Official agent skill

Tao Artifacts

by NVIDIA in NVIDIA/skills

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…

OfficialApache-2.0Auto-check: notes

Install Tao Artifacts

skills CLI
$ npx skills add NVIDIA/skills --skill tao-artifacts -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills tao-artifacts --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
tao-artifacts
GitHub stars
3.5k
Token cost
~1.4k tokens
SKILL.md length
510 words
Files
11 (incl. references)
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

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…

  • Works in 2 steps: Nested, not dotted. A spec is a nested… → Mode discrimination. mode: config…
  • Validating a spec-bundle before submit
  • SKILL.md covers Quick Start — validate an…, The two rules the schemas…, Optional action lifecycle and Fixed status vocabulary, plus 1 more section
  • Runs Python scripts from its folder; calls python and bundle

What it does

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.

When your agent uses it

  • Validating a spec-bundle before submit
  • Reading a .tao/jobs job-record
  • Resolving where results land
  • Consuming AutoMLs bestrec.json

Example prompts

  • “s bestrec.json. Trigger phrases include”
  • “job record schema”
  • “status vocabulary”
  • “/tao-artifacts”

Requirements

  • Python 3
  • Docker
  • Compatibility (from SKILL.md): Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk.
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Nested, not dotted. A spec is a nested dict mirroring the container's
  2. Mode discrimination. mode: config requires spec + config_format

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • bundle

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk.

    From compatibility in the SKILL.md frontmatter.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~141
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.3k

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.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash

Automated 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.

SKILL.md

The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 510 words, ~1,350 tokens.

Download SKILL.mdSave it as .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.
name
tao-artifacts
description
The contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, results_dir layout, best_rec) 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 best_rec.json. Trigger phrases include "validate the spec bundle", "job record schema", "status vocabulary", "results_dir layout", "best_rec schema".
allowed-tools
Read, Bash
compatibility
Python 3.10+ with jsonschema for validation. No nvidia-tao-sdk.
license
Apache-2.0
metadata.author
NVIDIA Corporation
metadata.version
0.1.0
tags
core

tao-artifacts

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/.

ArtifactSchemaProduced by → consumed by
spec-bundlereferences/spec_bundle.schema.jsonmodel/data skill → platform skill (at the submit seam)
job-recordreferences/job_record.schema.jsonscripts/tao_job_record.py (the ONLY writer) → any re-attaching agent/poller
results_dir layoutreferences/results_dir.contract.mdplatform skill at submit → whoever collects outputs
best_recreferences/best_rec.schema.jsontao-run-automl adapter → DEFT warm-start

Quick Start — validate an artifact

bash
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")
PY

Validate the bundle before the verify-before-launch gate; validate a job-record only when debugging (the writer script already enforces the schema).

The two rules the schemas enforce structurally

  1. Nested, not dotted. A 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.
  2. Mode discrimination. 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.

Optional action lifecycle

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.

  • The producing model/data skill owns environment, ordered pre_commands, ordered post_commands, distributed-launch intent, and completion evidence.
  • The platform owns container mounts, scheduler/container syntax, task/rank binding, timeouts, log paths, and preservation of the real child exit code.
  • environment is non-secret. Credential values continue to use the selected platform's secret/sidecar contract and never enter a spec-bundle.
  • Commands, environment values, and string values in 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.
  • A torchrun declaration expresses process topology, not SLURM/Kubernetes syntax. Each platform maps it to its native distributed launcher.
Show full SKILL.md (122 more words)Show less

Fixed status vocabulary

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.

Ordering invariants (enforced at the seam, stated here)

  • The verify-before-launch gate runs on the spec-bundle, before any job id exists.
  • 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

Files

SKILL.md and 10 other files (references) in skills/tao-artifacts of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • config/skillspector-baseline.yaml
  • evals/evals.json
  • references/best_rec.schema.json
  • references/job_record.schema.json
  • references/results_dir.contract.md
  • references/spec_bundle.schema.json
  • references/tests/test_artifact_schemas.py
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 67a13c0

Compare with similar skills

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.

Tao Artifacts compared with similar skills
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Tao Artifacts this skillNVIDIA/skills3.5k—~1.4kAutomated safety check: NotesApache-2.0
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About Atlantic Home Mortgagedavepoon/buildwithclaude3.6k1 repos~374Automated safety check: PassMIT
Home AssistantAnil-matcha/awesome-muse-connectors1.3k—~714Automated safety check: PassMIT
Home Assistantsundial-org/awesome-openclaw-skills6631 repos~1.3kAutomated safety check: PassNone
Home Assistant Automation Builderautonomous-ai/openharness1.1k—~1.2kAutomated safety check: PassMIT

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Questions about Tao Artifacts

What does Tao Artifacts do?

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.

When should I use Tao Artifacts?

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.

How do I install Tao Artifacts in Claude Code?

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.

How do I install Tao Artifacts in Codex?

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.

Can I use Tao Artifacts in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Tao Artifacts need to run?

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..

Does Tao Artifacts access the network?

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.

Is Tao Artifacts safe to install?

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.

What licence does Tao Artifacts use?

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.

How many tokens does Tao Artifacts use?

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.

What are the alternatives to Tao Artifacts?

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.

Who maintains Tao Artifacts?

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.