Completion Check

The completion-check skill enables AI agents to verify that generated responses fully satisfy user requirements before finalizing output. It acts as a validation layer, ensuring accuracy and completeness in complex tasks by cross-referencing results against initial constraints.

When to use it

• Validate code generation to ensure all edge cases and error handling are addressed.
• Confirm that creative writing pieces meet specific tone, length, or structural guidelines.
• Verify data extraction tasks to guarantee no information was omitted from the source text.
• Ensure logical consistency in multi-step reasoning problems before presenting the final solution.

Key capabilities

• Compares AI-generated output against original prompts and constraints.
• Identifies missing elements or incomplete answers within a response.
• Flags deviations from specified formats, styles, or technical requirements.

Example prompts

• "Generate a Python script to scrape data from this URL, then use completion-check to verify it includes error handling for network timeouts."
• "Draft a 500-word blog post about quantum computing in a humorous tone; run completion-check to ensure the word count and tone match exactly."
• "Extract all phone numbers from this customer support transcript and validate the extraction with completion-check against the original text."

Tips & gotchas

This skill is most effective when paired with detailed, unambiguous instructions in your initial prompt. It does not rewrite content; it only assesses whether the existing output meets your criteria, so ensure your requirements are explicitly defined beforehand.