Skip to content
cccskills
FreePublished on GitHub

sandbox-execution-fallback-238489

Recover Python file-generation tasks when sandbox execution fails by saving a script and running it through the shell, a command interface. Use the workflow for PDFs, document file operations, dependency checks, and output verification.

OriginalShow the original description

Fallback pattern for when execute_code_sandbox fails - write script to file and execute directly with run_shell

See how to install

Good for

  • Retrying PDF generation after sandbox execution fails
  • Continuing DOCX or XLSX file operations after errors
  • Checking Python packages needed to run a script

Files included (2)

  • SKILL.md3.2 KB
  • .skill_id47 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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_sandbox returns 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

  1. Include all dependencies in the script - Make the script self-contained
  2. Add error handling - Include try/except blocks to catch and report errors
  3. Use absolute or clear relative paths - Ensure file paths work in the execution context
  4. Verify prerequisites - Check that required Python packages are available
  5. Clean up temporary files - Remove intermediate scripts after successful execution if needed

Troubleshooting

If run_shell also fails:

  • Check if Python 3 is available: run_shell command: python3 --version
  • Install missing packages: run_shell command: pip3 install package_name
  • Check file permissions and paths
  • Review error output from the shell execution for specific issues

Why This Works

This pattern bypasses sandbox limitations by:

  • Running code in the actual execution environment with full library access
  • Avoiding sandbox timeout or resource constraints
  • Enabling direct file system access for I/O operations
  • Providing better error messages from the native Python interpreter

Self-Assessment

This skill captures a genuinely reusable pattern that:

  • Is generalizable beyond just PDF generation to any execute_code_sandbox failure
  • Provides clear, actionable steps
  • Includes example code and troubleshooting guidance
  • Would benefit future executions facing similar sandbox limitations

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.

  • Match audio parts with different durations
  • Extend drums and melodic parts using suitable methods
  • Check audio specifications before mixing
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.

  • Align instrument tracks to a reference audio file
  • Choose how to extend tracks that are too short
  • Verify audio properties after generation and effects
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.

  • Connect a dashboard to external APIs without exposing API keys
  • Create endpoints for stock quotes and news
  • Standardize API responses and error handling
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.

  • Export a master track with separate audio parts
  • Apply filters and reverb to audio
  • Check duration and format across deliverables
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.

  • Continue drafting after repeated web retrieval failures
  • Preserve task requirements during network errors
  • Mark information that could not be externally verified
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.

  • Retry Python work through the shell after repeated sandbox failures
  • Run longer tasks with an adjusted timeout
  • Execute Python work that needs multiple files or system utilities
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

See all skills by HKUDS

Report a problem with this skill