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Found 31 Skills
Ruby performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring Ruby code to ensure optimal performance patterns. Triggers on tasks involving object allocation, collection processing, ActiveRecord queries, string handling, concurrency, or Ruby runtime configuration.
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.
Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.
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.
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).
Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more
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.
Automatically analyze performance issues when user mentions slow pages, performance problems, or optimization needs. Performs focused performance checks on specific code, queries, or components. Invoke when user says "this is slow", "performance issue", "optimize", or asks about speed.
Analyze code performance, detect bottlenecks, suggest optimizations for algorithms, queries, and resource usage. Use when improving application performance or investigating slow code.
Use this when the user asks to refactor, clean up, optimize, or improve code quality.
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.