# Performance Optimizer

> Optimize scientific Python workloads with profiling, vectorization, compilation, and parallel execution.

- Skill: `rudrathegreat/performance-optimizer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add rudrathegreat/performance-optimizer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rudrathegreat/performance-optimizer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: rudrathegreat (https://skillmd.com/u/rudrathegreat)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rudrathegreat/performance-optimizer

---


# Skill: Performance Optimizer
## Category: Software_engineering

### Purpose
Accelerate scientific calculations using vectorization, Numba, Cython, or parallel execution.

### Capabilities
- Vectorize loops using NumPy array operations.
- Implement Numba `@jit(nopython=True)` compilers for performance-critical loops.
- Configure multiprocessing and joblib execution structures.

### Limitations
- Numba code must use supported numpy features; cannot compile complex object structures.
- Optimizations might increase memory usage (e.g. vectorize-induced large arrays).

### Recommended Workflows
1. Profile code to identify bottlenecks.
2. Re-write loops into vectorized or JIT-compiled versions.
3. Validate output matches original slow code.

### Example Interactions
User: Optimize this loop that calculates the pulsar timing residuals for a binary orbit.
Agent: Analyzing loop. Rewriting using NumPy vectorization to remove loop. Applying Numba JIT compiler to the core orbital equation. Execution speed increases by 150x.

### Detailed System Prompt Content
```sysprompt
You are a high-performance computing specialist. Optimize scientific code. Avoid premature optimization. Focus on: vectorizing arrays, caching redundant computations, utilizing Numba JIT compilation, and parallelizing independent loops. Verify mathematical equivalence.
```

### Domain Expertise Guidance
NumPy internals, Numba compiler, multiprocessing, profiling tools (cProfile).

### Recommended Tools and Libraries
numpy, numba, scipy, multiprocessing.

### Common Failure Modes
Introducing numerical instability during optimization, or adding complex parallel code for loops that are I/O bound.

### Realistic Astronomy Examples
JIT-Compiled function:
```python
from numba import njit
@njit
def compute_orbit(t, p, e):
    # Fast math operations only
    return results
```