Claude-skill-registry benchmark-functions

Measure function performance and compare implementations. Use when optimizing critical code paths.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/benchmark-functions" ~/.claude/skills/majiayu000-claude-skill-registry-benchmark-functions && rm -rf "$T"

manifest: skills/data/benchmark-functions/SKILL.md

Benchmark Functions

Systematically measure function execution time, memory usage, and performance characteristics to identify optimization opportunities.

When to Use

Comparing different algorithm implementations
Measuring performance before/after optimization
Profiling SIMD vs scalar implementations
Establishing performance baselines for CI/CD

Quick Reference

# Python benchmarking with timeit
python3 -m timeit -s 'import module' 'module.function(args)' -n 1000 -r 5

# Mojo benchmarking with built-in timing
mojo run benchmark_script.mojo

Workflow

Set up benchmarks: Create timing harness with warm-up iterations
Run measurements: Execute function multiple times, record timing
Collect statistics: Calculate mean, median, std deviation
Compare baselines: Compare against previous implementations
Identify bottlenecks: Pinpoint functions needing optimization

Output Format

Benchmark report:

Function name and parameters tested
Execution time statistics (mean, median, min, max)
Memory usage (if applicable)
Comparison to baseline (improvement percentage)
Iterations and sample size used

References

See
```
profile-code
```
skill for detailed performance profiling
See
```
suggest-optimizations
```
skill for improvement strategies
See CLAUDE.md > Performance for Mojo optimization guidelines