python-performance-optimization
- Repo stars 38,402
- License MIT
- Author updated Live
- Author repo antigravity-awesome-skills
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @sickn33 · MIT
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: python-performance-optimization
description: Profile and optimize Python code using cProfile, memory profilers, and performance best practice…
category: other
runtime: Python
---
# python-performance-optimization output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Use this skill when / Do not use this skill when / Instructions” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “Use this skill when / Do not use this skill when / Instructions” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “Use this skill when / Do not use this skill when / Instructions”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: python-performance-optimization
description: Profile and optimize Python code using cProfile, memory profilers, and performance best practice…
category: other
source: sickn33/antigravity-awesome-skills
---
# python-performance-optimization
## When to use
- Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Use this skill when / Do not use this skill when / Instructions” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "python-performance-optimization" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Use this skill when / Do not use this skill when / Instructions
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Python Performance Optimization
Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.
Use this skill when
- 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
Do not use this skill when
- The task is unrelated to python performance optimization
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Resources
resources/implementation-playbook.mdfor detailed patterns and examples.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review