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Found 51 Skills
Analyze code performance, detect bottlenecks, suggest optimizations for algorithms, queries, and resource usage. Use when improving application performance or investigating slow code.
Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more
Analyze and optimize Aptos Move contracts for gas efficiency, identifying expensive operations and suggesting optimizations. Triggers on: 'optimize gas', 'reduce gas costs', 'gas analysis', 'make contract cheaper', 'gas efficiency', 'analyze gas usage', 'reduce transaction costs'.
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.
Performance optimization specialist for improving application speed and efficiency. Use when investigating performance issues or optimizing code.
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.
Initialize evo for the current repository by exploring the codebase, proposing unexplored optimization dimensions, constructing the benchmark inside a baseline worktree, and running the first experiment. Use when the user invokes /evo:discover, mentions setting up evo, wants to instrument a codebase for autonomous optimization, or asks to start a new evo run on a project.
Use this when the user asks to refactor, clean up, optimize, or improve code quality.
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.
Improve code performance without changing behavior. Use when code fails latency/throughput requirements. Covers profiling, caching, and algorithmic optimization.
Analyze development sessions, capture learnings, and improve Claude Code instructions. Use when the user wants to reflect on a session, improve CLAUDE.md, extract learnings, or optimize AI-human collaboration. Supports two modes: quick (default) focuses on CLAUDE.md improvements, deep mode performs comprehensive session analysis with learning capture.