debug-sandbox-execution
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
npx oh-my-skill add hkuds/openspace/debug-sandbox-executioncurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- hkuds/openspace/debug-sandbox-executionnpx oh-my-skill verify hkuds/openspace/debug-sandbox-execution怎么用
商店整理自技能原文 · 版本 2c5cc40 · 表述以原文为准当 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。
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。
技能原文 SKILL.md
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
- Initial attempt: Run full script with
execute_code_sandbox - On failure: Rerun with
run_shelland| head -100to see partial output - Identify breakpoint: Find the last successful operation
- Split script: Create separate scripts for each major section
- Test incrementally: Run each section, verify outputs, proceed to next
- 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_sandboxcall that produces incomplete or no output