rUv ruvnet

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

A complete set of requirements—covering UX, CLI, and code—that builds on the previous pipeline for training and optimizing ONNX models with test‑time compute methods using DSPy. This document specifies user stories, command‐line interface arguments, and sample code snippets to guide implementation.

1. Overview

The goal is to build a unified tool (or pipeline application) that:

Trains a model using DSPy (with integrated or hybrid PyTorch training).
Exports the optimized model to ONNX format.

Single File Agent Template for Deno

File Name: agent.ts

Installation & Setup

Install Deno (if not already installed)

curl -fsSL https://deno.land/install.sh | sh

Dark Enlightenment: A Gonzo Chronicle (2025–2030)

Foreword by rUv

We stand at the brink of a new political age. In the shadows of Silicon Valley boardrooms and Washington backrooms, an unlikely alliance has taken shape. The Dark Enlightenment – an obscure neo-monarchist ideology born on internet forums – has crept from fringe blogs into the corridors of power. When I first heard whispers about tech CEOs and White House aides reading the same forbidden tracts, I knew something extraordinary was unfolding. This chronicle that follows is a firsthand journey into that unfolding drama, written in the heat of events by an intrepid observer who witnessed the transformation up close. It reads like a political thriller because, in many ways, it is one – except every bit of it is based on real people and real ideas shaping our world.

To set the stage, let me sketch the key players and ideas at work, so you can follow the wild narrative that ensues:

Curtis Yarvin (Mencius Moldbug) – Ex-programmer turn

	#!/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.

	use wsl for all local terminal when running in windows.

	never hardcode .env variables in dockerfiles or code.

	User query: {base_task} --keep it simple

	Context from Previous Research (if available):
	Key Facts:
	{key_facts}