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-route-visual-changenet-samplesgit 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-route-visual-changenet-samples)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-route-visual-changenet-samples"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-route-visual-changenet-samples/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-route-visual-changenet-samples"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-route-visual-changenet-samples.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 | $0.00095 | $0.03235 |
| Opus 5 | $0.00048 | $0.01618 |
| Sonnet 5 | $0.00019 | $0.00647 |
| Haiku 4.5 | $0.00010 | $0.00324 |
Grade A, and why
tao-route-visual-changenet-samples 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 8d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TAO VCN Sample Routing Skill
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).
You are the dispatcher between gap analysis and the augmentation modules in a VCN AOI SDA pipeline. Each augmentation module can only act on labels it knows how to handle:
- k-NN Mining can only mine real-image neighbors for labels that already exist in the source pool CSV. There is no point looking for
SHIFTneighbors if the pool has noSHIFTrows. - AnomalyGen (Cosmos SDG) can only generate synthetic anomalies for the classes its inference pipeline supports:
PASS,EXCESS_SOLDER,MISSING,BRIDGE. A weak sample with a label outside this set is unroutable to AnomalyGen.
This skill runs once per SDA iteration immediately after gap analysis. It splits the gap-analysis parquet into one filtered parquet per module so each module operates on its own eligible subset, and it writes a human-readable summary of the per-label routing decisions.
The work is intentionally trivial: read a parquet, do two .isin(...) filters, write two parquets, write one summary. The skill exists to make those decisions auditable — every label must show up in the summary with a yes/no verdict for each module so a downstream reviewer can spot when a label is silently dropped because no module accepted it.
Inputs
gaps_parquet— the gap-analysis output (typically<exp_dir>/rca_results/<timestamp>/kpi_gaps.parquetfromtao-analyze-gaps-visual-changenet). Required columns:filepath,label. Other columns (siamese_score,weakness) are preserved verbatim.source_pool_csv— VCN-format mining source pool CSV with alabelcolumn. Empty string or non-existent path is allowed; the mining subset will simply be empty in that case.- Output directory — where the two routed parquets, the summary, and the report are written. Default: a timestamped folder under the gap-analysis result directory:
<rca_result_dir>/routing_results/<timestamp>/. anomalygen_supported_labels(optional) — override the default AnomalyGen-eligible label set. Default:{"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}. Warning: This must stay in sync withANOMALYGEN_SUPPORTED_LABELSinmdo-kratos-workflows/pipelines/sda/routing.pyand the AnomalyGen integration's actual generator coverage. Adding a new defect class to AnomalyGen means adding it here too.
What ships with it
10 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 4.1 KB
- evals/evals.json 849 B
- hooks/_parse-stdin.sh 1.4 KB runs code
- hooks/routing-artifacts-check.sh 3.5 KB runs code
- hooks/routing-coverage-check.sh 2.8 KB runs code
- hooks/routing-package.sh 2.5 KB runs code
- hooks/routing-script-check.sh 3.1 KB runs code
- hooks/routing-section-check.sh 3.4 KB runs code
- skill-card.md 3.7 KB
- skill.oms.sig 5.9 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.
- 8d ago Changed 3180b65830d1
- 12d ago First seen · 272 lines · 95 tokens per session scan A dfc115768663
tao-route-visual-changenet-samples is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 95 tokens to every session and 3,235 once invoked, about $0.0005 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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