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-analyze-gaps-visual-changenetgit 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-analyze-gaps-visual-changenet)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-analyze-gaps-visual-changenet"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-analyze-gaps-visual-changenet/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-analyze-gaps-visual-changenet"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-analyze-gaps-visual-changenet.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 10 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 YARA Match · line 23 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Privilege Escalation · line 155 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 3 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 237 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 30 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 165 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 59 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 155 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
- low Tool Misuse · line 30 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- low Tool Misuse · line 165 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00103 | $0.04321 |
| Opus 5 | $0.00051 | $0.02160 |
| Sonnet 5 | $0.00021 | $0.00864 |
| Haiku 4.5 | $0.00010 | $0.00432 |
Grade A, and why
tao-analyze-gaps-visual-changenet 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TAO VCN Classify Gap Analysis 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 an analyst for NVIDIA TAO VCN Classify (Visual Component Net) inference results. Your job is to identify the weakest samples per ground-truth label by measuring signed distance from the decision threshold in the wrong direction, then surface them for downstream augmentation or relabeling.
This skill is intentionally lightweight. VCN's classify head is a single-score binary boundary (PASS vs NO_PASS by siamese_score), so the analysis is computational, not investigative. The whole computation lives behind one direct docker run invocation against the pinned TAO data-services image (see Setup). The container's entrypoint takes <category> <action> [hydra overrides...]; we pass gap_analysis vcn_aoi key=value …. Each override is a bare Hydra key=value that selectively overrides the script's GapAnalysisConfig schema (defaults are baked into the container; use the mounted minimal-spec --cfg=job recipe immediately below to introspect them). (There is no dataset keyword inside the container — that's the TAO launcher's pillar prefix and is dropped here.) You do not need delegated analysis, multi-phase image audits, or component-type clustering — VCN does not expose those dimensions. View only a small set of representative weak samples to qualify the gaps after the container returns.
CLI surface can shift between data-services container builds. If a gap_analysis vcn_aoi invocation fails on argument parsing, introspect the actual schema once per image with a minimal spec on a bind-mounted host path:
SPEC_DIR="$(mktemp -d)"
printf '%s\n' 'min_recall: 0.99' 'top_k_per_label: 5' > "$SPEC_DIR/vcn_aoi_spec.yaml"
docker run --rm -v "$SPEC_DIR:/w:ro" "$DS_IMAGE" gap_analysis vcn_aoi \
-e /w/vcn_aoi_spec.yaml --cfg=job
The -e path must resolve inside the container, so the spec must live under the directory mounted at /w. Reconcile any renamed keys (e.g. inference_csv vs inference_results_dir, output_dir vs results_dir) before retrying. Output parquet name is kpi_gaps.parquet.
What ships with it
15 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 844 B
- hooks/_parse-stdin.sh 1.4 KB runs code
- hooks/rca-artifacts-check.sh 3.7 KB runs code
- hooks/rca-label-coverage.sh 3.4 KB runs code
- hooks/rca-package.sh 2.9 KB runs code
- hooks/rca-script-check.sh 5.7 KB runs code
- hooks/rca-section-check.sh 3.3 KB runs code
- references/container-setup.md 2.7 KB
- references/output-template.md 3.6 KB
- references/pitfalls.md 3.8 KB
- references/visual-spot-check.md 1.6 KB
- skill-card.md 4.0 KB
- skill.oms.sig 6.8 KB
- tests/test_rca_artifacts_hook.py 2.9 KB runs code
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 · +18 lines 3429aa227039
- 12d ago First seen · 223 lines · 103 tokens per session scan A 80eccea0cdef
tao-analyze-gaps-visual-changenet is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 103 tokens to every session and 4,321 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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