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Langsmith Trace Analyzer

Checked
lubu-labs·vlatest·ai-ml·MIT

Langsmith Trace Analyzer dissects LLM traces to pinpoint performance bottlenecks and identify areas for optimization, boosting efficiency.

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 soba-labs/langchain-agent-skills --skill langsmith-trace-analyzer -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

lubu-labs

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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

    Makes no network calls.

    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.

  • 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 lubu-labs/langchain-agent-skills at a2d4a1011b.

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

Commit
a2d4a1011b
Manifest
skills/langsmith-trace-analyzer/SKILL.md
Scanned
7 files, 2,464 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

The langsmith-trace-analyzer skill analyzes LangSmith traces to identify potential issues and areas for improvement in agent workflows. It can pinpoint inefficiencies, errors, or unexpected behavior within a sequence of actions taken by an AI agent. This allows developers to debug complex agent interactions and optimize performance.

When to use it

  • Debugging Agent Errors: When your agent is failing unexpectedly, use this skill to examine the trace and identify the exact step causing the problem.
  • Performance Optimization: If your agent's execution time is too long, analyze traces to find bottlenecks in specific tools or steps.
  • Understanding Complex Workflows: Gain a deeper understanding of how an agent navigates a task by visualizing its entire decision-making process through trace analysis.
  • Identifying Unexpected Tool Usage: Detect if the agent is using tools in ways you didn't intend, potentially indicating issues with prompt design or tool configuration.

Key capabilities

  • Trace Analysis: Provides detailed insights into individual steps within a LangSmith trace.
  • Error Identification: Highlights potential errors and failures during execution.
  • Performance Bottleneck Detection: Identifies slow or inefficient steps in the agent workflow.
  • Visualization: Presents traces in a structured, easily understandable format.

Example prompts

  • "Analyze this LangSmith trace ID: [trace_id] and tell me what went wrong."
  • "Show me all the tools used in this trace and their execution times."
  • "Can you identify any potential performance bottlenecks in this agent's workflow based on this trace?"

Tips & gotchas

  • Requires access to LangSmith traces. Ensure your agents are configured to log traces within LangSmith for analysis.