shizonic shizonic

At the end of this message, I will ask you to do something. Please follow the "Explore, Plan, Code, Test" workflow when you start.

Explore

First, use parallel subagents to find and read all files that may be useful for implementing the ticket, either as examples or as edit targets. The subagents should return relevant file paths, and any other info that may be useful.

Plan

Next, think hard and write up a detailed implementation plan. Don't forget to include tests, lookbook components, and documentation. Use your judgement as to what is necessary, given the standards of this repo.

If there are things you are not sure about, use parallel subagents to do some web research. They should only return useful information, no noise.

You are an expert in analyzing development sessions and optimizing AI-human collaboration. Your task is to reflect on today's work session and extract learnings that will improve future interactions.

Session Analysis Phase

Review the entire conversation history and identify:

1. Problems & Solutions

What problems did we encounter?
Initial symptoms reported by user

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

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]

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

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?

Rust Cheat Sheet

Variables & Mutability

Variables are immutable by default. This makes Rust safer and makes concurrency easier.
Immutable means once a value is bound to that variable, it cannot be changed.
For example:

fn main() {
 let x = 5;

	Using Gemini and Context7 as MCP Server Sub-Agents for Claude Code
	Overview
	This guide explains how to integrate Google's Gemini AI models and Context7 (by Upstash) as sub-agents for Claude Code using the Model Context Protocol (MCP). Gemini provides deep code analysis and large context processing, while Context7 fetches version-specific documentation to ensure accuracy and reduce hallucinations. Claude Code orchestrates the workflow, delegating tasks to Gemini for analysis and Context7 for documentation.
	Why Use Gemini and Context7?

	Gemini Strengths:
	Large context window for analyzing extensive codebases or documentation.
	Complements Claude’s conversational and editing capabilities.
	Cost-effective via Gemini CLI’s free/low-cost plans.
	Ideal for deep code review, debugging, and brainstorming.

	This is an LLM-assisted workflow for creating a product requirement document using LLM assistance for task completion.
	It keeps track of inputs for the template and works with the user to acquire them, finally generating a completed PRD
	prompt when all slots are addressed.


	credit: Ian Nuttall - https://gist.github.com/iannuttall/f3d425ad5610923a32397a687758ebf2



	System-Prompt for Facilitating Chat-Based PRD Creation

	// Claude Code is a Beta product per Anthropic's Commercial Terms of Service.
	// By using Claude Code, you agree that all code acceptance or rejection decisions you make,
	// and the associated conversations in context, constitute Feedback under Anthropic's Commercial Terms,
	// and may be used to improve Anthropic's products, including training models.
	// You are responsible for reviewing any code suggestions before use.

	// (c) Anthropic PBC. All rights reserved. Use is subject to Anthropic's Commercial Terms of Service (https://www.anthropic.com/legal/commercial-terms).

	// Version: 0.2.9