Python Observability

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by l3digitalnet · vlatest · Repository

Monitors Python backend applications using metrics, logs, and traces for proactive issue detection and performance optimization.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add l3digitalnet-python-observability npx -- -y @trustedskills/l3digitalnet-python-observability
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "l3digitalnet-python-observability": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/l3digitalnet-python-observability"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

This skill allows AI agents to analyze Python code and provide observability insights. It can identify potential performance bottlenecks, resource leaks, and other issues that might impact application health. The agent uses static analysis techniques to generate reports on code complexity, dependencies, and potential vulnerabilities.

When to use it

  • Debugging Performance Issues: Use this skill when you're experiencing slow response times or high resource consumption in a Python application.
  • Code Review & Quality Assurance: Integrate the skill into your development workflow for automated code review and quality checks.
  • Identifying Dependencies: Quickly understand the dependencies of a Python project to assess potential security risks or upgrade paths.
  • Refactoring Complex Code: Use it to identify areas in your codebase that could benefit from refactoring to improve maintainability and performance.

Key capabilities

  • Static code analysis
  • Dependency graph generation
  • Performance bottleneck identification
  • Resource leak detection
  • Code complexity assessment

Example prompts

  • "Analyze this Python script for potential memory leaks: [paste code]"
  • "Generate a dependency graph for my project located at [path to project]."
  • "Identify performance bottlenecks in this function: [paste code]"

Tips & gotchas

  • The skill works best with well-structured Python code. Complex or obfuscated code may yield less accurate results.
  • Ensure the agent has access to the relevant codebase for analysis.

Tags

🛡️

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Details

Version
vlatest
License
Author
l3digitalnet
Installs
4

🌐 Community

Passed automated security scans.