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Debug Sandbox Execution

OrganizationPopular
HKUDS
debug-sandbox-execution

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

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namedebug-sandbox-execution
Stars
7.7K
Forks
918
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Debug Sandbox Execution AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/debug-sandbox-execution .claude/skills/debug-sandbox-execution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debug Sandbox Execution in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debug Sandbox Execution on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debug Sandbox Execution is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
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:

python
# 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:

python
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

Frequently asked questions

What does the Debug Sandbox Execution AI skill do?

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

Why use Debug Sandbox Execution on TypingMind?

Because you install it once and use it with any model. Debug Sandbox Execution is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Debug Sandbox Execution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/debug-sandbox-execution. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debug Sandbox Execution?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Debug Sandbox Execution?

As many as you like. As long as a model supports skills, you can use Debug Sandbox Execution with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Debug Sandbox Execution AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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