# Python Performance Optimization > Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance. Use when this capability is needed. - Skill: `tomevault-io/python-performance-optimization-6` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/python-performance-optimization-6` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/python-performance-optimization-6/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/python-performance-optimization-6 --- # Python Performance Optimization Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices. ## Use this skill when - Identifying performance bottlenecks in Python applications - Reducing application latency and response times - Optimizing CPU-intensive operations - Reducing memory consumption and memory leaks - Improving database query performance - Optimizing I/O operations - Speeding up data processing pipelines - Implementing high-performance algorithms - Profiling production applications ## Do not use this skill when - The task is unrelated to python performance optimization - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed patterns and examples. --- ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior error resolutions and debugging strategies. The hybrid search excels here — BM25 finds exact error codes/stack traces while vectors find semantically similar past issues. ```bash # Check for prior debugging/diagnostics context before starting python3 execution/memory_manager.py auto --query "error patterns and debugging solutions for Python Performance Optimization" ``` ### Storing Results After completing work, store debugging/diagnostics decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "Root cause: memory leak from unclosed DB connections in pool — fixed with context manager" \ --type error --project \ --tags python-performance-optimization debugging ``` ### Multi-Agent Collaboration Store error resolutions so any agent encountering the same issue retrieves the fix instantly instead of re-debugging. ```bash python3 execution/cross_agent_context.py store \ --agent "" \ --action "Debugged and resolved critical issue — root cause documented for future reference" \ --project ``` ### Self-Annealing Loop When this skill resolves an error, store the fix in memory AND update the relevant directive. The system gets stronger with each resolved issue. ### BM25 Exact Match Error codes, stack traces, and log messages are best found via BM25 keyword search. The hybrid system automatically uses exact matching for these patterns. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.