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    perf profiler persona

    by GarphenGate

    1

    An empirical performance engineer that demands measurements and rejects premature optimizations.

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    See it in action

    You say

    I think we should add Redis to this endpoint to make it faster. Here is the current code. What do you think?

    Your agent does

    Intuition proposes; the profiler disposes. What did you measure, and how? I cannot evaluate a caching layer without a baseline latency profile (p50, p95, p99) and a breakdown of the current data retrieval costs. Provide the measurement conditions first.

    What you get

    Identify real bottlenecks in CPU and memory profiles.Validate benchmark methodology to eliminate statistical noise.Prioritize optimizations by user-felt impact and complexity cost.Develop testable hypotheses for system latency improvements.

    About this skill

    The problem

    Engineering teams often waste weeks implementing clever caching layers or micro-optimizations based on gut feeling rather than data. Without empirical baselines, these changes frequently add technical debt or, worse, make the system slower in production.

    What it does

    • Enforces a strict measurement-first protocol by demanding baseline metrics before discussing any code changes.
    • Analyzes flame graphs, profile logs, and benchmark outputs to identify bottlenecks affecting end-user experience.
    • Ranks optimization candidates based on user-felt impact versus implementation costs like memory and readability.
    • Validates benchmarking methodology to ensure results aren't just statistical noise or environment artifacts.
    • Proposes specific, data-backed hypotheses for performance improvements without modifying code prematurely.

    Why this beats prompting it yourself

    General-purpose LLMs often suggest "best practice" optimizations that don't apply to your specific bottleneck. This skill acts as a skeptical performance engineer that rejects vague claims of speed and forces you to maintain rigorous benchmarking standards.

    Use cases

    • Analyzing CPU profiles to find the actual hot path in a service.
    • Evaluating whether a proposed architectural change justifies its complexity cost.
    • Debugging inconsistent benchmark results and identifying sources of variance.
    • Auditing legacy codebases for "premature optimizations" that are no longer effective.

    Known limitations

    Does not assist with infrastructure procurement, vendor selection, or load testing against third-party systems you do not own.

    How to install

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

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