name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
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This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.
The philosophy is simple: Autonomy through discipline. Trust through verification.
This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.
I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58
| // ==UserScript== | |
| // @name Aternos Anti Anti-adblock | |
| // @namespace r0630hh1edcuum5397kimyc0ucwy2h3psn4c6r1u4j | |
| // @version 0.1.341 | |
| // @description Fuck anti-adblock from the free hosting minecraft servers Aternos.org. Parry this you filthy casual! | |
| // @author Angry Developer against excessive ADs | |
| // @source https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a | |
| // @supportURL https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a | |
| // @updateURL https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a/raw/aternosAntiAntiadblock.user.js | |
| // @downloadURL https://gist.github.com/DvilMuck/f2b14f3f65e8f22974d781277158f82a/raw/aternosAntiAntiadblock.user.js |
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cd ~/DownloadsA pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
You know that specific kind of developer frustration where you've got four browser tabs open — one for OpenAI billing, one for Anthropic credits, one for DeepSeek, and one you forgot about entirely — and you're just trying to run a quick experiment? Yeah. That's the exact problem AgentRouter showed up to fix.
Launched in October 2025, AgentRouter (agentrouter.org) is a non-profit AI API gateway that routes your requests to multiple top-tier AI providers — Anthropic, OpenAI, DeepSeek, Zhipu AI — through a single API key and a single base URL. It's OpenAI-compatible, which means any tool that talks to OpenAI can be pointed at AgentRouter with one config change. And right now, signing up through a referral link gets you $200 in free credits before you've spent a single dollar.