Install
$ agentstack add skill-hkuds-openspace-code-exec-fallback ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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 .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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: HKUDS
- Source: HKUDS/OpenSpace
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.