Operational Home Layout
kunchenguid/firstmate
Load when locating, interpreting, or changing Firstmate home, config, data, state, project, or generated runtime paths.
The contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, resultsdir layout, bestrec) plus the fixed job-status…
$ npx skills add NVIDIA/skills --skill tao-artifacts -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills tao-artifacts --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-artifacts .claude/skills/tao-artifacts && 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-artifacts" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-artifacts into .claude/skills/tao-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-artifacts", 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-artifactsType 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-artifacts -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills tao-artifacts --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-artifacts .agents/skills/tao-artifacts && 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-artifacts" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-artifacts into .agents/skills/tao-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-artifacts", 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-artifacts -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills tao-artifacts --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-artifacts .cursor/skills/tao-artifacts && 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-artifacts" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-artifacts into .cursor/skills/tao-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-artifacts", 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-artifacts--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-artifacts -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills tao-artifacts --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-artifacts .gemini/skills/tao-artifacts && 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-artifacts" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-artifacts into .gemini/skills/tao-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-artifacts", 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-artifactsInstalls 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-artifacts -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-artifacts .github/skills/tao-artifacts && 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-artifacts" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-artifacts into .github/skills/tao-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-artifacts", 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-artifacts -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-artifacts --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-artifacts .opencode/skills/tao-artifacts && 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-artifacts" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-artifacts into .opencode/skills/tao-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-artifacts", 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-artifactsThe contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, resultsdir layout, bestrec) plus the fixed job-status…
Tao Artifacts is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. The contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, resultsdir layout, bestrec) plus the fixed job-status vocabulary and the nested-not-dotted spec rule. Use when authoring or validating a spec-bundle before submit, writing or reading a .tao/jobs job-record, resolving where results land, or consuming AutoML's bestrec.json. Trigger phrases include "validate the spec bundle", "job record schema", "status vocabulary", "resultsdir…
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including reference files (for example `BENCHMARK.md`, `config/skillspector-baseline.yaml` and `evals/evals.json`). Compatibility notes: Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk.
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.
2 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.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonbundleFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
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.
Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk.
From compatibility in the SKILL.md frontmatter.
Tao Artifacts loads about 1.4k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 141 tokens; SKILL.md has 510 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). 510 words, ~1,350 tokens.
.claude/skills/tao-artifacts/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.Four typed artifacts flow through every TAO job. Their schemas live here and
nowhere else — producers (model/data skills) and consumers (platform skills)
both validate against this skill's references/.
| Artifact | Schema | Produced by → consumed by |
|---|---|---|
| spec-bundle | references/spec_bundle.schema.json | model/data skill → platform skill (at the submit seam) |
| job-record | references/job_record.schema.json | scripts/tao_job_record.py (the ONLY writer) → any re-attaching agent/poller |
| results_dir layout | references/results_dir.contract.md | platform skill at submit → whoever collects outputs |
| best_rec | references/best_rec.schema.json | tao-run-automl adapter → DEFT warm-start |
python - <<'PY'
import json, yaml, jsonschema, pathlib
ref = pathlib.Path("${TAO_SKILL_BANK_PATH:?}/skills/core/tao-artifacts/references")
schema = json.loads((ref / "spec_bundle.schema.json").read_text())
bundle = yaml.safe_load(open("/path/to/bundle.yaml")) # or a dict built in-context
jsonschema.validate(bundle, schema) # raises on violation
print("bundle OK")
PYValidate the bundle before the verify-before-launch gate; validate a job-record only when debugging (the writer script already enforces the schema).
spec is a nested dict mirroring the container's
config shape — {"train": {"num_epochs": 12}}. Any key containing . at
any depth is rejected ({"train.num_epochs": 12} is the #1 authoring
mistake). Note the distinction: declared_inputs[].spec_key and
gpu_spec_key are dotted/indexed pointers into the spec
(dataset.train_data_sources[0].image_dir) — dots are correct there.mode: config requires spec + config_format
and a command containing {config_path}, and forbids args.
mode: args requires args and forbids spec. There is no other mode.Use execution when an action needs more than its primary command. This is the
shared model-to-platform seam; do not add a model-specific Docker, Kubernetes,
or SLURM renderer merely to carry runtime environment, attestations,
post-processing, or helper dependencies.
