OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.
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 skills add HKUDS/OpenSpace --skill code-execution-fallback-e81068git clone --depth 1 https://github.com/HKUDS/OpenSpaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/openspace/code-execution-fallback-e81068)<a href="https://agentmods.dev/skills/hkuds/openspace/code-execution-fallback-e81068"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/code-execution-fallback-e81068/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/openspace/code-execution-fallback-e81068"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/code-execution-fallback-e81068.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00973 |
| Opus 5.5 | $0.00009 | $0.00389 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (7,760 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 973 once invoked, about $0.0001 per session on Opus 5.5. 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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