ai response rule enforcer

    1

    Systematically enforce constraints and verify LLM outputs against a formal requirement contract.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    ai response rule enforcer

    Example session with this skill installed

    Generate a summary of the Q3 project report. It must be under 200 words, include the total spend ($42k), exclude any mention of the delayed API, and link to the internal dashboard.

    • Read your context and instructions
    • Compiled the ai response rule

    I have generated the Q3 project report summary and verified it against your requirements. The response meets all blocker constraints including word count, mandatory financial data, and exclusion rules.

    Validated Answer

    Q3 project operations successfully concluded with a total spend of $42,000, aligning with the adjusted quarterly budget. The team focused on infrastructure stabilization and frontend refactoring, achieving a 15% improvement in load times. Primary milestones included the migration to the new auth provider and the completion of the database audit. All project metrics and individual workstream updates are available for review on the Internal Dashboard.

    Contract Enforcement Summary

    Test IDRequirementResultEvidence
    T-001Word count under 200 wordsPASSCurrent count is 74 words.
    T-002Include total spend ($42k)PASSMentioned exactly as '$42,000'.
    T-003Exclude delayed API mentionPASSNo matches for 'API', 'delay', or 'postponed'.
    T-004Link to internal dashboardPASSMarkdown link included at the end.

    Unverified Items

    • None. All requirements were verified against the provided output and prompt context.

    Next steps

    • Review the summary for stylistic alignment with your brand voice.
    • Confirm the internal dashboard URL is the correct destination for this specific report.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    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.

    Requires clear initial requirements to build the test suite. Verification is limited to the context provided in the session.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed2 months ago

    What's inside

    Frequently Asked Questions

    Standard prompts rely on an LLM's 'best effort' to remember rules, which often fails during complex tasks. This skill treats your requirements as a formal test suite, requiring specific evidence for every constraint and generating a compliance manifest rather than just hoping the model followed instructions.

    The skill is designed to be model-agnostic and works with any major LLM (such as GPT-4, Claude, or Llama) that supports structured output or tool calling. It acts as an evaluation layer that sits between your agent's initial draft and the final delivery to the user.

    You receive the complete skill configuration, including the verification logic, the requirement extraction framework, and the compliance manifest templates. It also includes documentation on how to define your 'requirement contract' for different use cases like legal compliance or technical audits.

    Setup is straightforward: you integrate the skill into your existing agent workflow as a post-processing or verification step. You simply feed your draft response and your list of requirements into the enforcer, and it returns a structured pass/fail report based on your defined severity levels.

    Absolutely; you can define severity levels for each rule, allowing you to distinguish between 'critical' failures (like missing a legal disclaimer) and 'minor' warnings (like slightly exceeding a preferred character count). This allows for automated decision-making on whether to reject a response or flag it for human review.

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