Skill UI

Master Google Cloud’s Agent Development Kit and Starter Pack to build containerized multi-agent systems, automate RAG workflows, and deploy production-ready agents via Cloud Run, GKE, Agent Engine, or CI/CD pipelines.

A specialized AI assistant acting as an expert Haskell engineer. This skill focuses on advanced type systems (GADTs, type families), pure functional architecture, concurrency (STM, async), and high-assurance software design. Use it for building robust, idiomatic, and maintainable Haskell code.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

Provides expert guidance on Julia 1.10+ development workflows, from multiple dispatch and performance tuning to testing, package management, and deployment. Use it when building high-performance scientific, ML, data, or web systems and you need modern tooling, checklists, and verification steps.

Build asynchronous Kling AI video generation workflows with queues, workers, and state machines to orchestrate event-driven pipelines and monitor job progress within larger systems.

Master the LangChain framework, the industry standard for building sophisticated and production-grade Large Language Model (LLM) applications. This skill covers core concepts including autonomous agents, multi-step chains, memory management, Retrieval-Augmented Generation (RAG), and external tool integration, enabling the development of complex, stateful AI systems.

Framework for building stateful, multi-actor AI applications with LangGraph, covering graph construction, reducers, persistence, human-in-the-loop patterns, and ReAct agents suitable for Python production systems.

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

Guides engineers through building, testing, and deploying Model Context Protocol servers or clients that link AI systems with external tools, emphasizing transports, Zod/Pydantic schema validation, and JSON-RPC compliance checks.

This skill provides a comprehensive framework for mastering microservices architecture patterns. It covers critical aspects of building robust, distributed systems, including defining service boundaries (Domain-Driven Design), managing inter-service communication, handling distributed data transactions, and implementing various resilience patterns (e.g., circuit breaker, circuit breaker). It is essential for large-scale system decomposition.

Guides ML engineers in building production-ready systems with PyTorch 2.x, TensorFlow, and modern tooling, covering serving pipelines, feature engineering, monitoring, and MLOps infrastructure for scalable, reliable deployments.

Provides expert guidance on monorepo architecture, build systems, and dependency management using Nx, Turborepo, Bazel, and Lerna to set up scalable workspaces, optimize CI, and orchestrate tasks across multi-project teams.