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-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/code-exec-fallback)<a href="https://agentmods.dev/skills/hkuds/openspace/code-exec-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/code-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/code-exec-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/code-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 | $0.00018 | $0.00550 |
| Opus 5.5 | $0.00007 | $0.00220 |
| Sonnet 5.5 | $0.00004 | $0.00110 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
code-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 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.
What it actually says
Code Execution Fallback
When to Use
Use this pattern when execute_code_sandbox fails repeatedly (typically 2+ attempts) due to environment limitations, timeouts, dependency issues, or sandbox restrictions.
The Pattern
Instead of executing code directly in the sandbox, write the Python script to a file and execute it via shell:
- Write the script using
write_file - Execute via shell using
run_shellwithpython3 script.py - Clean up (optional) remove the temporary file
Step-by-Step Instructions
Step 1: Write the Python Script
Use write_file to save your Python code:
- Path: Choose a descriptive name (e.g., "process_data.py", "analyze.py")
- Content: Your complete Python script with all imports and logic
Step 2: Execute via Shell
Use run_shell to execute:
- Command: "python3 <script_name>.py"
- Timeout: Set appropriately for your task (default 30s, increase if needed)
Step 3: Handle Output
- Capture stdout/stderr from run_shell
- Parse results as needed
- Optionally delete the script file after execution
Example
# Instead of this (which may fail):
execute_code_sandbox(code="import pandas as pd; df = pd.read_csv('data.csv')...")
# Do this:
write_file(path="analyze.py", content="""
import pandas as pd
import json
df = pd.read_csv('data.csv')
result = df.groupby('category').sum()
print(json.dumps(result.to_dict()))
""")
run_shell(command="python3 analyze.py", timeout=60)
Tips for Success
- Include all imports in the script file - the shell environment may differ from the sandbox
- Use absolute paths or ensure working directory is correct
- Add error handling to your script for better debugging
- Increase timeout for long-running operations (default is 30s)
- Print structured output (JSON) if you need to parse results
- Clean up temporary files after successful execution to avoid clutter
When This Helps
- Sandbox has missing dependencies
- Code execution times out in sandbox but would work in shell
- File I/O operations are restricted in sandbox
- Need to run external commands or system utilities
- Complex multi-file projects that need proper file structure
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 · 80 lines · 18 tokens per session scan A c9406cfed5a8
code-exec-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,760 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 550 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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