset Format loads about 1.2k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 545 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, BashAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 545 words, ~1,243 tokens.
.claude/skills/tao-validate-dataset-format/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
tao-daft validate <format> --path <dataset-or-parent-dir><format> is a positional subcommand (e.g. metropolis-v3.0, cosmos-reason-v1.0);
--path is required. Discover supported formats and per-format flags via
tao-daft validate --help and the leaf --help (see "CLI conventions" below).
python -c "import nvidia_tao_daft" 2>/dev/null || {
echo "MISSING: tao-daft not installed. Run:"
echo " pip install nvidia-tao-daft"
exit 1
}Discover the installed validator formats before choosing a format slug, then
run validation with the target passed through --path:
tao-daft --version
tao-daft validate --help
tao-daft validate <format> --help
tao-daft validate <format> --path /path/to/daft-datasetDrive tao-daft validate against a DAFT dataset (or a tree of them).
The CLI is the spec; the skill picks subcommand + flags and explains
the result.
Trigger when the user mentions "TAO DAFT", "DAFT format", validating a
DAFT dataset, schema/cross-reference errors, or tao-daft validate.
Do not trigger for non-DAFT layouts (COCO, YOLO, Data Factory JSONL),
or for tao-daft info / tao-daft convert — those have their own skills.
If the user's opening is ambiguous, run a few --help commands first
to ground yourself, then come back and confirm the task.
nvidia-tao-daft installed (pip install nvidia-tao-daft; the wheel
is enough, no source repo). Confirm with tao-daft --version.tao-daft is nested argparse subcommands. Names and flags drift across
versions, so discover the current surface from --help rather than
trusting any list in this doc.
--format:
tao-daft validate <format> [flags]. List current formats via
tao-daft validate --help; slugs look like metropolis-v3.0,
cosmos-reason-v1.0.--path PATH, not positional. It accepts a single
dataset/scene or a parent directory — the validator walks the tree.tao-daft validate metropolis-v3.0 --help, before choosing them.
Don't assume a flag from one format exists on another.So the loop is: tao-daft --version → tao-daft validate --help →
pick format (infer if unspecified, see below) →
tao-daft validate <format> --help → run → interpret.
Use directory markers, not filenames:
meta.json next to media/ and text/ ⇒ cosmos-reason-v1.0.contextual/,
typically alongside raw/ and task/ ⇒ metropolis-v3.0.The CLI ends every run with a VALIDATION RESULTS block, then
✅ VALIDATION PASSED or ❌ VALIDATION FAILED, and exits non-zero on
failure (safe to chain in scripts).
Output can be large on big trees — capture the full output to a file and read it in slices rather than scrolling inline.
tao-daft validate --help reports
for the installed version; older slugs may have been retired.validate only. Defer to the dedicated skills for
tao-daft info and tao-daft convert.tao-daft: command not found — wheel not installed in the active
env. pip install nvidia-tao-daft; verify tao-daft --version.error: argument --path is required — path passed positionally.
Move it behind --path.invalid choice: '<format>' — slug isn't wired up in this
version. Re-run tao-daft validate --help and pick from the list.--help.--strict.© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files in skills/tao-validate-dataset-format of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
Tao Validate Dataset Format next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tao Validate Dataset Format this skillNVIDIA/skills | 3.5k | — | ~1.2k | Automated safety check: Notes | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | 1 repos | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT | |
| NEAR AI Cloud Private Inferenceinternet-court/internet-court-skill | 6.4k | 2 repos | ~1.3k | Automated safety check: Pass | Custom licence | |
| Nemoclaw Maintainer Normalize Title TagsNVIDIA/NemoClaw | 23k | — | ~693 | Automated safety check: Pass | Apache-2.0 |
NVIDIA/SkillSpector
Decides whether an agent skill is safe to install by combining a SkillSpector static scan with the agent's own source review, ending in APPROVE, CAUTION or REJECT.
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
internet-court/internet-court-skill
Shows how to call NEAR AI Cloud through an OpenAI-compatible API and verify that inference ran in a TEE, using attestation checks and signed chat responses.
NVIDIA/NemoClaw
Remove bracketed NemoClaw tags from GitHub issue and PR titles.
NVIDIA/NemoClaw
Audit and implement a NemoClaw dependency version upgrade, including Hermes and base images.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Works with
Run tao-daft validate to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Tao Validate Dataset Format is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run tao-daft validate to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors.
Tao Validate Dataset Format fits situations like: non-DAFT formats; the user asks to validate a DAFT dataset; check DAFT schema; validate a TAO dataset format.
Run `npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a claude-code`. Or copy the skill folder (skills/tao-validate-dataset-format in NVIDIA/skills) into .claude/skills/tao-validate-dataset-format in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a codex`. Or copy the skill folder (skills/tao-validate-dataset-format in NVIDIA/skills) into .agents/skills/tao-validate-dataset-format in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill tao-validate-dataset-format -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tao-validate-dataset-format, .gemini/skills/tao-validate-dataset-format, .github/skills/tao-validate-dataset-format and .opencode/skills/tao-validate-dataset-format in your project.
Going by SKILL.md and its folder, Tao Validate Dataset Format needs the command-line tools its instructions call (pip and python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft)..
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Tao Validate Dataset Format is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.2k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Tao Validate Dataset Format: Skill Inspector (NVIDIA/SkillSpector, 20k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Embeddings via 9Router (decolua/9router, 30k stars) and NEAR AI Cloud Private Inference (internet-court/internet-court-skill, 6.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.