Fallback workflow for reliable code execution when sandbox fails repeatedly
67
80%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Critical
Do not install without reviewing
Fix and improve this skill with Tessl
tessl review fix ./benchmarks/gdpval/skills/code-exec-fallback-266cba/SKILL.mdApply this pattern when you encounter repeated failures with execute_code_sandbox:
Track execution failures. After 2 consecutive failures with execute_code_sandbox, switch to the fallback approach.
Use write_file to save your Python script:
write_file(
path="/workspace/script_name.py",
content="# Your Python code here\nimport sys\n..."
)Use run_shell to run the script:
run_shell(
command="python /workspace/script_name.py",
timeout=300
)Parse stdout/stderr from run_shell output to verify success or diagnose issues.
# Instead of this (which may fail):
result = execute_code_sandbox(code="import pandas as pd\n...")
# Use this fallback pattern:
script_content = """
import pandas as pd
import sys
try:
# Your logic here
df = pd.DataFrame({'col': [1, 2, 3]})
print(df.to_csv())
sys.exit(0)
except Exception as e:
print(f"ERROR: {e}", file=sys.stderr)
sys.exit(1)
"""
# Write the script
write_file(path="/workspace/my_script.py", content=script_content)
# Execute via shell
result = run_shell(command="python /workspace/my_script.py", timeout=300)sys.exit() codesrun_shell default is 30s, increase for heavy operationswrite_file is more reliable for file I/O operationsrun_shell gives you direct control over execution environmentc5a9c4b
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.