Agent skill · nvidia
tao-finetune-kumo-anomaly
Kumo-Anomaly — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune Kumo-Anomaly", "run Kumo-Anomaly inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune Kumo-Anomaly", "use perform_anomaly_analysis_with_diffusion", "automl Kumo-Anomaly", "hyperparameter search Kumo-Anomaly", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "kumo-anomaly", "sdk/anomaly_analysis", or "TSDiffuser_Generic".
What it needs
About 7k tokens when loaded.
What this skill does
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 docs - Kumo-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 huggingfacehub 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 Inference Use performanomalyanalysiswithdiffusion in sdk/anomalyanalysis.py. It validates input, auto-dispatches across all visible GPUs, applies adaptive thresholding (SCS or MACS), and returns the original DataFrame with Anomaly (0/1) and MAE columns appended. Reporting (sdkconfig) performanomalyanalysiswithdiffusion takes an optional sdkconfig keyword — an ADDiffusionConfig, a path to a YAML file, or None for defaults. It scopes report-generation knobs only; inference-behavior args (thresholdstrategy, nsample, modelpath, modelconfigpath) stay as direct function arguments. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-finetune-kumo-anomaly