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Langsmith Observability

Listed
davila7·vlatest·devops·MIT

Langsmith Observability monitors your LLM deployments, surfacing insights into performance and errors to improve reliability & troubleshoot issues quickly.

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 davila7/claude-code-templates --skill langsmith-observability -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

davila7

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 2 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: proxy.company.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

    proxy.company.comundeclared

    What it found

    • cli-tool/components/skills/ai-research/observability-langsmith/references/troubleshooting.md:464contacts proxy.company.com
      os.environ["HTTP_PROXY"] = "http://proxy.company.com:8080"
    • cli-tool/components/skills/ai-research/observability-langsmith/references/troubleshooting.md:465contacts proxy.company.com
      os.environ["HTTPS_PROXY"] = "http://proxy.company.com:8080"

  • No credential access: failed

    Touches credential material in 1 place.

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

    What it found

    • cli-tool/components/skills/ai-research/observability-langsmith/references/troubleshooting.md:464environment dumped into a network call
      os.environ["HTTP_PROXY"] = "http://proxy.company.com:8080"

  • 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 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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/observability-langsmith/SKILL.md
Scanned
3 files, 1,366 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

langsmith-observability

What it does

This skill enables AI agents to integrate LangSmith for comprehensive observability of language model operations. It allows agents to trace, monitor, and debug interactions with LLMs in real-time, ensuring reliable performance across complex workflows.

When to use it

  • Debugging latency issues: Identify bottlenecks when API calls to models take longer than expected.
  • Quality assurance: Monitor evaluation metrics to ensure generated outputs meet specific accuracy standards.
  • Cost tracking: Analyze token usage patterns to optimize spending on expensive model endpoints.
  • Production monitoring: Maintain visibility into agent behavior after deployment to catch regressions early.

Key capabilities

  • End-to-end tracing of LLM requests and responses
  • Real-time performance metrics collection
  • Automated evaluation framework integration
  • Debugging support for complex multi-step agent chains

Example prompts

  • "Set up LangSmith tracing for my current RAG pipeline to visualize token usage per step."
  • "Create an evaluation run comparing the latest model version against baseline accuracy scores."
  • "Generate a trace report showing where latency spikes occurred during yesterday's production logs."

Tips & gotchas

Ensure your LangSmith project is properly configured with API keys before attempting integration. This skill requires active network access to the LangSmith backend; offline agents will not be able to send telemetry data.