debug-sandbox-execution

A troubleshooting method for Python execution failures when the error output is missing, incomplete, or unclear.

In plain words
What is it for?
It captures partial traces, isolates failing functions, and verifies smaller parts of a script incrementally.
Why use it?
It reveals how far the program ran and narrows the problem by testing imports, functions, and outputs separately.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/hkuds/openspace/debug-sandbox-execution
Any agent
npx skills add HKUDS/OpenSpace --skill debug-sandbox-execution
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenSpace

Made for: Claude Code, Codex.

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 771 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00026 $0.00771
Opus 5 $0.00013 $0.00385
Sonnet 5 $0.00005 $0.00154
Haiku 4.5 $0.00003 $0.00077

Measured 3d ago against content hash 74f9b59ad6be, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug-sandbox-execution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

benchmarks/gdpval/skills/debug-sandbox-execution/SKILL.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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"

Read the full file on GitHub · 99 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 99 lines · 26 tokens per session scan A 74f9b59ad6be

Subscribe to this mod's changes

debug-sandbox-execution is a skill published in the GitHub repository HKUDS/OpenSpace (7,486 stars, last pushed 21d ago), licensed MIT. It adds 26 tokens to every session and 771 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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