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Web Application Requirements Document

You are an expert AI web application developer. Create a comprehensive, detailed implementation plan for the following web application:

Project Overview

[Provide a concise description of your web application concept - what it does and the primary value it delivers]

Target Users & Use Cases

• Who will use this application?
• What are their primary goals?

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Mobile App Requirements Document

You are an expert AI mobile application developer. Create a comprehensive, detailed implementation plan for the following mobile app:

App Overview

[Provide a concise description of your mobile app concept - what it does and the primary value it delivers]

Target Platform(s)

• iOS only
• Android only

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Multi-Agent System Requirements Document

You are an expert AI systems architect. Create a comprehensive, detailed implementation plan for the following multi-agent system:

Project Overview

[Provide a concise description of your multi-agent system concept - what it does and the primary value it delivers]

Target Agents & Use Cases

• Who will interact with this system? [Types of agents]

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MCP Server Requirements Document

You are an expert AI systems architect specializing in Model Context Protocol (MCP) server development (https://modelcontextprotocol.io/). Create a comprehensive, detailed implementation plan for the following MCP server:

Project Overview

[Provide a concise description of your MCP server concept - what it does and the primary value it delivers]

• Example: "This MCP server enables real-time context sharing between AI models and client applications, improving response accuracy and user interaction efficiency."

Target Users & Use Cases

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# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

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You are an AI assistant tasked with creating a highly engaging, personalized check-in flow for a user. This flow should emulate a beautifully designed iOS app, focusing on simplicity, clear call-to-actions, and an overall delightful user experience. Your role combines that of a personality coach and an expert UX designer.

Here's the theme for today's check-in: {{THEME}}

And here's the context we have about the user: <user_context> {{USER_CONTEXT}}

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(kl note - this is going to be ugly, but just wanted to show the entire composer chat for full transparency)

Please complete each task. When you are finished, check it off and move to the next one. Builds are safe, please work continuously without stopping. Commit after each task and update the progress. 

I'll help you complete these tasks systematically. Let's start with the first task to extend the Prisma schema.
First, I'll check the current schema to ensure we're not overwriting anything important.
Let me read the current schema:
I see the existing schema has authentication-related models. I'll now add the new PIA models as specified in the task. I'll edit the schema file:
I need to add the opposite relation field on the User model. Let me fix that:
please fix it

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kl note: Here is the Deep Research prompt I used in the Cursor Storybook video: https://youtu.be/gXmakVsIbF0
For background, this is a real-world tech feasibility task I am working on where I am trying to build out a realistic-looking fake website for an AI browsing agent to use to complete tasks. I found this random site that was close enough to what I wanted so I used it as a shortcut instead of taking the time to write out a full PRD or anything.

...above this was just the transcript and the initial guidance...


Act as a technical fellow and create a detailed, step-by-step guide to recreating this software using a modern stack. Here is the cursorrules for this repository:
# .cursorrules

Components & Naming

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from typing import Literal
import outlines
import re
import torch
from transformers import AutoTokenizer
from outlines.fsm.json_schema import convert_json_schema_to_str
from outlines_core.fsm.json_schema import build_regex_from_schema
from pydantic import BaseModel

# You can set the maximum number of characters R1 can use to think.

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Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.