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-execution-fallback-238489git 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-execution-fallback-238489)<a href="https://agentmods.dev/skills/hkuds/openspace/sandbox-execution-fallback-238489"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/sandbox-execution-fallback-238489/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-execution-fallback-238489"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/sandbox-execution-fallback-238489.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 | $0.00029 | $0.00694 |
| Opus 5 | $0.00015 | $0.00347 |
| Sonnet 5 | $0.00006 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
sandbox-execution-fallback-238489 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sandbox Execution Fallback
This skill provides a reliable fallback pattern when execute_code_sandbox fails for code execution tasks (especially PDF generation, complex file operations, or library-dependent tasks).
When to Use
Use this fallback pattern when:
execute_code_sandboxreturns errors or fails to produce expected output- The task requires specific Python libraries not available in the sandbox
- File I/O operations (especially PDF, docx, xlsx) fail in the sandbox environment
- You need more control over the execution environment
Step-by-Step Instructions
Step 1: Write the Python Script to a File
Instead of executing code directly in the sandbox, use write_file to create a standalone Python script:
Use write_file to create a script (e.g., script.py) with your complete Python code.
Step 2: Execute the Script Directly
Run the script using run_shell with Python:
Use run_shell with command: python3 script.py
Step 3: Verify Output
Check that the script executed successfully and produced the expected files or output.
Example Pattern
Instead of this (sandbox execution):
execute_code_sandbox with code using libraries like reportlab, fpdf, etc.
Do this (fallback pattern):
# Step 1: Create the script
write_file:
path: generate_pdf.py
content: |
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
def create_pdf(filename):
c = canvas.Canvas(filename, pagesize=letter)
c.drawString(100, 750, "Hello World")
c.save()
create_pdf("output.pdf")
# Step 2: Execute directly
run_shell:
command: python3 generate_pdf.py
Best Practices
- Include all dependencies in the script - Make the script self-contained
- Add error handling - Include try/except blocks to catch and report errors
- Use absolute or clear relative paths - Ensure file paths work in the execution context
- Verify prerequisites - Check that required Python packages are available
- Clean up temporary files - Remove intermediate scripts after successful execution if needed
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 · 98 lines · 29 tokens per session scan A 9a309f8166ba
sandbox-execution-fallback-238489 is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 27d ago), licensed MIT. It adds 29 tokens to every session and 694 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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