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debug-sandbox-execution

@hkuds · 收录于 5 天前 · 上游提交 1 周前

Debug Python code execution failures by capturing partial traces, isolating failing functions, and incrementally verifying outputs

适合你,如果调试 Python 程序时经常找不到错误根源

/ 通过 npx 安装 校验哈希
npx oh-my-skill add hkuds/openspace/debug-sandbox-execution
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- hkuds/openspace/debug-sandbox-execution
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify hkuds/openspace/debug-sandbox-execution
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 sandbox 执行失败时,Claude 会分三步调试:先捕获部分输出定位失败位置,再单独测试每个函数,最后逐个生成并验证文件。

什么时候触发

当你使用 execute_code_sandbox 运行复杂脚本(如音频/视频生成)时,若输出不完整或报错,Claude 会触发此调试流程。

装好后可以这样说
Claude 会运行调试流程。
Claude 会隔离每个函数执行。
Claude 会添加断言检查。
技能原文 SKILL.md作者撰写 · MIT · 2c5cc40

Debug Sandbox Execution Failures

When execute_code_sandbox fails with unknown errors or incomplete output, use this debugging pattern to identify the root cause and recover incrementally.

Problem

The execute_code_sandbox tool may fail silently, truncate output, or produce opaque errors. Complex scripts with multiple file outputs are especially prone to partial failures.

Solution

Use a three-phase debugging approach:

Phase 1: Capture Partial Execution Traces

When a sandbox execution fails, rerun the code using run_shell with output piping to capture whatever output is produced before the failure:

python your_script.py 2>&1 | head -100

This reveals:

  • Which functions/steps executed successfully
  • Where the failure occurred
  • Any error messages that were suppressed
Phase 2: Isolate Failing Functions

Break the script into smaller, testable units. Execute each function or code block independently:

# Test individual components
if __name__ == "__main__":
    # Step 1: Test imports
    import numpy as np
    print("Imports OK")
    
    # Step 2: Test function A in isolation
    result_a = function_a()
    print(f"Function A: {result_a}")
    
    # Step 3: Test function B
    result_b = function_b(result_a)
    print(f"Function B: {result_b}")

Run each section with execute_code_sandbox separately to identify which component fails.

Phase 3: Incremental Output Generation

Generate output files one at a time, verifying each before proceeding:

import numpy as np
import soundfile as sf

# Generate and save file 1
audio1 = np.random.randn(48000 * 10).astype(np.float32)
sf.write('output_01.wav', audio1, 48000, subtype='FLOAT')

# Verify file 1 exists and has expected properties
import os
assert os.path.exists('output_01.wav'), "File 1 not created"

# Generate and save file 2
audio2 = np.random.randn(48000 * 10).astype(np.float32)
sf.write('output_02.wav', audio2, 48000, subtype='FLOAT')

# Verify file 2
assert os.path.exists('output_02.wav'), "File 2 not created"
Example Workflow
  1. Initial attempt: Run full script with execute_code_sandbox
  2. On failure: Rerun with run_shell and | head -100 to see partial output
  3. Identify breakpoint: Find the last successful operation
  4. Split script: Create separate scripts for each major section
  5. Test incrementally: Run each section, verify outputs, proceed to next
  6. Combine successful sections: Once all pieces work, combine into final script
Best Practices
  • Always verify file creation immediately after writing: assert os.path.exists(path)
  • Check file properties (size, duration, format) before assuming success
  • Use print statements liberally to mark progress through the script
  • Save intermediate outputs so failures don't require restarting from scratch
  • Test audio/video generation with short samples first (1-2 seconds) before full-length content
When to Use
  • Complex scripts with multiple file outputs
  • Audio/video generation pipelines
  • Scripts with external library dependencies
  • Any execute_code_sandbox call that produces incomplete or no output
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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