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Langsmith Evaluator

Listed
jackjin1997·vlatest·ai-ml

Langsmith Evaluator assesses LLM outputs for quality and consistency, streamlining feedback loops & improving model performance.

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 jackjin1997/clawforge --skill langsmith-evaluator -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

jackjin1997

Taken from the source listing. We haven't verified who controls this account.

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 undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.smith.langchain.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

    api.smith.langchain.comundeclared

    What it found

    • skills/langsmith-evaluator/scripts/upload_evaluators.py:21contacts api.smith.langchain.com
      LANGSMITH_API_URL = os.getenv("LANGSMITH_API_URL", "https://api.smith.langchain.com")

  • 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 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.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

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

  • Pinned to a commit: passed

    Scanned jackjin1997/clawforge at 896286f3d1.

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

Commit
896286f3d1
Manifest
skills/langsmith-evaluator/SKILL.md
Scanned
2 files, 635 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 allows AI agents to leverage LangSmith's evaluation capabilities. It provides a framework for evaluating LLM outputs against predefined metrics, enabling more robust and reliable agent performance. The skill facilitates automated feedback loops and helps identify areas for improvement in agent behavior.

When to use it

  • Evaluating Agent Responses: Assess the quality of an agent’s answers based on specific criteria (e.g., accuracy, helpfulness, safety).
  • Debugging Agent Behavior: Pinpoint why an agent is producing undesirable outputs by analyzing evaluation results.
  • Improving Prompt Engineering: Refine prompts to optimize for higher scores in LangSmith evaluations.
  • Automated Testing: Integrate the skill into automated testing pipelines to ensure consistent performance over time.

Key capabilities

  • Integration with Langsmith platform
  • Evaluation of LLM outputs against metrics
  • Automated feedback loops
  • Performance analysis and debugging

Example prompts

  • "Evaluate this agent response: '...' using the LangSmith evaluator."
  • "Run a LangSmith evaluation on the last 5 interactions with the user."
  • "Show me the performance report from the LangSmith evaluator for this task."

Tips & gotchas

  • Requires an active LangSmith account and API key to function correctly.
  • The effectiveness of the skill depends on well-defined and appropriate evaluation metrics within LangSmith.