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daft

New
5.5kCommunity RegistryDocumentationby Eventual, Inc.

Skills for working with Daft: UDF tuning, distributed scaling, and docs navigation

First seen 6/5/2026

Summary

This skill helps developers work with Daft, a distributed DataFrame library for Python.

  • It provides guidance on tuning UDFs for performance, scaling workloads across clusters, and navigating Daft's documentation efficiently.

Install & Usage

1
Create the skills directory
mkdir -p .claude/skills
2
Download the skill file
mkdir -p .claude/skills && curl -o .claude/skills/daft.md https://raw.githubusercontent.com/Eventual-Inc/Daft/main/SKILL.md
3
Invoke in Claude Code
/daft

Use Cases

Tuning a Python UDF to avoid serialization bottlenecks in Daft
Scaling a Daft DataFrame operation from a single machine to a Ray cluster
Finding the correct API for partitioning data in Daft documentation
Debugging a slow Daft query by identifying shuffle or memory issues
Converting a pandas pipeline to Daft for distributed execution
Optimizing I/O patterns when reading large datasets with Daft

Usage Examples

1

/daft How can I optimize a UDF that uses external libraries in Daft?

2

/daft Show me how to scale a group-by aggregation across multiple nodes

3

/daft Find the documentation for Daft's window functions

View source on GitHub

Security Audits

LicenseUnknownSourceWarnRepositoryPass

Frequently Asked Questions

What is daft?

This skill helps developers work with Daft, a distributed DataFrame library for Python. It provides guidance on tuning UDFs for performance, scaling workloads across clusters, and navigating Daft's documentation efficiently.

How to install daft?

To install daft: create the skills directory (mkdir -p .claude/skills), then run: mkdir -p .claude/skills && curl -o .claude/skills/daft.md https://raw.githubusercontent.com/Eventual-Inc/Daft/main/SKILL.md. Finally, /daft in Claude Code.

What is daft best for?

daft is a skill categorized under Documentation. Created by Eventual, Inc..

What can I use daft for?

daft is useful for: Tuning a Python UDF to avoid serialization bottlenecks in Daft; Scaling a Daft DataFrame operation from a single machine to a Ray cluster; Finding the correct API for partitioning data in Daft documentation; Debugging a slow Daft query by identifying shuffle or memory issues; Converting a pandas pipeline to Daft for distributed execution; Optimizing I/O patterns when reading large datasets with Daft.