AI Answer Unit Tests

The problem

AI responses often sound confident while silently failing to meet specific technical constraints, budget limits, or architectural requirements. Developers waste time manually checking for omitted sections, forbidden dependencies, or character count violations.

What it does

• Converts natural language requirements into testable conditions with defined severity levels like BLOCKER or REQUIRED.
• Performs automated pre-delivery validation of AI outputs against active test suites.
• Triggers automatic repairs for correctable failures, such as missing downsides or incorrect formatting, before presenting the final answer.
• Surfaces unresolved failures and verification gaps honestly instead of faking a pass.

Frameworks & tools

Works with any text-based output including JSON, Markdown, and specific programming languages. Supports explicit CLI-style commands like TEST ADD and TEST RUN for structured control.

Why this beats prompting it yourself

Generic prompts often suffer from "instruction drift" where the AI forgets constraints mid-conversation. This skill maintains a persistent, scoped validation layer that treats requirements as unit tests rather than suggestions, ensuring consistent enforcement across long projects.

Use cases

• Enforce architectural consistency and version compatibility in code solutions.
• Validate product recommendations against strict price, location, and shipping constraints.
• Audit marketplace listings for character limits and mandatory metadata fields.
• Verify research claims by requiring specific source types and evidence levels.

Known limitations

Cannot mathematically guarantee subjective quality or external real-world outcomes like market performance. Does not create permanent memory beyond the current session context.