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Performance optimizer

Skill rudrathegreat/Astronomy-AI-Toolkit/skills/software_engineering/performance_optimizer

A catered AI toolkit for astronomers

Install
npx -y skills add rudrathegreat/Astronomy-AI-Toolkit --skill performance_optimizer

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What its author says it does

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

SKILL.md

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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

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:

from numba import njit
@njit
def compute_orbit(t, p, e):
    # Fast math operations only
    return results

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most performance cost skills give in 379 tokens

Counted across 803 of the 1,058 authors here whose files we hold, read 2026-08-07

  • Keep skill files under 500 lines or tokensin 82 of 803, across 16 files
  • Use imperative form in instructionsin 80 of 803, across 9 files
  • Draft assertions while test runs are in progressin 75 of 803, across 9 files
  • Create two to three realistic test promptsin 74 of 803, across 9 files
  • Write skill descriptions to be pushyin 72 of 803, across 7 files
  • Save test cases to evals JSONin 72 of 803, across 6 files
  • Ask questions about edge cases and input formatsin 72 of 803, across 7 files
  • Save timing data immediately when runs completein 70 of 803, across 5 files
  • Include all trigger conditions in the skill descriptionin 69 of 803, across 3 files
  • Launch all test runs in a single turn or simultaneouslyin 69 of 803, across 3 files
  • Capture intent before writing a skillin 67 of 803, across 1 file
  • Import directly instead of barrel filesin 52 of 803, across 15 files

Said here and by no other author read

  • vectorize loops using numpy array operations
  • apply numba jit compilation to performance-critical loops
  • cache redundant computations
  • verify mathematical equivalence of optimized code

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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