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-train-foundation-stereogit 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-train-foundation-stereo)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-foundation-stereo"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-foundation-stereo/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-train-foundation-stereo"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-foundation-stereo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, 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 120 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.
- high Privilege Escalation · line 131 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 47 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 131 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00074 | $0.03193 |
| Opus 5 | $0.00037 | $0.01597 |
| Sonnet 5 | $0.00015 | $0.00639 |
| Haiku 4.5 | $0.00007 | $0.00319 |
Grade A, and why
tao-train-foundation-stereo 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Depth Net Stereo
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).
Stereo depth estimation using FoundationStereo architecture. Predicts disparity maps from stereo image pairs for 3D reconstruction.
Uses pretrained Depth Anything v2 and EdgeNeXt encoders. Set model.stereo_backbone.depth_anything_v2_pretrained_path and model.stereo_backbone.edgenext_pretrained_path.
The mono and stereo skills both invoke the unified TAO depth_net CLI inside the container; the mono/stereo family is selected via model.model_type (e.g., FoundationStereo).
PyT actions packaged by this model skill: train, evaluate, inference, export, and quantize. The PyT depth_net entrypoint does not accept a gen_trt_engine action in the current TAO image; build TensorRT engines only through the deploy workflow.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-foundation-stereo.md first. The deploy spec template lives in this skill's references/spec_template_deploy.yaml.
Train Action Policy
This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_info.yaml and resolve the run override from either an explicit automl_policy value or the user's workflow request. Use automl_policy: on by default and only expose on / off in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only. When automl_policy: on, automl_enabled: true, and both schemas/train.schema.json and references/spec_template_train.yaml are packaged, route the train action through tao-skill-bank:tao-run-automl by default with this model's skill_dir. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.
What ships with it
25 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 4.0 KB
- evals/evals.json 819 B
- references/checkpoint-inference-mappings-foundation-stereo.md 3.9 KB
- references/parameters-foundation-stereo.md 5.6 KB
- references/skill_info.yaml 5.5 KB
- references/spec_template_deploy.yaml 2.0 KB
- references/spec_template_evaluate.yaml 5.5 KB
- references/spec_template_export.yaml 5.6 KB
- references/spec_template_gen_trt_engine.yaml 5.6 KB
- references/spec_template_inference.yaml 5.6 KB
- references/spec_template_quantize.yaml 5.4 KB
- references/spec_template_train.yaml 5.4 KB
- references/spec-overrides-foundation-stereo.md 4.1 KB
- references/tao-deploy-foundation-stereo.md 12 KB
- references/tao-deploy-foundation-stereo.skill_info.yaml 3.2 KB
- references/troubleshooting-foundation-stereo.md 2.4 KB
- schemas/evaluate.schema.json 104 KB
- schemas/export.schema.json 105 KB
- schemas/gen_trt_engine.schema.json 107 KB
- schemas/inference.schema.json 105 KB
- schemas/manifest.json 30 KB
- schemas/quantize.schema.json 101 KB
- schemas/train.schema.json 101 KB
- skill-card.md 4.0 KB
- skill.oms.sig 9.7 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.
- 12d ago First seen · 190 lines · 74 tokens per session scan A 5719691b3c20
tao-train-foundation-stereo is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 74 tokens to every session and 3,193 once invoked, about $0.0004 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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