Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-TAO/tao-skill-bank --skill tao-artifactsgit clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bankWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-artifacts)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-artifacts"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-artifacts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-artifacts"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-artifacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.01277 |
| Opus 5 | $0.00061 | $0.00639 |
| Sonnet 5 | $0.00024 | $0.00255 |
| Haiku 4.5 | $0.00012 | $0.00128 |
Grade A, and why
tao-artifacts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tao-artifacts
Four typed artifacts flow through every TAO job. Their schemas live here and
nowhere else — producers (model/data skills) and consumers (platform skills)
both validate against this skill's references/.
| Artifact | Schema | Produced by → consumed by |
|---|---|---|
| spec-bundle | references/spec_bundle.schema.json |
model/data skill → platform skill (at the submit seam) |
| job-record | references/job_record.schema.json |
scripts/tao_job_record.py (the ONLY writer) → any re-attaching agent/poller |
| results_dir layout | references/results_dir.contract.md |
platform skill at submit → whoever collects outputs |
| best_rec | references/best_rec.schema.json |
tao-run-automl adapter → DEFT warm-start |
Quick Start — validate an artifact
python - <<'PY'
import json, yaml, jsonschema, pathlib
ref = pathlib.Path("${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
- Nested, not dotted. A
specis 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_keyandgpu_spec_keyare dotted/indexed pointers into the spec (dataset.train_data_sources[0].image_dir) — dots are correct there. - Mode discrimination.
mode: configrequiresspec+config_formatand acommandcontaining{config_path}, and forbidsargs.mode: argsrequiresargsand forbidsspec. There is no other mode.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 102 lines · 122 tokens per session scan A 188bed2dc479
tao-artifacts is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 122 tokens to every session and 1,277 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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