Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
141 skills found
A framework for managing the end-to-end LLM project lifecycle, from evaluating task-model fit and pipeline architecture design to implementing structured output parsing and agent-assisted development.
Expert guide for kagent: the Kubernetes-native framework for building, deploying, and managing AI agents, MCP tools, and A2A protocols.
Automate the migration of Netflix Conductor workflows to Temporal Python, including server orchestration, worker management, and workflow troubleshooting.
Implement a full Model Context Protocol (MCP) stack in Rails. Connect to external servers, expose your Rails app as an MCP server, or manage subprocess MCP containers via Docker with OAuth 2.1 PKCE support.
Manages complete plugin lifecycle for JUCE development: install, uninstall, reset, and destroy. Handles system folder deployment, cache management, and safe, version-controlled removal for audio developers.
Operate Railway infrastructure: manage projects, services, databases, object storage, deployments, environments, variables, logs, and performance metrics.
Full-stack SDLC agent workflow managing the entire production lifecycle from intake and planning to automated testing, CI/CD, and infrastructure deployment using MCP tools.
Unified AI gateway for 100+ LLMs with OpenAI-compatible API, model fallbacks, load balancing, and enterprise-grade tools.
Provision and manage Railway database services (Postgres, Redis, MySQL, MongoDB) with automated configuration and environment wiring.
Guides agent memory system implementation, compares frameworks (Mem0, Zep, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention.
Autonomous multi-agent LinkedIn system using LangGraph and Claude Opus 4.5 for trend research, content creation, voice profiling, and analytics-driven optimization.
Manage test infrastructure with IaC, Docker, and service virtualization. Optimize testing costs, ensure dev/prod environment parity, and automate environment provisioning for consistent, scalable software testing.