Automate LeetCode coaching workflows with Hot 100 taxonomy and template extraction.
npx skills add https://github.com/Decalogue/flyllm --skill leetcode-coach
Please help me install this Agent Skill. Skill: leetcode-coach Source: https://github.com/Decalogue/flyllm/tree/main/.claude/skills/leetcode-coach Command: npx skills add https://github.com/Decalogue/flyllm --skill leetcode-coach
The Skill guides users to build mental models from first principles, extract reusable templates, and systematically validate ideas with a Feynman-testing approach, converting algorithm knowledge into a local knowledge base rather than providing direct code.
Select a LeetCode problem and apply the SOP to extract a reusable template, test edge cases, and summarize a memory note.
High-intent search queries and answers about installing and using this skill.
LeetCode Hot 100 interview preparation uses a structured coaching workflow with problem identification, pattern extraction, template retrieval, edge-case planning, and Feynman testing to convert problems into reusable knowledge rather than direct code.
You can extract reusable templates systematically by applying a repeatable process that identifies problem features, retrieves matching templates, and consolidates memory-anchored notes for long-term retention and quick recall.
Feynman testing validates algorithm ideas by systematically explaining and testing edge cases, ensuring you build mental models from first principles instead of memorizing solutions during LeetCode practice.
This coaching approach does not provide direct code solutions; instead, it guides you to build mental models from first principles, extract reusable templates, and create memory-anchored notes for systematic algorithm mastery.
Progress tracking across LeetCode problem categories is handled through the Hot 100 taxonomy integration, which structures your practice workflow from problem identification through to knowledge consolidation in a local knowledge base.
A structured coaching workflow is needed when you want to master algorithm patterns systematically, moving beyond ad-hoc problem solving to build a repeatable process for template extraction, edge-case planning, and long-term memory retention.
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