# 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.

- Skill: `techwavedev/python-performance-optimization` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add techwavedev/python-performance-optimization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/python-performance-optimization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/python-performance-optimization

---


# 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-INTEGRATION-START -->

## 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 <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 "<your-agent>" \
  --action "Debugged and resolved critical issue — root cause documented for future reference" \
  --project <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.

<!-- AGI-INTEGRATION-END -->