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Found 35 Skills
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.
Go performance patterns including efficient string handling, type conversions, and container capacity hints. Use when optimizing Go code or writing performance-critical sections.
Write modern PHP with generators, SPL, and PHP 8+ features. Use for PHP development or optimization.
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
Detect performance anti-patterns and apply optimization techniques in Go. Covers allocations, string handling, slice/map preallocation, sync.Pool, benchmarking, and profiling with pprof. Use when checking performance, finding slow code, reducing allocations, profiling, or reviewing hot paths. Trigger examples: "check performance", "find slow code", "reduce allocations", "benchmark this", "profile", "optimize Go code". Do NOT use for concurrency correctness (use go-concurrency-review) or general code style (use go-coding-standards).
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
Supports automatic generation/optimization/fixation/checking of index files to ensure all index files (index.ts / index.js) comply with the barrel export specification. Core principle: All index files must follow the barrel export specification.
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.
Create ShinkaEvolve task scaffolds from a target directory and task description, producing `evaluate.py` and `initial.<ext>` (multi-language). Use when asked to set up new ShinkaEvolve tasks, evaluation harnesses, or baseline programs for ShinkaEvolve.
Specialized AI assistant for DSPy development with deep knowledge of predictors, optimizers, adapters, and GEPA integration. Provides session management, codebase indexing, and command-based workflows.
Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more
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).