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python-auto-dev（Python auto DEV 测试）

Agent Skill

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。它适合让 Agent 阅读 Python 代码、定位测试问题、整理运行命令、生成脚本或分析数据处理逻辑。使用时需要确认项目虚拟环境、依赖版本和测试入口；涉及执行脚本、读写文件、访问数据库或调用外部 API 时，应先明确运行目录和输入输出范围，避免误改生产数据。

总安装

8,152

周安装

333

GitHub Stars

公开资料未说明

下载量

2,611
OpenClaw

安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手，先确认命令和权限，再让它执行。

请帮我安装这个 Agent Skill：python-auto-dev（Python auto DEV 测试）
来源仓库：https://github.com/aptjason/python-auto-dev
安装命令：
openclaw skills install python-auto-dev
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

ClawHubOpenClaw
openclaw skills install python-auto-dev

简介

在配置的 conda 环境中自动生成、测试、调试和优化 Python 代码，管理 H:\code\Daily 中的所有项目文件。

SKILL.md

name
python-auto-dev
description
Automated Python code generation, testing, debugging, and optimization with integrated conda environment management. Uses default conda path "C:\anaconda3\condabin\conda.bat" and environment "py311". Project files are stored at H:\code\Daily. Use when Codex needs to: (1) Generate Python code from specifications, (2) Create and run automated tests, (3) Debug code with interactive tools, (4) Optimize performance and code quality, (5) Manage conda environments for Python projects. This skill bundles executable scripts that handle the entire Python development workflow end-to-end.

Python Auto-Dev Skill

Complete automation for Python development: generate code from specs, add tests, debug, and optimize—all with the configured conda environment.

Quick Start

When a user provides a coding task:

  1. Generate Python code based on their requirements
  2. Write unit tests using pytest or unittest
  3. Run tests and capture output
  4. Debug failures automatically
  5. Optimize with profiling and linting
  6. Deliver final code with test report

All operations use the py311 conda environment at C:\anaconda3\condabin\conda.bat and store files under H:\code\Daily.

Workflow

Phase 1: Code Generation

Use scripts/generate_code.py to create Python code from a specification. The script accepts:

  • spec: Natural language description of what the code should do
  • output_path: Where to save the generated file (default: H:\code\Daily\generated_<timestamp>.py)

The generated code should include:

  • Type hints
  • Docstrings
  • Basic error handling
  • Modular design

Phase 2: Test Creation

After code is generated, use scripts/create_tests.py to produce comprehensive unit tests:

  • Tests edge cases
  • Tests error conditions
  • Uses pytest fixtures where appropriate
  • Outputs to H:\code\Daily\ ests\

Phase 3: Test Execution & Debugging

Run tests with scripts/run_tests.py:

  • Activates the conda environment
  • Executes pytest with verbose output
  • Captures results in a report file

If tests fail, invoke scripts/debug_code.py:

  • Analyzes traceback
  • Suggests fixes
  • Can patch the code automatically (with confirmation)

Phase 4: Optimization

Once tests pass, use scripts/optimize_code.py:

  • Runs profiling (cProfile)
  • Checks code quality (pylint/flake8)
  • Suggests optimizations
  • Can apply safe optimizations automatically

Scripts Reference

All scripts are designed to be called directly by Codex. They handle conda activation internally.

  • scripts/generate_code.py - Generate Python from spec
  • scripts/create_tests.py - Create pytest/unittest suite
  • scripts/run_tests.py - Execute tests and report
  • scripts/debug_code.py - Analyze failures and suggest/patch
  • scripts/optimize_code.py - Profile and improve code quality

See references/script-usage.md for detailed parameter descriptions and examples.

Integration Notes

  • Default conda path is hard-coded for this setup; modify scripts if path changes.
  • All project files are isolated to H:\code\Daily to keep workspace clean.
  • Scripts assume Windows environment (conda .bat activation).
  • Output reports are saved as JSON and plain text for further processing.

When to Use This Skill

Use this skill when the task involves creating new Python code with a complete development pipeline. It's ideal for:

  • Rapid prototyping
  • Educational examples
  • Automated script generation
  • Refactoring tasks with test coverage
  • Optimization of existing code

Do not use for non-Python languages or when conda environment is unavailable.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明，方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用；具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

88.43%
按下载量换算2,309

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API；具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息，不托管安装包，不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果，不能写成本站确认安全。当前只有一个来源，正式发布前建议补源仓库或其他目录站核验。

来源信息

GitHub 仓库

github.com

/aptjason/python-auto-dev

SKILL.md 来源

clawhub.ai

/aptjason/python-auto-dev

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