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 sandbox-exec-fallbackgit 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/sandbox-exec-fallback)<a href="https://agentmods.dev/skills/hkuds/openspace/sandbox-exec-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/sandbox-exec-fallback/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/sandbox-exec-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/sandbox-exec-fallback.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 | $0.00026 | $0.00564 |
| Opus 5 | $0.00013 | $0.00282 |
| Sonnet 5 | $0.00005 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
sandbox-exec-fallback 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 5d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sandbox Execution Fallback
When to Use
Use this skill when execute_code_sandbox fails during Python code execution, particularly for tasks involving:
- Spreadsheet generation (xlsx, csv)
- Data processing and transformations
- File output operations
- Complex computations requiring libraries
Recovery Pattern
When execute_code_sandbox returns an error or fails to produce expected output:
Step 1: Write Python Code to File
Use write_file to save your Python script to a file:
# Use write_file tool with:
path: "script.py"
content: <your complete Python code as a string>
Step 2: Execute via Shell
Use run_shell to execute the script:
# Use run_shell tool with:
command: "python3 script.py"
timeout: <appropriate timeout, e.g., 300 for long-running tasks>
Step 3: Verify Output
Check the shell output for:
- Success messages or printed results
- Error messages (if any, diagnose and fix the script)
- File creation confirmations
Step 4: Access Generated Files
If the script creates output files (e.g., spreadsheets), they will be in the current working directory. Use list_dir to confirm, then read_file to access if needed.
Why This Works
- Same Python Environment:
python3in the shell uses the same environment as the sandbox - More Robust: Shell execution handles long-running or memory-intensive tasks better
- Debuggable: Errors are captured in stdout/stderr for easier diagnosis
- Identical Results: Produces the same output as sandbox execution when successful
Example: Spreadsheet Generation
Original code that failed in execute_code_sandbox:
import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
df.to_excel('output.xlsx', index=False)
print("File created successfully")
Recovery Execution:
write_filewith path="generate_spreadsheet.py" and the code above as contentrun_shellwith command="python3 generate_spreadsheet.py"- Verify output.xlsx was created with
list_dir
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.
- 5d ago First seen · 82 lines · 26 tokens per session scan A 8ef768e5e454
sandbox-exec-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 27d ago), licensed MIT. It adds 26 tokens to every session and 564 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 2026-09-03.
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