environment, ordered pre_commands,
ordered post_commands, distributed-launch intent, and completion evidence.environment is non-secret. Credential values continue to use the selected
platform's secret/sidecar contract and never enter a spec-bundle.spec may use
{config_path}, {job_id}, and {results_dir}. The platform binds them
only after the job record has been opened; the job record's results_dir is
authoritative over any pre-review display path. Persist hashes of both the
producer bundle and the bound runtime config.supporting_files names checked-in orchestration helpers relative to the
producing skill root. The platform stages the closed set, verifies every
declared SHA256, and rejects traversal, undeclared siblings, or overwrite of
a different bundle. Supporting files orchestrate an action; they must never
shadow or patch code inside the selected image.torchrun declaration expresses process topology, not SLURM/Kubernetes
syntax. Each platform maps it to its native distributed launcher.Every job state anywhere in the pipeline is exactly one of:
PENDING · RUNNING · COMPLETE · ERROR · CANCELED · UNKNOWN
Platform-native sub-states (ImagePullBackOff, PENDING-because-resources,
Insufficient-GPU, slurm COMPLETING…) are never new states — they ride in
the transition's message field. Terminal = COMPLETE | ERROR | CANCELED.
This is what lets the in-turn poll loop and the detached poller share one code
path across docker/slurm/kubernetes/brev.
tao_job_record.py open writes PENDING + the resolved results_dir
first and returns the id — the only handle a launch can use. A submit
that skipped the gate has no id, so it cannot launch.transitions is append-only; .tao/ lives outside every synced results
tree.© 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 10 other files (references) in skills/tao-artifacts of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
Tao Artifacts 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 Artifacts this skillNVIDIA/skills | 3.5k | — | ~1.4k | Automated safety check: Notes | Apache-2.0 | |
| Operational Home Layoutkunchenguid/firstmate | 7.7k | — | ~6k | Automated safety check: Notes | MIT | |
| About Atlantic Home Mortgagedavepoon/buildwithclaude | 3.6k | 1 repos | ~374 | Automated safety check: Pass | MIT | |
| Home AssistantAnil-matcha/awesome-muse-connectors | 1.3k | — | ~714 | Automated safety check: Pass | MIT | |
| Home Assistantsundial-org/awesome-openclaw-skills | 663 | 1 repos | ~1.3k | Automated safety check: Pass | None | |
| Home Assistant Automation Builderautonomous-ai/openharness | 1.1k | — | ~1.2k | Automated safety check: Pass | MIT |
kunchenguid/firstmate
Load when locating, interpreting, or changing Firstmate home, config, data, state, project, or generated runtime paths.
davepoon/buildwithclaude
Background information about Lendtrain powered by Atlantic Home Mortgage — company history, credentials, founder bio, and contact information for borrower trust-building.
Anil-matcha/awesome-muse-connectors
Read entity states and call services on your Home Assistant instance.
sundial-org/awesome-openclaw-skills
Control Home Assistant smart home devices, run automations, and receive webhook events.
autonomous-ai/openharness
Turns a home-automation request into standard, testable automations.yaml, run against Home Assistant Core's real triggers and verified with its own trace tool.
mvanhorn/printing-press-library
Printing Press CLI for Adguard Home. An agent skill from mvanhorn/printing-press-library.
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.
The contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, resultsdir layout, bestrec) plus the fixed job-status…. Tao Artifacts is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. The contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, resultsdir layout, bestrec) plus the fixed job-status vocabulary and the nested-not-dotted spec rule.
Tao Artifacts fits situations like: validating a spec-bundle before submit; reading a .tao/jobs job-record; resolving where results land; consuming AutoMLs bestrec.json.
Run `npx skills add NVIDIA/skills --skill tao-artifacts -a claude-code`. Or copy the skill folder (skills/tao-artifacts in NVIDIA/skills) into .claude/skills/tao-artifacts in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill tao-artifacts -a codex`. Or copy the skill folder (skills/tao-artifacts in NVIDIA/skills) into .agents/skills/tao-artifacts 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-artifacts -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-artifacts, .gemini/skills/tao-artifacts, .github/skills/tao-artifacts and .opencode/skills/tao-artifacts in your project.
Going by SKILL.md and its folder, Tao Artifacts needs Python for the scripts in its folder and the command-line tools its instructions call (python and bundle). Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk..
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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 Artifacts 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 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Tao Artifacts: Operational Home Layout (kunchenguid/firstmate, 7.7k stars), About Atlantic Home Mortgage (davepoon/buildwithclaude, 3.6k stars), Home Assistant (Anil-matcha/awesome-muse-connectors, 1.3k stars) and Home Assistant (sundial-org/awesome-openclaw-skills, 663 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.