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Distributed Tracing

Checked
wshobson·vlatest·devops·MIT

Tracks requests across multiple services to pinpoint performance bottlenecks and diagnose issues quickly for improved system observability.

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 wshobson/agents --skill distributed-tracing -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

wshobson

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 wshobson/agents at 46891e7e60.

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

Commit
46891e7e60
Manifest
plugins/observability-monitoring/skills/distributed-tracing/SKILL.md
Scanned
2 files, 418 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 distributed-tracing skill enables AI agents to monitor and analyze requests across multiple services in a microservices architecture. It provides visibility into request flow, latency, and error tracking by capturing detailed traces of interactions between different components.

When to use it

  • Debugging complex service-to-service communication issues in production environments
  • Optimizing performance bottlenecks in distributed systems
  • Investigating failures that span multiple services or APIs
  • Ensuring compliance with SLAs by monitoring end-to-end request latency

Key capabilities

  • Captures trace data across microservices
  • Tracks request flow and latency metrics
  • Identifies error points in service interactions
  • Integrates with observability tools for centralized logging

Example prompts

  • "Trace the request path from the user API to the payment gateway."
  • "Identify which service caused a 503 error in the last hour."
  • "Generate a latency report for all services over the past 24 hours."

Tips & gotchas

  • Ensure all microservices are instrumented with tracing agents for full visibility.
  • High cardinality data can increase storage and processing overhead, so consider sampling strategies.