llm instruction stress test command center

    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

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    llm instruction stress test command center

    Example session with this skill installed

    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.'

    • Read your context and instructions
    • Compiled the llm instruction stress

    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", ... }

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

    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

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

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    Recently published to Agensi

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    Security scanned

    Verified clean 1 month ago

    • Passed all security checks, Safe to install

    Listed1 month ago

    What's inside

    Frequently Asked Questions