ai response rule enforcer

The problem

LLM outputs often ignore negative constraints or omit mandatory formatting requirements during long-form generation. Developers struggle with hallucinated compliance where the agent claims to follow instructions it actually skipped.

What it does

• Extracts implicit and explicit requirements into a structured test suite (constraints, mandatory fields, exclusions).
• Formalizes deterministic pass/fail tests with specific severity levels and evidence requirements.
• Evaluates candidate responses against the contract, treating unverified claims as failures by default.
• Generates a compliance manifest including a test summary table and a list of unverified items.
• Enforces final-decision logic to select or reject candidates based on user-defined tradeoffs.

Why this beats prompting it yourself

Standard prompting relies on the LLM's "best effort" to remember rules. This skill implements a systematic verification layer that treats requirements as a test suite, forcing the model to provide evidence for every claim and explicitly report what it couldn't verify.

Use cases

• Enforcing strict legal or regulatory disclosure requirements in generated documents.
• Validating that technical summaries include all mandatory architectural components.
• Ensuring marketing copy adheres to specific brand exclusions and character limits.
• Auditing complex reasoning tasks where every step must be traceable to a source.