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Creating Custom NPX Components with Vite.js

Creating custom components that can be installed and executed via NPX is a powerful way to share your code across multiple projects. With Vite.js, this process becomes more streamlined. Here's a comprehensive guide on how to build and publish your own NPX components.

Setting Up Your Project

First, you need to create a new Vite project:

npm create vite@latest my-npx-component

Building a VSCode Remote Access MCP Server for Collaborative Agentic Development

Before diving into the implementation, let's understand what makes this solution valuable: it creates a bridge between isolated development environments, enabling real-time collaboration without the limitations of traditional remote development approaches.

MCP Server Architecture

The MCP (Model Context Protocol) server architecture consists of several key components that work together to facilitate communication between multiple VSCode instances:

A centralized MCP server that handles message routing and state synchronization
Client connections from multiple workspaces or codespaces

Great. I’ll develop a phased implementation plan, edge function code, deployment strategy, user guide, and full documentation for a key rotator using Deno Edge Functions. This will focus on rotating Gemini 2.5 Pro API keys to handle 429 rate limits efficiently.

I’ll return with a clear breakdown of components, including a secure architecture, key storage and cycling logic, usage limits, and guidance for setup and customization.

Secure API Key Rotator for Google Gemini 2.5 Pro (Deno Edge Functions)

Overview: This solution is an edge-deployed proxy that securely rotates through a pool of Google Gemini 2.5 Pro API keys to avoid hitting per-key rate limits (e.g. free-tier limit of ~2 requests/minute (Gemini 2.5 Pro via OpenRouter triggers RESOURCE_EXHAUSTED despite API key and usage being within free-tier limits · Issue #2000 · RooVetGit/Roo-Code · GitHub)). By

Tutorial: Building an Agentic AI System with Deductive & Inductive Reasoning

1. Introduction

Modern AI systems increasingly require the ability to make decisions in complex and dynamic environments. One promising approach is to create an agentic AI system that combines:

Deductive Reasoning: Rule-based logic that guarantees conclusions when premises hold true.
Inductive Reasoning: Data-driven inference that generalizes from specific cases to handle uncertainty.

By integrating these two methods, often referred to as neuro-symbolic AI, an agent can provide transparent, explainable decisions while also adapting to new data. This tutorial explains the concepts behind this approach and shows you how to build an edge-deployable ReAct agent using Deno.

agentic-robots-txt: Dynamic Robots.txt with MCP Integration

agentic-robots-txt is a Node.js package that generates a dynamic robots.txt file with extended directives for AI agents, and exposes those rules via Anthropic’s Model Context Protocol (MCP). It helps web developers control standard web crawlers and guide AI model agents by providing an agentic manifest and agent guide references in the robots.txt. The package also includes an MCP server so AI agents (MCP clients) can retrieve these rules programmatically. Key features include dynamic rule generation, MCP compliance, security controls, and easy integration into frameworks like Express.

Dynamic `robots.txt` Generation

A robots.txt file defines crawl rules for bots (traditionally search engines) by specifying allowed and disallowed paths (The ultimate guide to robots.txt • Yoast). agentic-robots-txt automates creatin

	# SPARC Agentic Development Rules

	Core Philosophy

	1. Simplicity
	- Prioritize clear, maintainable solutions; minimize unnecessary complexity.

	2. Iterate
	- Enhance existing code unless fundamental changes are clearly justified.

	{
	"customModes": [
	{
	"slug": "sparc",
	"name": "⚡️ SPARC Orchestrator",
	"roleDefinition": "You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.",
	"customInstructions": "Follow SPARC:\n\n1. Specification: Clarify objectives and scope. Never allow hard-coded env vars.\n2. Pseudocode: Request high-level logic with TDD anchors.\n3. Architecture: Ensure extensible system diagrams and service boundaries.\n4. Refinement: Use TDD, debugging, security, and optimization flows.\n5. Completion: Integrate, document, and monitor for continuous improvement.\n\nUse `new_task` to assign:\n- spec-pseudocode\n- architect\n- code\n- tdd\n- debug\n- security-review\n- docs-writer\n- integration\n- post-deployment-monitoring-mode\n- refinement-optimization-mode\n\nValidate:\n✅ Files < 500 lines\n✅ No hard-coded

	#!/usr/bin/env -S node --no-warnings=ExperimentalWarning --enable-source-maps

	// 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).

	Objective:
	Enhance [Your Name]’s [Field/Expertise] through [Key Approach] to refine [Core Focus Areas] and achieve [Desired Outcomes].

	Instructions:
	1. Clarity: Use structured steps, examples, and definitions.
	2. References: Cite sources at the end.
	3. Segmentation: Break complex topics into logical sections.
	4. Interactivity: Encourage refinement through feedback.
	5. Tools: Specify relevant code, methods, or frameworks.
	6. Feedback: Use benchmarks for continuous improvement.