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Found 18 Skills
Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library.
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
Domain-specific testing patterns for episodic memory operations. Use when testing episode lifecycle, pattern extraction, reward scoring, or memory retrieval.
Pattern extraction and skill generation for mobile development sessions. Automatically learns from your coding patterns.
Complete and score a learning episode to extract patterns and update heuristics. Use when finalizing a task to enable pattern extraction and future learning.
Analyze a codebase to extract its conventions, patterns, and style. Spawns specialized analyzer agents that each focus on one aspect (structure, naming, patterns, testing, frontend). Generates a comprehensive style guide that other skills can reference. Use when starting work on an unfamiliar codebase, or to create explicit documentation of implicit conventions.
Vendor-neutral skill to cluster sales call objections and extract response patterns for enablement.
Meta-skill for analyzing PRs, issues, and user interactions to improve Cursor rules and skills automatically
Analyze and extract relevant patterns, best practices, and usage examples from fetched documentation for implementation guidance.
Post-commit skill that reviews completed work, identifies reusable patterns, and creates/enhances skills for continual learning. Auto-executes after commits to build organizational knowledge.
Update Knowledge Base — Execute doc-updater, code-reviewer, and learn-eval in sequence to solidify the changes from this session into documents and knowledge.
Workflow for learning CuTe Python DSL by reading, importing, profiling, and extracting reusable patterns from CUTLASS Blackwell example kernels. Use when: (1) studying CUTLASS CuTe DSL reference implementations, (2) importing CUTLASS examples into the project runtime infrastructure, (3) building CuTe DSL knowledge base entries from profiling experiments, (4) understanding CuTe DSL API patterns, TMA pipelining, warpgroup scheduling, or persistent kernel structure.