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profile-code

Imported from https://github.com/mvillmow/ProjectOdyssey.

Packaged view

This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.

Stars

Hot score

Updated

March 20, 2026

Overall rating

C3.9

Composite score

3.9

Best-practice grade

A92.0

Install command

npx @skill-hub/cli install mvillmow-projectodyssey-profile-code

Repository

mvillmow/ProjectOdyssey

Skill path: .claude/skills/tier-2/profile-code

Imported from https://github.com/mvillmow/ProjectOdyssey.

Open repository

Best for

Primary workflow: Ship Full Stack.

Technical facets: Full Stack.

Target audience: everyone.

License: Unknown.

Original source

Catalog source: SkillHub Club.

Repository owner: mvillmow.

This is still a mirrored public skill entry. Review the repository before installing into production workflows.

What it helps with

Install profile-code into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
Review https://github.com/mvillmow/ProjectOdyssey before adding profile-code to shared team environments
Use profile-code for development workflows

Works across

Claude CodeCodex CLIGemini CLIOpenCode

Favorites: 0.

Sub-skills: 0.

Aggregator: No.

Original source / Raw SKILL.md

---
name: profile-code
description: "Measure execution time and memory usage of code. Use when analyzing performance characteristics."
mcp_fallback: none
category: analysis
tier: 2
---

# Profile Code

Use profiling tools to measure CPU time, memory allocation, and identify performance bottlenecks in code.

## When to Use

- Finding performance bottlenecks
- Measuring CPU vs memory tradeoffs
- Understanding where code spends most time
- Optimizing hot code paths

## Quick Reference

```bash
# Python CPU profiling with cProfile
python3 -m cProfile -s cumulative script.py | head -30

# Memory profiling
pip install memory-profiler
python3 -m memory_profiler script.py

# Detailed call graph
pip install graphviz
python3 -m pstats /tmp/profile.prof
```

## Workflow

1. **Select profiler**: Choose appropriate tool (cProfile for CPU, memory_profiler for memory)
2. **Run with instrumentation**: Execute code with profiling enabled
3. **Capture metrics**: Record timing and memory data
4. **Analyze output**: Identify top time consumers and memory hogs
5. **Report findings**: Document bottlenecks with before/after context

## Output Format

Profiling report:

- Top functions by execution time
- Call count for each function
- Memory allocation per function
- Call graph/call tree
- Percentage of total time/memory per function
- Recommendations for optimization

## References

- See `suggest-optimizations` skill for improvement recommendations
- See `benchmark-functions` skill for measuring improvements
- See CLAUDE.md > Performance for optimization guidelines