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Distributed Tracing

Checked
aj-geddes·vlatest·devops·MIT

This skill provides distributed tracing data to pinpoint performance bottlenecks across complex systems, improving debugging and optimization efforts.

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 aj-geddes/useful-ai-prompts --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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/distributed-tracing/SKILL.md
Scanned
8 files, 343 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 generate structured distributed tracing data, specifically formatted as JSON. It allows agents to simulate or log system events with timestamps and service identifiers for observability purposes.

When to use it

  • Debugging complex microservice architectures where request flows span multiple components.
  • Generating synthetic trace data for testing monitoring dashboards like Jaeger or Zipkin.
  • Creating reproducible logs to analyze latency bottlenecks in distributed systems.
  • Validating that your observability pipeline correctly parses JSON-formatted events.

Key capabilities

  • Outputs tracing data strictly in valid JSON format.
  • Includes standard fields such as trace ID, span ID, parent span ID, and timestamps.
  • Supports custom attributes to tag specific services or operations within the trace.
  • Generates hierarchical span structures representing request lifecycles.

Example prompts

"Generate a distributed trace JSON for a user login request flowing through an API gateway, auth service, and database." "Create a synthetic trace with three spans showing high latency in the payment processing step." "Output a JSON trace object including custom attributes for 'user_id' and 'transaction_type'."

Tips & gotchas

Ensure your observability backend is configured to ingest JSON payloads if you plan to visualize this data immediately. This skill focuses on data generation; it does not automatically connect to live tracing infrastructure or collect real-world metrics without additional agent logic.