# Python Performance Optimization

> Profile and optimize Python code using cProfile, memory profilers, and performance best practices.

- **Type:** Skill
- **Install:** `agentstack add skill-jartan-llc-grimoire-python-performance-optimization`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Jartan-LLC](https://agentstack.voostack.com/s/jartan-llc)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Jartan-LLC](https://github.com/Jartan-LLC)
- **Source:** https://github.com/Jartan-LLC/grimoire/tree/main/plugins/pythonica/skills/python-performance-optimization

## Install

```sh
agentstack add skill-jartan-llc-grimoire-python-performance-optimization
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

## Core Concepts

### 1. Profiling Types

- **CPU Profiling**: Identify time-consuming functions
- **Memory Profiling**: Track memory allocation and leaks
- **Line Profiling**: Profile at line-by-line granularity
- **Call Graph**: Visualize function call relationships

### 2. Performance Metrics

- **Execution Time**: How long operations take
- **Memory Usage**: Peak and average memory consumption
- **CPU Utilization**: Processor usage patterns
- **I/O Wait**: Time spent on I/O operations

### 3. Optimization Strategies

- **Algorithmic**: Better algorithms and data structures
- **Implementation**: More efficient code patterns
- **Parallelization**: Multi-threading/processing
- **Caching**: Avoid redundant computation
- **Native Extensions**: C/Rust for critical paths

## Quick Start

### Basic Timing

```python
import time

def measure_time():
    """Simple timing measurement."""
    start = time.time()

    # Your code here
    result = sum(range(1000000))

    elapsed = time.time() - start
    print(f"Execution time: {elapsed:.4f} seconds")
    return result

# Better: use timeit for accurate measurements
import timeit

execution_time = timeit.timeit(
    "sum(range(1000000))",
    number=100
)
print(f"Average time: {execution_time/100:.6f} seconds")
```

## Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient. For advanced patterns, see `references/advanced-patterns.md`.

## Best Practices

1. **Profile before optimizing** - Measure to find real bottlenecks
2. **Focus on hot paths** - Optimize code that runs most frequently
3. **Use appropriate data structures** - Dict for lookups, set for membership
4. **Avoid premature optimization** - Clarity first, then optimize
5. **Use built-in functions** - They're implemented in C
6. **Cache expensive computations** - Use lru_cache
7. **Batch I/O operations** - Reduce system calls
8. **Use generators** for large datasets
9. **Consider NumPy** for numerical operations
10. **Profile production code** - Use py-spy for live systems

## Common Pitfalls

- Optimizing without profiling
- Using global variables unnecessarily
- Not using appropriate data structures
- Creating unnecessary copies of data
- Not using connection pooling for databases
- Ignoring algorithmic complexity
- Over-optimizing rare code paths
- Not considering memory usage

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Jartan-LLC](https://github.com/Jartan-LLC)
- **Source:** [Jartan-LLC/grimoire](https://github.com/Jartan-LLC/grimoire)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-jartan-llc-grimoire-python-performance-optimization
- Seller: https://agentstack.voostack.com/s/jartan-llc
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.