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-launch-workflowgit 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-launch-workflow)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-launch-workflow"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-launch-workflow/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-launch-workflow"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-launch-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 253 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00131 | $0.04024 |
| Opus 5 | $0.00066 | $0.02012 |
| Sonnet 5 | $0.00026 | $0.00805 |
| Haiku 4.5 | $0.00013 | $0.00402 |
Grade A, and why
tao-launch-workflow 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 11d 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 — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TAO Workflow Launch Intake
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).
Use this skill before launching any TAO workflow or model action.
Quick Start
Run the platform helper, ask for platform and monitoring preferences, then run the selected platform detail helper before asking for credentials.
Non-Negotiable Launch Gate
This gate is model-agnostic. Apply it to every TAO model, data action, and application workflow before launching side-effecting work.
Do not create runner scripts, launch scripts, compatibility shims, workspace folders, state files, logs, or dependency-install side effects until the launch preflight passes.
Preflight passes only after all of these are true:
- The execution platform is selected from the packaged platform helper.
- Platform credentials and required credential groups are satisfied.
- Model-specific credentials are satisfied.
- The default container image is resolved from packaged model/action metadata,
shown to the user, and either confirmed or replaced by an explicit
image=<override>. - The platform access check succeeds from the launch host.
- Dataset inputs are mapped to concrete spec keys and verified from the selected platform's point of view.
- Required compute shape fields from the model/workflow skill are known.
- Required local tools for the selected data/platform path are present, or the user approved installing the smallest missing dependency and preflight was rerun.
- A launch review with image, platform, datasets, compute shape, expected runtime, and any generated/default configuration changes has been shown and confirmed by the user. For AutoML, the launch review must explicitly state recommendation count/budget, max concurrency, algorithm, metric, direction, and searched parameters/ranges even when defaults are used.
If any item is missing, ask for the missing input and stop before generating artifacts. This applies to AutoML, normal train/eval/infer/export/TRT, and DEFT/application workflows.
What ships with it
8 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.
- 11d ago First seen · 364 lines · 131 tokens per session scan A 43304974f48e
tao-launch-workflow is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 131 tokens to every session and 4,024 once invoked, about $0.0007 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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