Agent skill

Python Performance Optimization

Profiles slow Python code with cProfile and memory profilers, then applies targeted fixes for CPU, memory, I/O and query bottlenecks.

MITAuto-check passedDevelopment

Install Python Performance Optimization

skills CLI

$ npx skills add wshobson/agents --skill python-performance-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install wshobson/agents python-performance-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/python-development/skills/python-performance-optimization .claude/skills/python-performance-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "python-performance-optimization" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization into .claude/skills/python-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-performance-optimization", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add wshobson/agents --skill python-performance-optimization -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install wshobson/agents python-performance-optimization --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/python-development/skills/python-performance-optimization .agents/skills/python-performance-optimization && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "python-performance-optimization" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization into .agents/skills/python-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-performance-optimization", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wshobson/agents --skill python-performance-optimization -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install wshobson/agents python-performance-optimization --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/python-development/skills/python-performance-optimization .cursor/skills/python-performance-optimization && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "python-performance-optimization" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization into .cursor/skills/python-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-performance-optimization", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/wshobson/agents.git --path plugins/python-development/skills/python-performance-optimization

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add wshobson/agents --skill python-performance-optimization -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install wshobson/agents python-performance-optimization --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/python-development/skills/python-performance-optimization .gemini/skills/python-performance-optimization && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "python-performance-optimization" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization into .gemini/skills/python-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-performance-optimization", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install wshobson/agents python-performance-optimization

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add wshobson/agents --skill python-performance-optimization -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/python-development/skills/python-performance-optimization .github/skills/python-performance-optimization && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "python-performance-optimization" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization into .github/skills/python-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-performance-optimization", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wshobson/agents --skill python-performance-optimization -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install wshobson/agents python-performance-optimization --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/python-development/skills/python-performance-optimization .opencode/skills/python-performance-optimization && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "python-performance-optimization" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization into .opencode/skills/python-performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-performance-optimization", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: python-performance-optimization
GitHub stars: 40k
Used in: 12 other repos
Token cost: ~814 tokens
SKILL.md length: 295 words
Files: 3 (incl. references)
Skills in repo: 142
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Profiles slow Python code with cProfile and memory profilers, then applies targeted fixes for CPU, memory, I/O and query bottlenecks.

Works in 3 steps: Profiling Types → Performance Metrics → Optimization Strategies
Finding out why a Python function or endpoint is slow
SKILL.md covers When to Use This Skill, Core Concepts, Quick Start and Detailed patterns and worked…, plus 2 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill organizes performance work around measurement first. It separates CPU profiling, memory profiling, line-by-line profiling and call graphs, tracks metrics such as execution time, peak memory, CPU use and I/O wait, and then picks among four kinds of fix: better algorithms and data structures, tighter implementation patterns, parallelism and caching, with native extensions in C or Rust left for critical paths.

Its best-practice list includes profiling before changing anything, focusing on hot paths, using the right built-in data structure, preferring built-in functions, caching with lru_cache, batching I/O, using generators for large datasets, considering NumPy for numeric work and using py-spy on live systems. Longer worked examples sit in references/details.md and references/advanced-patterns.md, which the agent reads when the main file is not enough.

When your agent uses it

Finding out why a Python function or endpoint is slow
Reducing memory consumption or tracking down a suspected leak
Speeding up a data processing pipeline or I/O-heavy job
Profiling a production Python service without stopping it

Example prompts

“Profile report_builder.py with cProfile and tell me which functions take the most time.”
“This script's memory grows without bound; find the leak and fix it.”
“Our CSV import takes minutes; speed it up and show before and after timings.”
“How can I profile the live API process with py-spy?”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Profiling Types
Performance Metrics
Optimization Strategies

What it can do on your machine

Read from SKILL.md and the folder at commit 46891e7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Python Performance Optimization loads about 814 tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 295 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~57
When it runs · the whole SKILL.md, loaded when a task matches: ~814
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wshobson/agents at commit 46891e7, republished under its MIT licence (© wshobson). 295 words, ~814 tokens.

Download SKILL.mdSave it as .claude/skills/python-performance-optimization/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.

name: python-performance-optimization
description: Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python Performance Optimization

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

When to Use This Skill

Identifying performance bottlenecks in Python applications
Reducing application latency and response times
Optimizing CPU-intensive operations
Reducing memory consumption and memory leaks
Improving database query performance
Optimizing I/O operations
Speeding up data processing pipelines
Implementing high-performance algorithms
Profiling production applications

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.

