Code Review Checklist
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
定位 Python 脚本的性能瓶颈，集成 cProfile、tracemalloc 和 lineprofiler，一键执行 CPU 热点函数、内存分配或逐行耗时分析，并可输出 JSON 报告。当用户提到优化脚本、分析性能、定位 CPU 或内存热点、进行逐行分析，或询问代码为什么慢、如何加速、内存泄漏等问题时触发。
Install Py Perf Analyzer
$ npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install rongxinzy/RongxinAI py-perf-analyzer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SKILLs/py-perf-analyzer .claude/skills/py-perf-analyzer && rm -rf skills-srcUse ~/.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/
Install the "py-perf-analyzer" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzer into .claude/skills/py-perf-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "py-perf-analyzer", 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 install https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzerType 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.
$ npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install rongxinzy/RongxinAI py-perf-analyzer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .agents/skills && cp -r skills-src/SKILLs/py-perf-analyzer .agents/skills/py-perf-analyzer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "py-perf-analyzer" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzer into .agents/skills/py-perf-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "py-perf-analyzer", 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.
$ npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install rongxinzy/RongxinAI py-perf-analyzer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/SKILLs/py-perf-analyzer .cursor/skills/py-perf-analyzer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "py-perf-analyzer" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzer into .cursor/skills/py-perf-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "py-perf-analyzer", 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 https://github.com/rongxinzy/RongxinAI.git --path SKILLs/py-perf-analyzer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install rongxinzy/RongxinAI py-perf-analyzer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/SKILLs/py-perf-analyzer .gemini/skills/py-perf-analyzer && rm -rf skills-srcUse ~/.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/
Install the "py-perf-analyzer" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzer into .gemini/skills/py-perf-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "py-perf-analyzer", 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.
$ gh skill install rongxinzy/RongxinAI py-perf-analyzerInstalls 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).
$ npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .github/skills && cp -r skills-src/SKILLs/py-perf-analyzer .github/skills/py-perf-analyzer && rm -rf skills-srcUse ~/.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/
Install the "py-perf-analyzer" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzer into .github/skills/py-perf-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "py-perf-analyzer", 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.
$ npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install rongxinzy/RongxinAI py-perf-analyzer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/SKILLs/py-perf-analyzer .opencode/skills/py-perf-analyzer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "py-perf-analyzer" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/SKILLs/py-perf-analyzer into .opencode/skills/py-perf-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "py-perf-analyzer", 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
py-perf-analyzer- GitHub stars
- 154
- Token cost
- ~794 tokens
- SKILL.md length
- 97 words
- Files
- 6 (incl. scripts)
- Skills in repo
- 94
- Repo updated
- First seen
- Licence
- MIT
At a glance
定位 Python 脚本的性能瓶颈，集成 cProfile、tracemalloc 和 lineprofiler，一键执行 CPU 热点函数、内存分配或逐行耗时分析，并可输出 JSON 报告。当用户提到优化脚本、分析性能、定位 CPU 或内存热点、进行逐行分析，或询问代码为什么慢、如何加速、内存泄漏等问题时触发。
- Tasks that involve Performance optimization
- SKILL.md covers 功能, 依赖, 使用方式 and 输出说明
- Runs Python scripts from its folder; calls python and pip
What it does
Py Perf Analyzer is an agent skill from rongxinzy/RongxinAI. 定位 Python 脚本的性能瓶颈，集成 cProfile、tracemalloc 和 lineprofiler，一键执行 CPU 热点函数、内存分配或逐行耗时分析，并可输出 JSON 报告。当用户提到优化脚本、分析性能、定位 CPU 或内存热点、进行逐行分析，或询问代码为什么慢、如何加速、内存泄漏等问题时触发。
Its SKILL.md is about 790 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts (for example `scripts/perf_profile.py` and `zhiyuan/metadata.yaml`).
It sits in Development, covering Performance optimization. It works with Python. The repository describes itself as: An all-in-one local AI Agent workspace with a fully self-developed stack. The licence is MIT.
