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Found 56 Skills
Guidance on Python code style optimization and Pythonic idioms; Based on the complete content of *One Python Craftsman* and the "Friendly Python" concept, covering variable naming, control flow, data types, container types, function design, exception handling, decorators, file operations, and SOLID principles; Providing user-friendly and maintainer-friendly design patterns, review checklists, and over 140 practical templates
Optimizes Python library performance through profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and optimization strategies. Use when analyzing performance bottlenecks, finding memory leaks, or setting up performance regression testing.
Deep line-by-line code review that finds all bugs, logic errors, redundancies, and issues. Traces call stacks, fixes everything, verifies 100%. Use when reviewing features, PRs, code changes, or auditing for bugs.
Follow this sub-process for code optimization — handle tasks where 'behavior remains unchanged but structure changes' (structure / performance / readability). Shift single-module internal optimization from 'AI random refactoring' to 'first scan to generate a checklist, confirm each item with the user, execute step by step according to the method library, and obtain manual approval for each step'. Trigger scenarios: When the user mentions phrases like 'optimize / refactor / rewrite / split / poor performance / too long code' without any accompanying behavior changes. Do not handle new requirements (route to feature), bugs (route to issue), or cross-module architecture restructuring (route to architecture + decisions).
Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including Vector<T>, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.
Go performance patterns including efficient string handling, type conversions, and container capacity hints. Use when optimizing Go code or writing performance-critical sections.
Backend 리팩토링 전문가. "백엔드 리팩토링", "API 리팩토링", "서버 코드 정리" 키워드로 트리거. 기존 백엔드 코드를 test-data 가이드 코드 패턴으로 리팩토링.
Optimize code for performance, readability, or efficiency
Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more
Analyze code for performance issues and suggest optimizations. Use when users ask to "optimize this code", "find performance issues", "improve performance", "check for memory leaks", "review code efficiency", or want to identify bottlenecks, algorithmic improvements, caching opportunities, or concurrency problems.
Use AliCloud Milvus (serverless) with PyMilvus to create collections, insert vectors, and run filtered similarity search. Optimized for Claude Code/Codex vector retrieval flows.
Audit your Claude Code setup for prompt caching efficiency. Measures prefix size, hook patterns, rule duplication, dynamic injection sizes, and tool stability. Use when asked to 'check caching', 'optimize prompt cache', or 'audit setup efficiency'. Returns a scored report with fixes ranked by token savings.