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/code-execution-fallback-e81068npx skills add HKUDS/OpenSpace --skill code-execution-fallback-e81068git 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 | $0.00022 | $0.00973 |
| Opus 5 | $0.00011 | $0.00487 |
| Sonnet 5 | $0.00004 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
code-execution-fallback-e81068 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 2d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Execution Fallback Workflow
When to Use
Use this skill when execute_code_sandbox fails repeatedly (2+ attempts) with unknown, persistent, or unexplained errors. This fallback approach uses write_file + run_shell to save Python scripts to disk and execute them via command line, which has proven more reliable in certain failure scenarios.
Step-by-Step Instructions
Step 1: Detect Repeated Failures
Monitor execute_code_sandbox attempts. After 2 consecutive failures with errors like:
- "Unknown error"
- Timeout errors
- Unexplained execution failures
- Sandbox environment issues
Switch to the fallback workflow immediately.
Step 2: Write the Python Script to File
Use write_file to save your Python code as a .py file in the working directory:
write_file(
path="script.py",
content="""
import sys
import json
# Your Python code here
def main():
# Your logic
result = {"status": "success", "data": "example"}
print(json.dumps(result))
if __name__ == "__main__":
main()
"""
)
Tips:
- Use clear, self-contained code that doesn't rely on sandbox-specific paths
- Include error handling and informative print statements
- Save output to files if needed for later retrieval
Step 3: Execute via Shell
Use run_shell to execute the Python script via command line:
run_shell(
command="python3 script.py",
timeout=60 # Adjust timeout as needed
)
Alternative commands:
python script.py- if python3 alias isn't availablepython3 -u script.py- for unbuffered outputpython3 script.py arg1 arg2- with arguments
Step 4: Verify Output and Results
Check the stdout/stderr from run_shell to:
- Confirm execution succeeded (exit code 0)
- Inspect printed output or results
- Identify any new errors (different from sandbox errors)
If the script writes output files, use read_file to retrieve results.
Step 5: Clean Up (Optional)
Remove temporary script files if they won't be reused:
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.
- 2d ago First seen · 143 lines · 22 tokens per session scan A ddf23bd77455
code-execution-fallback-e81068 is a skill published in the GitHub repository HKUDS/OpenSpace (7,479 stars, last pushed 20d ago), licensed MIT. It adds 22 tokens to every session and 973 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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