Ai Output Validation
ListedHelps with AI, validation as part of agent workflows workflows.
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ai-output-validation. youngger9765/career_ios_backend is deleted or private, so it cannot be installed from.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-25. We don't show a command we know will fail.
What we know about this skill
Publisher
youngger9765
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
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About this skill
What it does
This skill allows an AI agent to validate the output of other skills or tools. It can check for accuracy, completeness, and adherence to specific formats or constraints. The validation process includes comparing generated content against expected outcomes or rulesets, ensuring higher quality results.
When to use it
- Code Generation: Validate that generated code compiles and runs without errors.
- Content Creation: Check if a draft blog post adheres to brand guidelines and contains all necessary sections.
- Data Extraction: Verify that extracted data from documents matches the expected schema and data types.
- Report Generation: Ensure a financial report includes all required metrics and is formatted correctly.
Key capabilities
- Accuracy checking against predefined rulesets
- Completeness verification based on expected content
- Format validation (e.g., JSON, CSV)
- Comparison of generated output with known correct answers
Example prompts
- "Validate the following Python code snippet for syntax errors and potential runtime issues: [code]"
- "Check if this email draft includes all required disclaimers and a clear call to action: [email text]"
- "Verify that this JSON data conforms to the schema located at [schema URL]."
Tips & gotchas
The skill's effectiveness depends on having well-defined validation rules or expected outputs. Providing clear instructions and context will improve accuracy.