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# from reddit: https://www.reddit.com/r/ClaudeAI/comments/1hciaxk/solved_installing_mcp_servers_on_windows_with/
param(
    [Parameter(Mandatory=$true)]
    [string]$PackageName
)

Write-Host "Starting installation process for $PackageName..."

# Check Claude Desktop folder
Write-Host "Checking Claude Desktop installation..."

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You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

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You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

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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.

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Prompt Chaining with QwQ, Qwen, o1-mini, Ollama, and LLM

Here we explore prompt chaining with local reasoning models in combination with base models. With shockingly powerful local models like QwQ and Qwen, we can build some powerful prompt chains that let us tap into their capabilities in a immediately useful, local, private, AND free way.

Explore the idea of building prompt chains where the first is a powerful reasoning model that generates a response, and then use a base model to extract the response.

Play with the prompts and models to see what works best for your use cases. Use the o1 series to see how qwq compares.

Setup

• Bun (to run bun run chain.ts ...)

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[
  { "code": "aa", "name": "Afar" },
  { "code": "ab", "name": "Abkhazian" },
  { "code": "ae", "name": "Avestan" },
  { "code": "af", "name": "Afrikaans" },
  { "code": "ak", "name": "Akan" },
  { "code": "am", "name": "Amharic" },
  { "code": "an", "name": "Aragonese" },
  { "code": "ar", "name": "Arabic" },
  { "code": "as", "name": "Assamese" },

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/*
 * twitter-entities.js
 * This function converts a tweet with "entity" metadata 
 * from plain text to linkified HTML.
 *
 * See the documentation here: http://dev.twitter.com/pages/tweet_entities
 * Basically, add ?include_entities=true to your timeline call
 *
 * Copyright 2010, Wade Simmons
 * Licensed under the MIT license

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/**
 * Simple Node.js script to turn a specific page on a Google Sheet
 * into a JSON object for the main purpose of HTML Templating.
 * 
 * @author jonobr1 / http://jonobr1.com
 * 
 */

var https = require('https');
var path = require('path');

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/** 
 * If there are service workers available to us, then on window load we can register our worker
 */
'serviceWorker' in navigator && window.addEventListener('load', () => {
  navigator.serviceWorker.register('worker.js')
    .then(() => {
      // Registration was successful
      console.log('ServiceWorker registration successful!');
    }, (err) => {
      // registration failed :(

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var document;

var dbg = (typeof console !== 'undefined') ? function(s) {
    console.log("Readability: " + s);
} : function() {};

/*
 * Readability. An Arc90 Lab Experiment. 
 * Website: http://lab.arc90.com/experiments/readability
 * Source:  http://code.google.com/p/arc90labs-readability