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-od-mapgit 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-od-map)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-analyze-gaps-od-map"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-analyze-gaps-od-map/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-od-map"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-analyze-gaps-od-map.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 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 92 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.
- high Privilege Escalation · line 99 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.
- medium MCP Rug Pull · line 92 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 139 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 113 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 | $0.00084 | $0.01813 |
| Opus 5 | $0.00042 | $0.00907 |
| Sonnet 5 | $0.00017 | $0.00363 |
| Haiku 4.5 | $0.00008 | $0.00181 |
Grade A, and why
tao-analyze-gaps-od-map 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 13d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TAO Analyze Gaps OD mAP
Use this skill to run TAO Data Services object-detection gap analysis. The skill compares ground-truth and inference annotations, computes per-image per-class TP/FP/FN/AP50 metrics, and identifies weak images where any class metric falls below its threshold. It does not run inference; upstream steps must produce the inference annotations first.
The container entrypoint is:
gap_analysis object_detection -e /absolute/path/to/object_detection.yaml
Inputs
Required spec fields:
| Field | Meaning |
|---|---|
ground_truth_ann_path |
KITTI label directory or COCO .json with ground-truth boxes. |
inference_ann_path |
KITTI label directory or COCO .json with model predictions. |
images_dir |
Root image directory. Establishes the full image universe including unannotated images. |
results_dir |
Output directory for all artifacts. |
kpi |
Identifier tag written to every output row. |
input_format |
kitti or coco. Must be declared explicitly; never inferred from the path. |
Common optional fields:
| Field | Default | Meaning |
|---|---|---|
iou_threshold |
0.5 |
IoU at or above which a prediction is accepted as a true positive. |
conf_threshold |
0.0 |
Predictions below this confidence are dropped before matching. |
min_area |
0 |
Boxes whose pixel area (w × h) is strictly below this value are discarded. |
class_mapping |
{} |
Maps raw annotation label strings to canonical class names. Absent labels are kept as-is. |
weak_thresholds |
{} |
Per-class thresholds as {class_name: {recall, precision, ap50}}. Absent keys fall back to the default_*_threshold values. Reference ITS defaults: car 0.99, bicycle 0.7, person 0.7 — a strict gate on the abundant, well-learned class and looser gates on the rare ones the loop exists to improve. |
default_recall_threshold |
0.5 |
Fallback recall threshold for classes not listed in weak_thresholds. |
default_precision_threshold |
0.0 |
Fallback precision threshold. Set to 0.0 to disable precision-based weak selection. |
default_ap50_threshold |
0.5 |
Fallback for classes absent from weak_thresholds. Set 0.0 so unlisted classes never mark an image weak — the reference filter had no fallback, and leaving TAO DS's 0.5 in place silently gates every class you did not list. |
What ships with it
5 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.
- 13d ago First seen · 140 lines · 84 tokens per session scan A 9b51843b5473
tao-analyze-gaps-od-map is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 1,813 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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