Agent skill · nvidia
nvflare-fed-stats
Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.
What it needs
About 6k tokens when loaded.
What this skill does
NVFLARE Federated Statistics Data-first and automatic: point at tabular or image data and it runs end-to-end — no interaction, no user statistics code. Use When Use when the user asks to compute statistics, data summaries, histograms, or quantiles across federated sites for tabular data (CSV, parquet, any pandas-representable form) or image datasets (PNG/JPEG/BMP/TIFF folders; DICOM/NIfTI with the matching loader), with or without an accompanying README/notes or statistics script. Supported for tabular: count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max (variance and stddev are distinct — never substitute one for the other); for images: count, failurecount, pixel-intensity histograms. Both paths use FedStatsRecipe generation, simulator validation, completeness checks. Do Not Use When Do not use for model training conversion (route to nvflare-convert-pytorch, nvflare-convert-lightning, or nvflare-convert-huggingface), a failed or stalled existing job (route to nvflare-diagnose-job), or generic pandas/data-science help without federated intent. If a request combines federated statistics and model-training conversion, treat it as two independent jobs and workflows: do not merge or automatically chain them, do not route the combination to nvflare-orient, and ask which workflow to run first before generating or running either job. Recommend nvflare-fed-stats first only when the user's purpose is to understand data distribution; handle conversion later as a separate request. Hierarchical statistics, production deployment, Kubernetes, POC lifecycle, and privacy-policy design beyond the recipe's built-in knobs are out of scope. Statistics outside the supported set — categorical counts, correlations, custom aggregations — are reported as unsupported, never silently dropped or approximated. Workflow 1. Apply the standard automatic path below without loading the full shared workflow. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/nvflare-fed-stats