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.
npx agentmods add skills/hkuds/openspace/debug-sandbox-executionnpx skills add HKUDS/OpenSpace --skill debug-sandbox-executiongit clone --depth 1 https://github.com/HKUDS/OpenSpaceWhat 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.
| Model | Per session | Once 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 |
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.
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"
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.
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.
- 3d ago First seen · 99 lines · 26 tokens per session scan A 74f9b59ad6be
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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…