When your agent uses it
- Tasks that involve Performance optimization
Example prompts
- “/py-perf-analyzer”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9c64865. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonpip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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
Py Perf Analyzer loads about 794 tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 97 words of instructions outside code blocks.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from rongxinzy/RongxinAI at commit 9c64865, republished under its MIT licence (© rongxinzy). 97 words, ~794 tokens.
Download SKILL.mdSave it as
.claude/skills/py-perf-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- py-perf-analyzer
- description
- 定位 Python 脚本的性能瓶颈，集成 cProfile、tracemalloc 和 line_profiler，一键执行 CPU 热点函数、内存分配或逐行耗时分析，并可输出 JSON 报告。当用户提到优化脚本、分析性能、定位 CPU 或内存热点、进行逐行分析，或询问代码为什么慢、如何加速、内存泄漏等问题时触发。
- license
- MIT
- metadata.type
- tool
- metadata.tags
- python, profiling, performance, cProfile, memory, optimization
Perf Profiler
Python 性能分析工具，集成 cProfile、line_profiler 和 tracemalloc，一键定位 CPU 和内存瓶颈。
功能
- CPU 分析 (cProfile)：统计每个函数的调用次数、累计耗时、自身耗时，定位热点函数
- 内存分析 (tracemalloc)：追踪内存分配热点、峰值内存、内存增长来源
- 逐行分析 (line_profiler)：精确到每一行代码的耗时和命中次数，适合深入优化
- 组合分析：
all模式一次执行同时收集 CPU + 内存数据，减少重复运行开销
依赖
| 依赖 | 类型 | 用途 |
|---|---|---|
| Python 3.7+ | 必须 | 运行环境 |
| cProfile / pstats | 内置 | CPU 性能分析 |
| tracemalloc | 内置 | 内存追踪 |
| line_profiler | 可选 | 逐行分析（pip install line_profiler） |
使用方式
python scripts/perf_profile.py <脚本路径> [脚本参数...] [选项]参数
| 参数 | 说明 | 默认值 |
|---|---|---|
script | 要分析的 Python 脚本路径（必填） | - |
script_args | 传递给目标脚本的参数 | 无 |
--mode | 分析模式：cpu / memory / line / all | cpu |
--top | 显示 Top N 结果 | 20 |
--sort | CPU 分析排序：cumulative / tottime / calls | cumulative |
--output | 输出 JSON 报告到文件 | 仅终端输出 |
--function | 逐行分析的目标函数名（逗号分隔） | 自动发现 |
--threshold | 只显示占比超过此值(%)的函数 | 0 |
示例
# CPU 分析（默认模式）
python scripts/perf_profile.py my_script.py
# 内存分析
python scripts/perf_profile.py my_script.py --mode memory
# 逐行分析指定函数
python scripts/perf_profile.py my_script.py --mode line --function compute,process_data
# 全量分析（CPU + 内存），只看 Top 10
python scripts/perf_profile.py my_script.py --mode all --top 10
# 传递参数给目标脚本，输出 JSON 报告
python scripts/perf_profile.py my_script.py --output report.json -- --input data.csv --output result.csv
# 只关注占比 > 5% 的函数
python scripts/perf_profile.py my_script.py --threshold 5输出说明
CPU 分析报告
============================================================
CPU 性能分析报告 (cProfile)
============================================================
总执行时间: 2.3456 秒
总函数调用: 1,234,567 次
分析函数数: 89 个
Top 5 耗时函数:
--------------------------------------------------------
排名 占比 累计(s) 自身(s) 调用 函数
--------------------------------------------------------
1 45.2% 1.0605 0.8234 1000 compute.py:23:matrix_multiply
2 22.1% 0.5183 0.5183 50000 utils.py:45:normalize
3 12.3% 0.2885 0.1200 500 io.py:12:read_batch
...
🔍 主要瓶颈: matrix_multiply (45.2% 时间)
位置: compute.py:23内存分析报告
============================================================
内存分析报告 (tracemalloc)
============================================================
峰值内存: 128.5 MB
当前内存: 64.2 MB
Top 5 内存分配:
--------------------------------------------------------
排名 大小 数量 位置
--------------------------------------------------------
1 45.2 MB 10000 data_loader.py:78
2 22.1 MB 5000 transform.py:45
...
