🤖 AI Agents & Workflows

AI-Powered Personal Compliment & Coaching Engine

📁 AI Agents & Workflows 👤 Contributed by @mmanisaligil 🗓️ Updated
The prompt
Build a web app called "Mirror" — an AI-powered personal coaching tool that gives users emotionally intelligent, personalized feedback. Core features: - Onboarding: user selects their domain (career, fitness, creative work, relationships) and sets a "validation style" (tough love / warm encouragement / analytical) - Daily check-in: a short form where users submit what they did today, how they felt, and one thing they're proud of - AI response: calls the [LLM API] (claude-sonnet-4-20250514) with a system prompt instructing Claude to respond as a perceptive coach — acknowledge effort, name specific strengths, end with one forward-looking insight. Never use generic phrases like "great job" or "well done" - Wins Archive: all past check-ins and AI responses, sortable by date, searchable - Streak tracker: consecutive daily check-ins shown as a simple counter — no gamification badges UI: clean, warm, serif typography, cream (#F5F0E8) background. Should feel like a private journal, not an app. No notifications except a gentle daily reminder at a user-set time. Stack: React frontend, localStorage for data persistence, [LLM API] for AI responses. Single-page app, no backend required.

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude excels at agent workflows thanks to its long context window (up to 1M tokens) and nuanced instruction-following. ChatGPT has native Actions (tool-calling) built in. Gemini integrates best with Google Workspace data. For autonomous workflows, Claude Sonnet 4.6 is the current sweet-spot for quality and cost.

How to customize this prompt

Adjust the agent's role and constraints to your environment. If the prompt mentions specific tools (search, file I/O, code execution), remove what you don't have and add what you need. Add guardrails: "Always ask for confirmation before writing files." Define success criteria explicitly.

Common use cases

  • Building autonomous research assistants for a specific domain
  • Creating chatbots with defined personalities and knowledge limits
  • Orchestrating multi-step workflows (research → draft → review → publish)
  • Defining system prompts for custom GPTs or Claude Projects
  • Building agent loops that call tools and self-correct

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).

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