Top Rated
The most starred skills loved by the community. Quality guaranteed!
numpy-masked
by cuba6112
Masked arrays for robust handling of missing or invalid data, ensuring they are excluded from statistical and mathematical computations. Triggers: masked array, numpy.ma, missing data, invalid values, hard mask.
pytorch-distributed
by cuba6112
Distributed training strategies including DistributedDataParallel (DDP) and Fully Sharded Data Parallel (FSDP). Covers multi-node setup, checkpointing, and process management using torchrun. (ddp, fsdp, distributeddataparallel, torchrun, nccl, rank, process-group)
torch-compile
by cuba6112
Optimize PyTorch with torch.compile (TorchDynamo/Inductor), focusing on compile overhead, graph breaks, and benchmark methodology. Use when speeding up PyTorch models or debugging compile behavior; triggers: torch.compile, torchdynamo, inductor, graph break, pytorch optimization.
torchserve
by cuba6112
Model serving engine for PyTorch. Focuses on MAR packaging, custom handlers for preprocessing/inference, and management of multi-GPU worker scaling. (torchserve, mar-file, handler, basehandler, model-archiver, inference-api)
numpy-indexing
by cuba6112
Advanced indexing techniques including slicing, fancy indexing, and boolean masks, along with memory implications of views vs. copies. Triggers: indexing, slicing, fancy indexing, boolean mask, np.where, np.ix_.
numpy-statistics
by cuba6112
Standard and NaN-robust statistical functions for data analysis, histograms, and correlation matrices. Triggers: statistics, mean, nanmean, histogram, corrcoef, percentile, std.
docker-compose
by cuba6112
Multi-service orchestration with Docker Compose, focusing on network isolation, environment-specific profiles, and service discovery. Triggers: docker-compose, container-networking, docker-profiles, service-discovery, yaml-config.
pytorch-geometric
by cuba6112
Library for Graph Neural Networks (GNNs). Covers MessagePassing layers, modular aggregation schemes, and handling large graphs via mini-batching with disjoint graph representation. (pyg, messagepassing, gnn, gcn, gat, edge_index, knn_graph, global_mean_pool)
api-design
by kprsnt2
REST and GraphQL API design best practices including HTTP methods, status codes, and documentation.
cicd
by kprsnt2
CI/CD pipeline best practices including GitHub Actions, testing, and deployment strategies.
django
by kprsnt2
Django framework best practices including project structure, ORM, and security.
mongodb
by kprsnt2
MongoDB best practices including schema design, indexing, and query optimization.
docker
by kprsnt2
Docker and containerization best practices including multi-stage builds, security, and Docker Compose.
express
by kprsnt2
Express.js server best practices including middleware, error handling, and security.
fastapi
by kprsnt2
FastAPI framework best practices including Pydantic schemas, dependency injection, and async patterns.
nextjs
by kprsnt2
Next.js framework best practices including App Router, data fetching, and performance optimization.
git-workflow
by kprsnt2
Git best practices and workflows including conventional commits, branching strategies, and collaboration patterns.
nodejs
by kprsnt2
Node.js server development patterns including async patterns, error handling, and security best practices.
go
by kprsnt2
Best practices for Go development including idiomatic patterns, concurrency, and error handling.
performance
by kprsnt2
Web performance optimization best practices including Core Web Vitals, loading, and caching.
javascript
by kprsnt2
Best practices for JavaScript/TypeScript development including modern ES6+ patterns, error handling, and performance optimization.
python
by kprsnt2
Best practices for Python development including PEP 8, modern Python 3.10+ features, and package management.
kubernetes
by kprsnt2
Kubernetes deployment best practices including resource management, security, and observability.
react
by kprsnt2
React development patterns and best practices including hooks, state management, and performance optimization.