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Langsmith Dataset

Official
langchain-ai·vlatest·data·MIT

Langsmith Dataset allows you to easily create, manage, and annotate datasets for evaluating and improving your language models – boosting accuracy and reliability.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add langchain-ai/langsmith-skills --skill langsmith-dataset -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

langchain-ai

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • config/skills/langsmith-dataset/SKILL.md:39remote script piped into a shell
      curl -sSL https://raw.githubusercontent.com/langchain-ai/langsmith-cli/main/scripts/install.sh | sh

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Only contacts known or declared hosts: raw.githubusercontent.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    raw.githubusercontent.comsource host

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned langchain-ai/langsmith-skills at bc2f989d85.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
bc2f989d85
Manifest
config/skills/langsmith-dataset/SKILL.md
Scanned
1 file, 296 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to interact with LangSmith datasets, allowing them to retrieve, analyze, and manage evaluation data directly. It facilitates the creation of custom test cases and the inspection of model performance metrics stored within the LangSmith platform.

When to use it

  • You need to programmatically access historical evaluation results for specific LLM runs.
  • Your agent requires the ability to generate new test cases based on existing dataset patterns.
  • You are building a feedback loop where the agent analyzes its own performance data stored in LangSmith.
  • You want to automate the retrieval of ground truth labels for validation tasks.

Key capabilities

  • Retrieval and management of datasets hosted on LangSmith.
  • Creation and modification of custom test cases within the dataset structure.
  • Analysis of model evaluation metrics associated with specific data points.
  • Integration of dataset operations directly into agent workflows.

Example prompts

  • "Retrieve the last 10 evaluation results from my 'customer-support' LangSmith dataset."
  • "Create a new test case in the dataset labeled 'math-problems' containing five addition questions."
  • "Analyze the accuracy scores for all runs associated with the 'legal-document-review' dataset and summarize the findings."

Tips & gotchas

Ensure your AI agent has valid API credentials configured to access your specific LangSmith workspace before attempting to retrieve or modify datasets. This skill is designed for agents that need direct programmatic access to evaluation infrastructure rather than just reading static text.