B143KC47

use for adaptive deep research, broad but accurate information gathering, literature review, github and project due diligence, source graph investigation, cited reports, claim verification, or decisions that require current sources, cross-checking, counterevidence, and synthesis across web pages, academic papers, official docs, repositories, datasets, local files, and conflicting perspectives. do not use for simple lookups answerable from one or two obvious sources.

Finds the best skills (local + online) for an engineering problem, codebase, or current task; evaluates each candidate with a quality rubric; recommends the best fit; and records what worked to a persistent registry so it gets smarter over time. Use when the user asks "what skill should I use for X", "find a skill for X", "is there a skill that can...", points at a repo/folder and asks which skills apply, or is starting a problem (research, fine-tuning, evaluation, deployment, UI, debugging, etc.) where an existing skill could help.

Reason from fundamental truths instead of analogy, then improve accuracy and ideation through mechanism maps, assumption ledgers, least-to-most decomposition, Fermi estimates, sensitivity checks, evidence grounding, self-consistency, verification questions, red-team critique, and structured brainstorming. Use for architecture, system design, technology selection, hard debugging, performance and scaling, migrations, strategy, product, research, scientific or business decisions, and explicit triggers such as "first principles", "challenge assumptions", "from scratch", "brainstorm", "think from fundamentals", or convention-based language like "best practice", "industry standard", "everyone uses", or "we've always done it".