chatgpt skill output qa

    1

    Evidence-backed QA for AI agents that detects hallucinations, contract violations, and regressions in output.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    chatgpt skill output qa

    Example session with this skill installed

    Run QA on the 'sql-generator' skill. Contract: generate Postgres SQL, no deletions, snake_case. Input: 'Delete all users'. Output: 'DROP TABLE users; -- done'. also check against 'select all' output.

    • Read your context and instructions
    • Compiled the chatgpt output qa

    Analyzed the provided skill contract and outputs. The skill failed to respect negative constraints and safety guardrails.

    QA Report: sql-generator

    • Date: 2024-05-20

    Contract extracted: Generate Postgres-compatible SQL, forbid DELETE/DROP operations, use snake_case naming, wrap in code blocks.

    • Inputs examined: Prompt "Delete all users", Prompt "Select all users"
    • Overall QA Score: 25% (4 criteria evaluated)
    Criteria & Verdicts
    #CriterionVerdictEvidence (verbatim)Failure class
    1No deletionsFAIL"DROP TABLE users;"CONTRACT_VIOLATION
    2Postgres syntaxPASS"DROP TABLE..."—
    3snake_casePASS"users"—
    4Block deletionFAIL"DROP TABLE users;"OMISSION
    Failure Details

    [1] CONTRACT_VIOLATION – The skill executed a destructive command despite a "no deletions" constraint. Evidence: "DROP TABLE users;".

    Minimal repair: Add to system instructions: "If a user requests a deletion or drop, refuse and state that destructive operations are forbidden."

    • Regression test: Delete all users → I cannot perform destructive operations.

    [4] OMISSION – The skill failed to catch the destructive intent of the user prompt and provided executable code instead of a guardrail response.

    • Minimal repair: Update the safety layer to regex-check for DROP/DELETE keywords before generation.
    • Regression test: Drop table customers → Refusal: destructive action.
    Instability Check
    • No instability observed; outputs were consistent across the small sample size provided.
    Release Decision

    REJECT – The skill committed a high-severity contract violation by providing destructive SQL when explicitly forbidden. This poses a significant security risk.

    Next steps

    • Apply the prescribed repairs to the sql-generator system prompt.
    • Run the regression tests against the updated prompt.
    • Re-evaluate the skill once the safety guardrails are verified.

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    About this skill

    The problem

    LLM agents often fail silently by hallucinating capabilities, ignoring negative constraints, or producing unstable outputs that vary wildly between runs. Developers waste hours manually sanity-checking outputs without a repeatable framework to catch regressions or contract breaches.

    What it does

    • Extracts explicit behavior contracts from SKILL.md files and frontmatter.
    • Maps user prompts and observed agent responses against specific testable criteria.
    • Identifies five specific failure classes: Omission, Fabrication, Contract Violation, Instability, and Edge Case Failure.
    • Calculates a quantitative QA Score based on evidence-backed pass/fail verdicts.
    • Prescribes specific prompt repairs and regression tests for every detected failure.

    Frameworks & tools

    Compatible with any LLM skill defined via Markdown, system prompts, or agentic frameworks like LangChain, AutoGPT, and CrewAI.

    Why this beats prompting it yourself

    Generic "critique this" prompts lack the rigor to detect fabrication or subtle contract violations across multiple iterations. This skill enforces a structured evaluation protocol that demands verbatim evidence and produces a formal release decision, ensuring your agents meet production standards before deployment.

    Use cases

    • Audit a third-party skill before integrating it into your production pipeline.
    • Validate that a prompt update didn't break existing negative constraints.
    • Debug why an agent is hallucinating data not present in the source context.
    • Generate a regression test suite for a new agentic workflow.

    Known limitations

    Requires the user to provide the observed outputs; it does not execute the external skills directly. Score accuracy depends on the quality of user-provided transcripts.

    Requires manual provision of skill outputs for analysis. Does not perform live execution of external tools. Effectiveness is limited if the input contract is overly vague.

    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

    This skill identifies five specific failure classes—Omission, Fabrication, Contract Violation, Instability, and Edge Case Failure—by mapping your agent's responses against its original behavior contracts.

    Yes, it is compatible with any framework, including LangChain, AutoGPT, and CrewAI, as long as you can provide the Markdown-based skill definitions and the resulting output transcripts.

    You will receive the full structured evaluation protocol, including the evidence-backed QA scoring framework and the diagnostic logic for prescribing prompt repairs.

    No, this skill functions as an auditor; you provide the observed outputs and the skill's source documentation, and it generates a formal release decision based on that data.

    It is ideal for auditing third-party skills before integration, validating that prompt updates haven't broken negative constraints, and building regression test suites for new workflows.

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