730 reviewed prompts.
Helps you write a strong, reusable prompt for an AI assistant through short rounds of drafting, optional additions, and clarifying questions. Use when a prompt keeps giving vague or inconsistent results.
Produces a balanced buy-or-skip recommendation for a product you are considering, with pros, cons, alternatives and a clear verdict. Use it before a purchase of any size.
Reviews how an application is organized and proposes a layered structure with small, testable steps. Use it when code has grown tangled and hard to change.
Suggests a short list of realistic activities and hobbies that fit your space, weather, budget and time. Use it when you are bored, planning a weekend, or want a new way to spend free hours.
Reformats rough or chat-style text into clean plain text that pastes into a document or email without stray symbols. Use it when copy from a chat or notes keeps breaking your formatting.
Plans and interprets an analysis of product usage data to find why users stay or leave, ending in prioritized, testable recommendations. Use when engagement or retention is flat or falling.
Recommends a practical build, test and deployment pipeline for your app, sized to your team and risk level, with a phased rollout. Use when setting up deployment for the first time or fixing slow, manual releases.
Answers small coding questions directly, and for bigger builds sets out the choices, design and folder structure first, then writes complete code in small, runnable steps. Use it to build a script, tool or small app with an assistant.
Prepares a negotiation plan for a contract or deal: positions, walk-away point, concessions, key terms, and wording for tough moments. Use before a vendor, customer, or partnership negotiation.
Explains a programming concept step by step with analogies, a small worked example, common mistakes and practice tasks. Use when mentoring a junior developer or learning a concept yourself.
Extracts what the feature is, what should happen and what actually happens from pasted logs, chat threads or rough notes, and flags missing details. Use before filing a ticket or handing a bug to a developer.
Designs how a machine learning model is trained, validated, deployed, monitored, and retired in production, including drift detection, rollback, and governance. Use when moving a model from notebook to production or fixing a fragile pipeline.
