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Python Micrometer Gcp Cloud Monitoring

Checked
dawiddutoit·vlatest·cloud

Helps with Python, Google Cloud, monitoring as part of developing backend services and APIs workflows.

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 dawiddutoit/custom-claude --skill python-micrometer-gcp-cloud-monitoring -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

dawiddutoit

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 dawiddutoit/custom-claude at 17b45a90a6.

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

Commit
17b45a90a6
Manifest
skills/python-micrometer-gcp-cloud-monitoring/SKILL.md
Scanned
3 files, 484 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 Python applications to instrument metrics and export them directly to Google Cloud Monitoring using Micrometer. It provides a standardized way to collect application performance data, allowing for centralized monitoring and alerting within the GCP ecosystem. This facilitates deeper insights into application behavior and resource utilization.

When to use it

  • Monitoring Application Performance: Track key metrics like request latency, error rates, and CPU usage in Python applications deployed on Google Cloud Platform.
  • Troubleshooting Production Issues: Quickly identify performance bottlenecks or errors by correlating application metrics with Cloud Monitoring dashboards.
  • Capacity Planning: Analyze resource utilization trends to optimize infrastructure costs and ensure adequate capacity for future growth.
  • Observability Integration: Integrate custom Python applications into existing GCP observability pipelines.

Key capabilities

  • Micrometer instrumentation for Python applications.
  • Automatic metric export to Google Cloud Monitoring.
  • Standardized metric naming and tagging.
  • Support for various Micrometer registries.

Example prompts

  • "Instrument the process_order function in my application to track its execution time."
  • "Configure this skill to send metrics every 5 seconds to Cloud Monitoring."
  • “Show me how to add custom tags to my request latency metric.”

Tips & gotchas

  • Requires a Google Cloud project with appropriate permissions to write metrics to Cloud Monitoring.
  • Ensure Micrometer is properly configured within your Python application before using this skill.