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Found 2,256 Skills
Best Practices Guide for Tauri Framework. Use Cases: Tauri Application Development, Code Review, Architecture Design
MoonBit (.mbt) coding standards and best practices. Use when writing, reviewing, or refactoring MoonBit code.
Эксперт SQL window functions. Используй для аналитических запросов, OVER clauses и ranking functions.
Analyze application logs from the .evlog/logs/ directory. Use when debugging errors, investigating slow requests, understanding request patterns, or answering questions about application behavior. Reads structured NDJSON wide events written by evlog's file system drain.
Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy".
Animation and motion design patterns using Motion library (formerly Framer Motion) and View Transitions API. Use when implementing component animations, page transitions, micro-interactions, gesture-driven UIs, or ensuring motion accessibility with prefers-reduced-motion.
Agent skill for reviewer - invoke with $agent-reviewer
Aggregate and display system metrics with anomaly detection for a time period
Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.
Use this skill when the user wants to inspect, report on, create, update, pause, resume, budget, target, upload assets for, or troubleshoot Meta, Facebook, or Instagram ads via the Marketing API. It covers ad accounts, campaigns, ad sets, ads, creatives, ad images and videos, targeting search, batch reads, and Insights reports. Helpful for requests about Meta Ads Manager, ROAS or CPA reporting, launch workflows, creative rollout, audience setup, delivery issues, budget changes, or bulk ad operations.
Use this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
Establish CPU/GPU baselines before resource-intensive operations.