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langsmith-dataset

Convert LangSmith traces into structured evaluation datasets via CLI and SDK.

8|Updated Jul 26, 2026
Search Tags:#testing#llm#evaluation#traces#dataset#langsmith
One-click install
npx skills add https://github.com/joonlab/joonlab-claudecode-setting-for-share --skill langsmith-dataset-joonlab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-dataset
Source: https://github.com/joonlab/joonlab-claudecode-setting-for-share/tree/main/claude/skills/langsmith-dataset
Command: npx skills add https://github.com/joonlab/joonlab-claudecode-setting-for-share --skill langsmith-dataset-joonlab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langsmith.

What problem does it solve?

This skill solves the friction of managing evaluation datasets for LLM applications by providing a unified interface to create, upload, and maintain datasets directly from your trace data.

Core Features & Use Cases

  • Trace-to-Dataset Pipeline: Automatically convert raw LangSmith traces into structured evaluation datasets.
  • CLI-Driven Management: Perform CRUD operations on datasets, examples, and experiments without leaving your terminal.
  • Use Case: When you need to validate a new prompt version, use this skill to export recent production traces, format them into a test set, and upload them to LangSmith for automated evaluation.

Quick Start

Use the langsmith-dataset skill to export traces from the current project and upload them as a new evaluation dataset named Evaluation Set Alpha.

Frequently Asked Questions about langsmith-dataset

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

FAQPage Schema
How do I create evaluation datasets from LangSmith traces?▼

You can create evaluation datasets from LangSmith traces by extracting raw trace data and converting it into structured JSON formats for testing and validation. This skill automates that extraction pipeline directly from your production data.

How do I manage LLM evaluation examples from the command line?▼

Managing LLM evaluation examples from the command line is possible through the LangSmith CLI. You can perform CRUD operations on datasets, examples, and experiments directly within your terminal without needing a graphical interface.

What is the best way to validate a new LLM prompt version using production data?▼

The best way to validate a new LLM prompt version is to export recent production traces, format them into a test set, and upload them to LangSmith. This ensures your evaluation workflow uses real-world data for automated testing.

Do I need the LangSmith SDK to automate dataset management?▼

Yes, you need the LangSmith SDK and CLI installed to automate dataset management. This skill interfaces with both tools to handle example management, experiment tracking, and trace extraction programmatically.

Can I track LLM experiments automatically when uploading test datasets?▼

Yes, you can track LLM experiments automatically when uploading test datasets. The skill ensures consistent evaluation workflows by automating dataset creation alongside experiment tracking through the LangSmith SDK.

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