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Found 487 Skills
Create distinctive, bold UI designs that avoid generic AI aesthetics. This skill should be used when users want frontend components with strong visual identity, creative typography, intentional color palettes, and production-grade animations - specifically to avoid the bland, safe, homogeneous "AI slop" that plagues most generated interfaces.
Optimize application performance - bundle size, API response times, database queries, React rendering, and serverless function performance. Use when investigating slow pages, profiling, load testing, or before production deployments.
Generate web assets including favicons, app icons (PWA), and social media meta images (Open Graph) for Facebook, Twitter, WhatsApp, and LinkedIn. Use when users need icons, favicons, social sharing images, or Open Graph images from logos or text slogans. Handles image resizing, text-to-image generation, and provides proper HTML meta tags.
Python performance optimization patterns using profiling, algorithmic improvements, and acceleration techniques. Use when optimizing slow Python code, reducing memory usage, or improving application throughput and latency.
MongoDB query optimization and indexing strategies. Use when writing queries, creating indexes, building aggregation pipelines, or debugging slow operations. Triggers on "slow query", "create index", "optimize query", "aggregation pipeline", "explain output", "COLLSCAN", "ESR rule", "compound index", "partial index", "TTL index", "text search", "geospatial", "$indexStats", "profiler".
Remove LLM-generated code patterns that add noise without value. Use when reviewing diffs, PRs, or branches to clean up AI-generated code. Triggers include requests to "remove slop", "clean up AI code", "review for AI patterns", or checking diffs against main for unnecessary verbosity, redundant checks, or over-engineering introduced by LLMs. Language-agnostic.
BAZDMEG Method workflow checkpoint system for AI-assisted development. Enforce quality gates at three phases: pre-code, post-code, and pre-PR. Use when: (1) starting a new feature or bug fix, (2) finishing AI-generated code before review, (3) preparing a pull request, (4) running a planning interview, (5) auditing automation readiness, (6) preventing AI slop, (7) session bootstrap, (8) source rank, (9) domain gates, (10) bugbook. Triggers: 'bazdmeg', 'pre-code checklist', 'post-code checklist', 'pre-PR checklist', 'planning interview', 'quality gates', 'session bootstrap', 'source rank', 'domain gates', 'bugbook'.
React performance optimization patterns including memoization, code splitting, bundle size reduction, re-render elimination, and profiling. Covers React Compiler automatic optimization, manual memo/useMemo/useCallback targeting, React.lazy with Suspense, barrel file avoidance, content-visibility for large lists, startTransition for non-urgent updates, and React DevTools profiling. Use when optimizing React app performance, reducing bundle size, eliminating unnecessary re-renders, debugging slow components, code splitting, or profiling rendering bottlenecks. Use for performance audit, bundle analysis, re-render diagnosis, lazy loading, virtualization.
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 about performance, slowness, optimization, or wants to make code more efficient. Focus on hot paths, unnecessary work, and algorithmic complexity.
Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.
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).