Total 45,964 skills
Showing 12 of 45964 skills
Implements CQRS with event sourcing on the iii engine. Use when building command/query separation, event-sourced systems, or fan-out architectures where commands publish domain events and multiple read model projections subscribe independently.
Official Framer Motion skill for layout animations — shared layout transitions, layoutId, exit animations, AnimatePresence. Use when building shared element transitions, layout animations, reorderable lists, or when asking about Framer Motion layout, layoutId, or shared transitions.
Ship a new dtctl release — bump version, write changelog entries, run tests, commit, tag, push, and write GitHub release notes. Use this skill whenever the user says "release", "ship it", "cut a release", "new version", "bump version", "publish", or asks about the dtctl release process. Also use when the user wants to update CHANGELOG.md for a release or write GitHub release notes.
Create a git release — tag, push, and create GitHub release
Deploy vLLM to Kubernetes (K8s) with GPU support, health probes, and OpenAI-compatible API endpoint. Use this skill whenever the user wants to deploy, run, or serve vLLM on a Kubernetes cluster, including creating deployments, services, checking existing deployments, or managing vLLM on K8s.
Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.
Replay-first debug flow for SGLang serving problems. Use when a live or recent server shows health-check failures, latency or throughput regressions, queue growth, timeouts, distributed stalls, crash dumps, wrong outputs after deploys, or PD/EP/HiCache issues, and the job is to turn the problem into a replay plus the right next debug tool.
Shared optimization guidance plus CuTe Python DSL overlays. Use when: (1) selecting optimizations for a CuTe Python DSL kernel, (2) deciding whether a finding is shared or cute-dsl-specific, (3) recording CuTe Python DSL implementation notes, (4) reviewing the knowledge layout for cute-dsl work, (5) mapping shared patterns to a CuTe Python DSL implementation surface.
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.
CuTe Python DSL API reference and implementation patterns for NVIDIA GPU kernel programming. Provides execution model, core API table, key constraints, common patterns, and documentation index. Use when: (1) writing or modifying CuTe DSL kernel code, (2) looking up CuTe DSL API syntax, (3) implementing attention/GEMM/MLA patterns in CuTe DSL, (4) understanding CuTe DSL execution model and compilation pipeline, (5) checking what CuTe DSL can and cannot do.
Find AI models on Replicate using search and curated collections.
Extracts the behavioral requirements, user experience (UX) flows, micro-interactions, and conditional visibility rules of a frontend component from its source code. Produces an experience.md file focused on "how it feels, behaves, and when it renders".