npx skills add ...
npx skills add langchain-ai/deepagents --skill cudf-analytics
npx skills add langchain-ai/deepagents --skill 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.
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.
Use this skill when:
Always start every script with this boilerplate. It tests actual GPU operations, not just import.
cuDF mirrors the pandas API. Common operations:
cuDF requires explicit type specification for optimal performance:
float32 or float64 for numeric dataint32 or int64 for integer dataWhen reporting analysis results:
df = read_csv("data.csv")# 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"]))result = df.groupby("category").agg({
"revenue": ["sum", "mean", "count"],
"quantity": ["sum", "mean"],
})
result_pd = to_pd(result)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)])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])# 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)