内存增长热点:
--------------------------------------------------------
1 +32.0 MB (+8000) data_loader.py:78
...JSON 报告
使用 --output 参数可导出完整的结构化 JSON 报告，包含所有分析维度的详细数据，便于后续处理或接入 CI 流水线。
© rongxinzy, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (scripts) in SKILLs/py-perf-analyzer of rongxinzy/RongxinAI.
- SKILL.md
- LICENSE
- requirements.txt
- scripts/perf_profile.py
- zhiyuan/icon.png
- zhiyuan/metadata.yaml
Open the folder on GitHubat commit 9c64865
Compare with similar skills
Py Perf Analyzer 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Py Perf Analyzer this skillrongxinzy/RongxinAI | 154 | — | ~794 | Automated safety check: Pass | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 4 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Pycrazyguitar/pysheeet | 8.2k | — | ~886 | Automated safety check: Pass | MIT | |
| Python Performance Optimizationwshobson/agents | 40k | 13 repos | ~814 | Automated safety check: Pass | MIT | |
| Keybase RPC Log Analysiskeybase/client | 9.3k | — | ~3k | Automated safety check: Pass | BSD-3-Clause | |
| The Art of Debuggingstas00/the-art-of-debugging | 1.7k | — | ~6.1k | Automated safety check: Notes | CC-BY-SA-4.0 |
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Python Performance Optimization
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Profiles slow Python code with cProfile and memory profilers, then applies targeted fixes for CPU, memory, I/O and query bottlenecks.
Keybase RPC Log Analysis
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Captures a clean Keybase service log and analyzes it for redundant, duplicated or looping RPCs, then checks whether a caching fix reduced the calls.
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Reviews code for security, performance, quality and maintainability, using a checklist, a finding template and two metrics scripts.
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Questions about Py Perf Analyzer
What does Py Perf Analyzer do?
定位 Python 脚本的性能瓶颈，集成 cProfile、tracemalloc 和 lineprofiler，一键执行 CPU 热点函数、内存分配或逐行耗时分析，并可输出 JSON 报告。当用户提到优化脚本、分析性能、定位 CPU 或内存热点、进行逐行分析，或询问代码为什么慢、如何加速、内存泄漏等问题时触发。. Py Perf Analyzer is an agent skill from rongxinzy/RongxinAI.
When should I use Py Perf Analyzer?
Py Perf Analyzer fits situations like: tasks that involve Performance optimization.
How do I install Py Perf Analyzer in Claude Code?
Run `npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a claude-code`. Or copy the skill folder (SKILLs/py-perf-analyzer in rongxinzy/RongxinAI) into .claude/skills/py-perf-analyzer in your project. Claude Code loads it when a task matches its description.
How do I install Py Perf Analyzer in Codex?
Run `npx skills add rongxinzy/RongxinAI --skill py-perf-analyzer -a codex`. Or copy the skill folder (SKILLs/py-perf-analyzer in rongxinzy/RongxinAI) into .agents/skills/py-perf-analyzer in your project. Codex loads it when a task matches its description.
Can I use Py Perf Analyzer 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 rongxinzy/RongxinAI --skill py-perf-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/py-perf-analyzer, .gemini/skills/py-perf-analyzer, .github/skills/py-perf-analyzer and .opencode/skills/py-perf-analyzer in your project.
What does Py Perf Analyzer need to run?
Going by SKILL.md and its folder, Py Perf Analyzer needs Python for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3.
Does Py Perf Analyzer access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Py Perf Analyzer 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Py Perf Analyzer use?
Py Perf Analyzer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Py Perf Analyzer use?
About 794 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Py Perf Analyzer?
Skills that share tags, products or a category with Py Perf Analyzer: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Py (crazyguitar/pysheeet, 8.2k stars), Python Performance Optimization (wshobson/agents, 40k stars) and Keybase RPC Log Analysis (keybase/client, 9.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Py Perf Analyzer?
rongxinzy (a GitHub organization) maintains it in rongxinzy/RongxinAI, which has 154 GitHub stars. The repository holds 94 skills in this directory. The repository was last updated on October 10, 2026.
Source: rongxinzy/RongxinAI on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.