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Python Observability

Checked
wshobson·vlatest·backend·MIT

Collects Python application metrics, logs, and traces to enable debugging and performance optimization in backend systems.

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 python-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

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/python-development/skills/python-observability/SKILL.md
Scanned
2 files, 385 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 python-observability skill enables AI agents to monitor, log, and analyze Python applications in real time. It integrates with popular observability tools like Prometheus, Grafana, and OpenTelemetry to provide insights into application performance, errors, and usage patterns.

When to use it

  • Debugging complex Python backend services by tracking logs and metrics.
  • Monitoring production systems for anomalies or performance bottlenecks.
  • Building dashboards in Grafana to visualize system behavior over time.
  • Implementing distributed tracing across microservices using OpenTelemetry.

Key capabilities

  • Integration with Prometheus for metric collection.
  • Support for logging via structured formats like JSON.
  • Distributed tracing with OpenTelemetry.
  • Real-time alerting based on predefined thresholds.
  • Compatibility with Grafana for visualization and monitoring.

Example prompts

  • "Set up Prometheus to monitor response times of my Flask API."
  • "Generate a dashboard in Grafana showing error rates over the last 24 hours."
  • "Add OpenTelemetry tracing to track requests across multiple microservices."

Tips & gotchas

  • Ensure your Python environment has all required dependencies installed, such as prometheus-client and opentelemetry-api.
  • Observability tools may require configuration adjustments for proper data collection and visualization.