cudf-analytics
Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical summaries, anomaly detection, or large-scale data profiling.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/langchain-ai/deepagents --skill cudf-analytics- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads cudf-analytics automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
cuDF Analytics Skill
GPU-accelerated data analysis using NVIDIA RAPIDS cuDF. cuDF provides a pandas-like API that runs on NVIDIA GPUs, enabling massive speedups on large datasets.
When to Use This Skill
Use this skill when:
- Analyzing CSV files, datasets, or tabular data
- Computing statistical summaries (mean, median, std, quartiles)
- Performing groupby aggregations
- Detecting anomalies or outliers in data
- Profiling datasets with millions of rows
- Computing correlation matrices
Initialization (REQUIRED)
Always start every script with this boilerplate. It tests actual GPU operations, not just import.
import pandas as pd
try:
import cudf
# Smoke-test: verify GPU compute AND host transfer both work
_test = cudf.Series([1, 2, 3])
assert _test.sum() == 6
assert _test.to_pandas().tolist() == [1, 2, 3]
GPU = True
except Exception as e:
print(f"[GPU] cudf unavailable, falling back to pandas: {e}")
GPU = False
def read_csv(path):
return cudf.read_csv(path) if GPU else pd.read_csv(path)
def to_pd(df):
"""Convert cuDF DataFrame/Series to pandas. Use this instead of .to_pandas() directly."""
if not GPU:
return df
try:
return df.to_pandas()
except Exception as e:
print(f"[GPU] .to_pandas() failed, using Arrow fallback: {e}")
return df.to_arrow().to_pandas()
Quick Reference
cuDF mirrors the pandas API. Common operations:
Read Data
df = read_csv("data.csv")
Statistical Summary
# Use to_pd() when you need pandas output
summary = to_pd(df[["value", "score"]].describe())
# Scalar values work directly with float()
mean_val = float(df["value"].mean())
q1 = float(df["value"].quantile(0.25))
# Correlation
corr = float(df["value"].corr(df["score"]))
Groupby Aggregation
result = df.groupby("category").agg({
"revenue": ["sum", "mean", "count"],
"quantity": ["sum", "mean"],
})
result_pd = to_pd(result)
Anomaly Detection (IQR Method)
col = "value"
Q1 = float(df[col].quantile(0.25))
Q3 = float(df[col].quantile(0.75))
IQR = Q3 - Q1
lower = Q1 - 1.5 * IQR
upper = Q3 + 1.5 * IQR
outliers = to_pd(df[(df[col] < lower) | (df[col] > upper)])
Anomaly Detection (Z-Score Method)
mean = float(df[col].mean())
std = float(df[col].std())
df["z_score"] = (df[col] - mean) / std
anomalies = to_pd(df[df["z_score"].abs() > 3])
Filtering and Selection
# Filter rows
filtered = df[df["status"] == "active"]
# Select columns
subset = df[["name", "revenue", "date"]]
# Sort
sorted_df = df.sort_values("revenue", ascending=False)
# Convert to pandas for final output / iteration
result_pd = to_pd(sorted_df)
Data Type Requirements
cuDF requires explicit type specification for optimal performance:
- Use
float32orfloat64for numeric data - Use
int32orint64for integer data - String columns use cuDF's string dtype automatically
Output Guidelines
When reporting analysis results:
- Include dataset dimensions (rows x columns)
- Show key statistics in formatted tables
- Highlight notable patterns, trends, or anomalies
- Provide both summary statistics and specific examples
- Note any data quality issues (missing values, outliers)
FAQ
What does the cudf-analytics skill do?
Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical summaries, anomaly detection, or large-scale data profiling.
How do I install cudf-analytics?
Run npx skills add https://github.com/langchain-ai/deepagents --skill cudf-analytics in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cudf-analytics/ (personal) or .claude/skills/cudf-analytics/ (project).
Which AI agents can use cudf-analytics?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is cudf-analytics safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains cudf-analytics?
It is published by langchain-ai in langchain-ai/deepagents, and it is an official skill from the maker of the technology it covers. It has 216 recorded installs.
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