lk:init

What problem does it solve? Starting to learn a new subject often leads to unfocused research and bloated syllabi. This Skill solves that by first aligning on your goals, time budget, and desired depth, then building a filtered knowledge map so you only study what matters. ## Core Features & Use Cases - Intent Intake: Interactively captures your target depth (Familiarity, Competency, or Mastery), weekly time budget, success metric, and scope exclusions before any research begins. - RAG Foundation Setup: Provisions Seed and Adversarial notebooks, uploads your sources, and initializes a persistent state file for the learning pipeline. - Intent-Filtered Knowledge Mapping: Queries the seed material to identify core topics, expansion topics, hard dependencies, and explicit scope boundaries, plus adversarial gap-analysis queries. - Use Case: You want to reach conversational competency in Kubernetes in 5 hours per week. The Skill narrows the subject to a handful of core topics, ignores out-of-scope areas, and hands off a structured state file to the roadmap skill. ## Quick Start Ask the agent to initialize a learning plan for a subject, for example: initialize a learning foundation for Kubernetes at competency level with five hours per week.