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code-exec-fallback

@hkuds · 收录于 5 天前 · 上游提交 1 周前

Fallback pattern for executing Python code when execute_code_sandbox fails

适合你,如果需要在沙箱执行失败时用备用方式运行Python代码

/ 通过 npx 安装 校验哈希
npx oh-my-skill add hkuds/openspace/code-exec-fallback
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- hkuds/openspace/code-exec-fallback
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify hkuds/openspace/code-exec-fallback
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 2c5cc40 · 表述以原文为准
它做什么

当沙箱执行Python代码失败时,Claude会将代码写入文件,然后通过shell命令运行该文件,并返回运行结果。

什么时候触发

当使用execute_code_sandbox执行代码连续失败(通常2次以上),原因是环境限制、超时、依赖问题或沙箱限制时触发。

装好后可以这样说
触发fallback模式。
使用write_file和run_shell。
技能原文 SKILL.md作者撰写 · MIT · 2c5cc40

Code Execution Fallback

When to Use

Use this pattern when execute_code_sandbox fails repeatedly (typically 2+ attempts) due to environment limitations, timeouts, dependency issues, or sandbox restrictions.

The Pattern

Instead of executing code directly in the sandbox, write the Python script to a file and execute it via shell:

  1. Write the script using write_file
  2. Execute via shell using run_shell with python3 script.py
  3. Clean up (optional) remove the temporary file
Step-by-Step Instructions
Step 1: Write the Python Script
Use write_file to save your Python code:
- Path: Choose a descriptive name (e.g., "process_data.py", "analyze.py")
- Content: Your complete Python script with all imports and logic
Step 2: Execute via Shell
Use run_shell to execute:
- Command: "python3 <script_name>.py"
- Timeout: Set appropriately for your task (default 30s, increase if needed)
Step 3: Handle Output
- Capture stdout/stderr from run_shell
- Parse results as needed
- Optionally delete the script file after execution
Example
# Instead of this (which may fail):
execute_code_sandbox(code="import pandas as pd; df = pd.read_csv('data.csv')...")

# Do this:
write_file(path="analyze.py", content="""
import pandas as pd
import json

df = pd.read_csv('data.csv')
result = df.groupby('category').sum()
print(json.dumps(result.to_dict()))
""")

run_shell(command="python3 analyze.py", timeout=60)
Tips for Success
  1. Include all imports in the script file - the shell environment may differ from the sandbox
  2. Use absolute paths or ensure working directory is correct
  3. Add error handling to your script for better debugging
  4. Increase timeout for long-running operations (default is 30s)
  5. Print structured output (JSON) if you need to parse results
  6. Clean up temporary files after successful execution to avoid clutter
When This Helps
  • Sandbox has missing dependencies
  • Code execution times out in sandbox but would work in shell
  • File I/O operations are restricted in sandbox
  • Need to run external commands or system utilities
  • Complex multi-file projects that need proper file structure
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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