llm instruction stress test command center

    by nowrich

    1

    A systematic auditing engine that scores and rewrites agent instructions for safety, efficiency, and robustness.

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLI+17 more

    See it in action

    You say

    Audit these instructions for a customer support bot: 'Help users with their orders. If you don't know the answer, just try your best to be helpful. Output in JSON.'

    Your agent does

    Instruction Audit Report

    Hallucination Risk

    • Score: 2/10
    • Weakness: Encourages guessing ('try your best').
    • Fix: Add 'If the information is not in the context, state that you do not know.'

    Revised Instructions

    { "role": "support_agent", ... }
    

    What you get

    Audit system prompts for prompt injection vulnerabilitiesReduce LLM token overhead by removing redundant instructionsStandardize agent output formats for downstream API consumptionDecrease hallucination rates by enforcing grounding constraints

    About this skill

    The problem

    Agent instructions often fail in production due to ambiguous framing, prompt injection vulnerabilities, or hallucination risks. Most developers rely on trial and error rather than a systematic audit of their system prompts.

    What it does

    • Evaluates instructions across eight dimensions including Injection Safety, Hallucination Risk, and Token Efficiency.
    • Assigns a 0 to 10 score for each dimension to identify specific architectural weaknesses.
    • Generates a diagnostic report with prioritized fixes categorized by severity.
    • Produces a fully rewritten, production-ready version of the instructions with all improvements applied.

    Why this beats prompting it yourself

    Manual testing rarely covers edge cases like delimiters for untrusted data or specific token efficiency trade-offs. This skill applies a structured auditing framework that forces the agent to act as a repair engineer, not just a critic, ensuring every weakness has a concrete code-level fix.

    Use cases

    • Hardening RAG system prompts against prompt injection from external documents.
    • Reducing token costs for high-volume agents without sacrificing reasoning quality.
    • Converting vague natural language directives into strict, parsable output contracts.
    • Optimizing complex multi-step workflows with appropriate chain-of-thought techniques.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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