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-finetune-kumo-anomalygit 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-finetune-kumo-anomaly)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-finetune-kumo-anomaly"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-finetune-kumo-anomaly/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-finetune-kumo-anomaly"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-finetune-kumo-anomaly.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 | $0.00139 | $0.03529 |
| Opus 5.5 | $0.00056 | $0.01412 |
| Sonnet 5.5 | $0.00028 | $0.00706 |
| Haiku 4.5 | $0.00014 | $0.00353 |
Grade A, and why
tao-finetune-kumo-anomaly 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 17d 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kumo-Anomaly
Diffusion-based anomaly detection and fine-tuning for multivariate time series. The model reconstructs randomly masked segments and scores each timestep by MAE between reconstruction and original signal; adaptive thresholding (SCS or MACS) converts scores to binary labels.
Source code: https://github.com/NVIDIA/Kumo-TS Pretrained weights: https://huggingface.co/nvidia/Kumo-Anomaly
For the most up-to-date usage information, refer to the README files in the Kumo-TS repository:
Kumo-Anomaly/README.md— full SDK reference, model architecture, and API docsKumo-Anomaly/examples/datasets/README.md— dataset format, synthetic data generation, and CSV conventions
External dependencies
| Dependency | Purpose | Install |
|---|---|---|
| Python 3.12+ | Runtime | https://www.python.org/downloads/ |
| uv | Package + environment manager | pip install uv |
| CUDA toolkit (optional) | GPU acceleration | https://developer.nvidia.com/cuda-downloads |
| huggingface_hub | Weight download from HF | Bundled via uv sync |
Credentials
nvidia/Kumo-Anomaly is a public repo — no token required for downloading weights.
If you ever hit a 401/403 (gated access or private fork) or a 504 on first download, see the Known pitfalls section.
Quick start
git clone --branch main --single-branch https://github.com/NVIDIA/Kumo-TS Kumo-TS
cd Kumo-TS/Kumo-Anomaly
uv sync # install dependencies (one-time)
# Inference — synthetic data, auto-downloads weights from HF on first run
uv run python examples/quick_example.py
# Inference — your own CSV
uv run python examples/quick_example.py \
--model-path final_model.pth \
--config-path curriculum_medium.yaml \
--dataset-path /path/to/data.csv
# Pre-download weights only (warm the cache before going offline)
uv run python examples/quick_example.py --download-weights
# Fine-tune on your own normal-behavior data
uv run python examples/finetune_example.py \
--csv /path/to/normal_training_data.csv \
--timestamp-col timestamp \
--label-col is_anomaly \
--epochs 20 \
--output-dir artifacts/finetune_my_data
What ships with it
7 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.
- 17d ago First seen · 306 lines · 139 tokens per session scan A d2018066dbfb
tao-finetune-kumo-anomaly is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (95 stars, last pushed yesterday), licensed Apache-2.0. It adds 139 tokens to every session and 3,529 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-09-29.
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