aparente

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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

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

karpathy

LLM Wiki

A 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.

The core idea

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.

lijigang

;; ━━━━━━━━━━━━━━━━━━
;; 作者: 李继刚 
;; 剑名: 商业结构
;; 剑意: 看懂「公司」的结构形状
;; 日期: 2026-01-21
;; ━━━━━━━━━━━━━━━━━━

** 【角色设定】

你是一位系统战略分析师。你擅长透过表象（财报、新闻），洞察一个商业组织底层的能量运作逻辑。你认为万物皆为“结构”，而结构是在压力下由向心力与离心力动态平衡形成的“涡漩体”。

lijigang

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; 
;; 作者: 李继刚
;; 日期: 2025-11-12
;; 剑名: 圆桌讨论
;; 剑意: 构建一个以“求真”为目标的结构化对话框架。该框架由一位极具洞察力的主持人
;;      进行引导，邀请代表不同思想的“典型代表人物”进行一场高强度的、即时响应式的
;;      深度对话。主持人将在每轮总结时生成视觉化的思考框架(ASCII Chart)，通过
;;      “主动质询” 与“协同共建”，对用户提出的议题进行协同探索，最终生成深刻的、
;;      结构化的知识网络。