Diagnose live Python processes with peeka-cli to identify bottlenecks and memory leaks.
npx skills add https://github.com/wwulfric/peeka --skill peeka-diagnostics
Please help me install this Agent Skill. Skill: peeka-diagnostics Source: https://github.com/wwulfric/peeka/tree/main/.agents/skills/peeka-diagnostics Command: npx skills add https://github.com/wwulfric/peeka --skill peeka-diagnostics
Diagnosing live Python applications non-invasively to identify slowdowns, memory leaks, thread contention, and misbehavior without requiring code changes.
Install peeka-cli for Python 3.8+ and attach to a running process with peeka-cli attach <pid>, then begin observations with a simple watch command such as peeka-cli watch 'module.func' -n 5.
High-intent search queries and answers about installing and using this skill.
Non-invasive runtime diagnostics let you diagnose a live Python process without code changes. You attach a diagnostic tool to the running process to inspect performance bottlenecks, memory leaks, and thread issues directly.
You can trace function calls, watch variables, inspect thread stacks, and analyze memory in a running Python application. These observability commands identify bottlenecks and misbehavior without requiring application restarts or modifications.
Yes, runtime tracing applies to debugging Python applications in both development and production. Guided workflows include environment detection and permission checks to ensure safe diagnostic processes when inspecting live production environments.
The best way to watch a variable is attaching to the live Python process and targeting the specific function. You execute a watch command on the module function to observe variable values across multiple executions without altering source code.
Inspecting thread issues requires passing permission checks within the diagnostic workflow. The tool performs environment detection and validates permissions before attaching to ensure safe and authorized access to the live Python process.
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