Search and filter by type across the catalog
1,239 results in Observability, Evals, Memory, Skills · page 40 of 52
Deploy code to Railway using "railway up". Use when user wants to push code, says "railway up", "deploy", "ship", or "push". For initial setup or creating services, use railway-new skill. For Docker images, use railway-environment skill.
Manage Railway deployments - view logs, redeploy, restart, or remove deployments. Use for deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes). NOT for deleting services - use railway-environment skill with isDeleted for that.
Query, stage, and apply configuration changes for Railway environments. Use for ANY variable or env var operations, service configuration (source, build settings, deploy settings), lifecycle (delete service), and applying changes. Prefer over railway-status skill for any configuration or variable queries.
Create Railway projects, services, and databases with proper configuration. Use when user says "setup", "deploy to railway", "initialize", "create project", "create service", or wants to deploy from GitHub. Handles initial setup AND adding services to existing projects. For databases, use railway-railway-database skill instead.
Check service status, rename services, change service icons, link services, or create services with Docker images. For creating services with local code, prefer railway-new skill. For GitHub repo sources, use railway-new skill to create empty service then railway-environment skill to configure source.
Check current Railway project status for this directory. Use when user asks "railway status", "is it running", "what's deployed", "deployment status", or about uptime. NOT for variables or configuration queries - use railway-environment skill for those.
Search and deploy services from Railway's template marketplace. Use when user wants to add a service from a template, find templates for a specific use case, or deploy tools like Ghost, Strapi, n8n, Minio, Uptime Kuma, etc. For databases (Postgres, Redis, MySQL, MongoDB), prefer the railway-database skill.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
Comprehensive React and Next.js performance optimization guide with 40+ rules for eliminating waterfalls, optimizing bundles, and improving rendering. Use when optimizing React apps, reviewing performance, or refactoring components.
This skill should be used when building React components with TypeScript, typing hooks, handling events, or when React TypeScript, React 19, Server Components are mentioned. Covers type-safe patterns for React 18-19 including generic components, proper event typing, and routing integration (TanStack Router, React Router).