Best Practices

Profile before optimizing - Measure to find real bottlenecks
Focus on hot paths - Optimize code that runs most frequently
Use appropriate data structures - Dict for lookups, set for membership
Avoid premature optimization - Clarity first, then optimize
Use built-in functions - They're implemented in C
Cache expensive computations - Use lru_cache
Batch I/O operations - Reduce system calls
Use generators for large datasets
Consider NumPy for numerical operations
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

Files

SKILL.md and 2 other files (references) in plugins/python-development/skills/python-performance-optimization of wshobson/agents.

SKILL.md
references/advanced-patterns.md
references/details.md

Open the folder on GitHubat commit 46891e7

Used in 12 other repositories

We found 25 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 12 other GitHub owners. This page covers the copy in wshobson/agents, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Python Performance Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Python Performance Optimization compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Python Performance Optimization this skillwshobson/agents	40k	12 repos	~814	Automated safety check: Pass	MIT	2 days ago
Keybase RPC Log Analysiskeybase/client	9.3k	—	~3k	Automated safety check: Pass	BSD-3-Clause	today
The Art of Debuggingstas00/the-art-of-debugging	1.7k	—	~6.1k	Automated safety check: Notes	CC-BY-SA-4.0	today
LoopX Performance Diagnosisloopx-project/loopx	6.2k	—	~880	Automated safety check: Pass	Apache-2.0	today
Memory Optimizationbenchflow-ai/skillsbench	1.8k	—	~1.5k	Automated safety check: Pass	Apache-2.0	2 mo ago
Torch Performance Optimizationalbumentations-team/albucore	123	—	~895	Automated safety check: Pass	MIT	2 days ago

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Questions about Python Performance Optimization

What does Python Performance Optimization do?

Profiles slow Python code with cProfile and memory profilers, then applies targeted fixes for CPU, memory, I/O and query bottlenecks. The skill organizes performance work around measurement first. It separates CPU profiling, memory profiling, line-by-line profiling and call graphs, tracks metrics such as execution time, peak memory, CPU use and I/O wait, and then picks among four kinds of fix: better algorithms and data structures, tighter implementation patterns, parallelism and caching, with native extensions in C or Rust left for critical paths.

When should I use Python Performance Optimization?

Python Performance Optimization fits situations like: finding out why a Python function or endpoint is slow; reducing memory consumption or tracking down a suspected leak; speeding up a data processing pipeline or I/O-heavy job; profiling a production Python service without stopping it.

How do I install Python Performance Optimization in Claude Code?

Run `npx skills add wshobson/agents --skill python-performance-optimization -a claude-code`. Or copy the skill folder (plugins/python-development/skills/python-performance-optimization in wshobson/agents) into .claude/skills/python-performance-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Python Performance Optimization in Codex?

Run `npx skills add wshobson/agents --skill python-performance-optimization -a codex`. Or copy the skill folder (plugins/python-development/skills/python-performance-optimization in wshobson/agents) into .agents/skills/python-performance-optimization in your project. Codex loads it when a task matches its description.

Can I use Python Performance Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wshobson/agents --skill python-performance-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-performance-optimization, .gemini/skills/python-performance-optimization, .github/skills/python-performance-optimization and .opencode/skills/python-performance-optimization in your project.

What does Python Performance Optimization need to run?

SKILL.md names no scripts, command-line tools or credentials: Python Performance Optimization is instructions for the agent only.

Does Python Performance Optimization access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Python Performance Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Python Performance Optimization use?

Python Performance Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Python Performance Optimization use?

About 814 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.5k tokens, read only when the agent opens those files.

What are the alternatives to Python Performance Optimization?

Skills that share tags, products or a category with Python Performance Optimization: Keybase RPC Log Analysis (keybase/client, 9.3k stars), The Art of Debugging (stas00/the-art-of-debugging, 1.7k stars), LoopX Performance Diagnosis (loopx-project/loopx, 6.2k stars) and Memory Optimization (benchflow-ai/skillsbench, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Python Performance Optimization?

wshobson (a GitHub user) maintains it in wshobson/agents, which has 40,254 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on October 5, 2026.

Source: wshobson/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Python Performance Optimization

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Python Performance Optimization

When to Use This Skill

Core Concepts

1. Profiling Types

2. Performance Metrics

3. Optimization Strategies

Quick Start

Basic Timing

Detailed patterns and worked examples

Best Practices

Common Pitfalls

Files

Used in 12 other repositories

Compare with similar skills

Similar skills

More from wshobson/agents

Related

Questions about Python Performance Optimization