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-port-huggingface-modelgit 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-port-huggingface-model)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-port-huggingface-model"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-port-huggingface-model/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-port-huggingface-model"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-port-huggingface-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00210 | $0.04521 |
| Opus 5.5 | $0.00084 | $0.01808 |
| Sonnet 5.5 | $0.00042 | $0.00904 |
| Haiku 4.5 | $0.00021 | $0.00452 |
Grade C, and why
tao-port-huggingface-model scanned grade C with 1 finding 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 1mo 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.
Harvests environment variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
**Goal:** decide whether to proceed at all. Gather credentials, locate/clone the four TAO repos, create a consistent working branch, launch the `tao-hf-inspect` container (Context A), validate the HF model is CV with a s How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on 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-HF Integration Skill
Integrate a HuggingFace (HF) Computer Vision model into the NVIDIA TAO Toolkit ecosystem. Work the phases iteratively — not purely linearly — via a build → test → debug → fix → retest loop at every step: when something fails, diagnose and fix before moving on; when it passes, move to the next step.
This SKILL.md is the workflow coordinator. Each phase has a dedicated references/phase-N-*.md with the full step-by-step content, code, docker invocations, and gates. Read the matching reference at the start of each phase — the summaries below are not sufficient.
Local-Only Rule
All work is strictly local. Do NOT push/commit/branch on any remote (GitLab, GitHub, HuggingFace), create merge/pull requests or issues, or upload/publish Docker images to any registry or artifact store. You may only read/clone from remotes — all edits, Docker builds, and test runs stay on the local machine.
Submodule Override Strategy
The user clones the four TAO repos (tao-core, tao-pytorch, tao-deploy, tao-dataservices) independently into one working directory. The tao-core/ submodule nested inside each repo points to the original unmodified commit; modifications only exist in the top-level tao-core/. Always install from the top-level tao-core/, never <repo>/tao-core/ — the nested submodule silently ignores all modifications. Override rules: (1) mount the working directory -v $(pwd):/workspace; (2) pip install /workspace/tao-core FIRST, before tao-pytorch/tao-deploy; (3) PYTHONPATH top-level tao-core first, e.g. -e PYTHONPATH=/workspace/tao-core:/workspace/tao-pytorch. See references/cross-cutting.md for the directory tree.
What ships with it
20 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.
- BENCHMARK.md 3.9 KB
- eval.config 3.2 KB
- evals/evals.json 809 B
- references/cross-cutting.md 10 KB
- references/docker-patterns.md 16 KB
- references/hf-inspection.md 6.2 KB
- references/phase-0-prereqs.md 9.9 KB
- references/phase-1-inspection.md 10 KB
- references/phase-2-codebase.md 7.3 KB
- references/phase-3-implementation.md 35 KB
- references/phase-4-deploy.md 13 KB
- references/phase-5-packaging.md 3.3 KB
- references/phase-6-container-tests.md 15 KB
- references/phase-7-optimization.md 7.8 KB
- references/repo-structure.md 11 KB
- references/tao-patterns.md 20 KB
- references/task-type-guide.md 16 KB
- references/workflow-consistency.md 27 KB
- skill-card.md 4.3 KB
- skill.oms.sig 8.1 KB
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.
- 1mo ago First seen · 211 lines · 210 tokens per session scan C fd8cacf316e1
tao-port-huggingface-model is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (97 stars, last pushed 5d ago), licensed Apache-2.0. It adds 210 tokens to every session and 4,521 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it C with 1 finding (harvests environment variables). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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