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cudf-analytics

Accelerate statistical profiling and anomaly detection on tabular data with NVIDIA cuDF.

3|Updated Mar 16, 2026
Search Tags:#anomaly detection#data profiling#cudf#csv processing#groupby aggregation#gpu analytics#pandas fallback
One-click install
npx skills add https://github.com/bogware/bog-agents --skill cudf-analytics-bogware
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cudf-analytics
Source: https://github.com/bogware/bog-agents/tree/main/examples/nvidia_deep_agent/skills/cudf-analytics
Command: npx skills add https://github.com/bogware/bog-agents --skill cudf-analytics-bogware

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves slow, CPU-bound analysis when you need fast statistics, groupby aggregations, profiling, and anomaly detection on large tabular datasets.

Core Features & Use Cases

  • GPU-accelerated cuDF analytics: Runs a pandas-like workflow on NVIDIA GPUs for faster summaries and transformations on large CSVs or in-memory tables.
  • GPU smoke-tested initialization: Verifies cuDF GPU compute and host transfer, with an automatic fallback to pandas when GPU support is unavailable.
  • Analytics operations at scale: Supports statistical summaries, groupby aggregation, correlation, and anomaly detection (IQR and Z-score), plus conversion utilities for downstream pandas workflows.

Quick Start

Use the cudf-analytics skill to compute a full descriptive summary and correlation for value and score columns from a large CSV dataset.

Frequently Asked Questions about cudf-analytics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run groupby aggregation and statistical profiling on large CSV files without CPU bottlenecks?▼

GPU analytics with cuDF accelerates statistical profiling and groupby aggregation on large CSV files by utilizing NVIDIA GPUs instead of CPU-bound pandas, delivering faster summaries and transformations for large-scale tabular datasets.

What is the best way to detect anomalies and outliers in large tabular datasets?▼

Anomaly detection on large tabular datasets is handled through cuDF analytics using IQR and Z-score methods, enabling GPU-accelerated outlier identification on large-scale data to bypass slow CPU processing limits.

Do I need an NVIDIA GPU to use cuDF for data profiling, or can it fall back to pandas?▼

An NVIDIA GPU is required for cuDF acceleration, but the workflow includes GPU smoke-tested initialization that automatically falls back to pandas when GPU support is unavailable, ensuring data profiling operations continue running.

Can I convert cuDF outputs back to pandas for downstream workflows?▼

cuDF outputs can be converted back to pandas using built-in safe conversion utilities, allowing you to transition GPU-accelerated statistical summaries and groupby aggregation results into pandas-compatible formats for downstream processing.

Why does GPU analytics fail during cuDF initialization on my machine?▼

GPU analytics initialization fails when NVIDIA cuDF GPU compute or host transfer smoke tests detect unsupported hardware, triggering an automatic fallback to CPU-only pandas to complete statistical profiling and anomaly detection tasks.

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