The Java ecosystem pack — the exact build, test, lint, format and dependency commands, plus the idioms, that the java-developer agent uses.
Foundational Java patterns covering core syntax, object-oriented programming, generics, collections, streams, lambdas, and modern Java features.
The Kotlin ecosystem pack — the exact build, test, lint, format and dependency commands, plus the idioms, that the kotlin-developer agent uses.
Foundational Kotlin patterns covering null safety, coroutines, data classes, extension functions, and Kotlin idioms.
Kotlin-specific library development patterns. Use when creating Kotlin libraries, designing idiomatic Kotlin APIs with extension functions and DSLs, configuring Gradle Kotlin DSL…
The Python ecosystem pack — the exact build, test, lint, format and dependency commands, plus the idioms, that the python-developer agent uses.
Foundational Python patterns covering core syntax, idioms, type hints, testing, and modern tooling. Use when writing Python code, understanding Pythonic patterns, working with…
Foundational Roc patterns covering platform/application architecture, records, tags, pattern matching, abilities, and functional idioms.
Foundational Rust patterns covering core syntax, traits, generics, lifetimes, and common idioms. Use when writing Rust code, understanding ownership basics, working with…
Rust documentation practices for HASH codebase. Use when writing doc comments, documenting functions/types/traits/modules, creating error sections, using intra-doc links — from…
Profile and debug Rust code using valgrind and callgrind with nextest integration. Use when debugging crashes, segfaults, memory errors, analyzing instruction counts, cache…
The Swift ecosystem pack — the exact build, test, lint, format and dependency commands, plus the idioms, that the swift-developer agent uses.
Foundational Swift development patterns covering modern Swift syntax, SwiftUI, protocol-oriented programming, and Cocoa Touch frameworks.
The TypeScript ecosystem pack — the exact build, test, lint, format and dependency commands, plus the idioms, that the typescript-developer agent uses.
Foundational TypeScript patterns covering types, interfaces, generics, utility types, and common idioms.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, to — from…
Use when implementing a LangChain-based agent runtime from an approved ai-architecture.md agent control-flow design.
LangChain & AI Agent Frameworks expert. Covers LCEL chains, ReAct/Plan-and-Execute/Multi-Agent architectures, tool calling, memory systems, LangGraph, CrewAI, prompt engineering,…
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration. — from whatamelon/AGENT-HARNESS-BASELINE
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration. — from majiayu000/claude-skill-registry
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating…
Configure CI/CD for LangChain with GitHub Actions, mocked unit tests, gated integration tests, and RAG pipeline validation.
Comprehensive reference for the LangChain ecosystem including LangChain, LangGraph, and Deep Agents for Python 3.10+.
Optimize LangChain API costs with token tracking, model tiering, caching, prompt compression, and budget enforcement.
Collect LangChain debug evidence for troubleshooting and bug reports. Use when preparing GitHub issues, collecting LangSmith traces, or gathering diagnostic info for complex…
Deploy LangChain applications to production with LangServe, Docker, and cloud platforms (Cloud Run, AWS Lambda).
Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.
Create a minimal working LangChain example with LCEL chains. Use when starting a new LangChain integration, testing your setup, or learning LCEL pipe syntax with prompts and…
Create a minimal working LangChain example. Use when starting a new LangChain integration, testing your setup, or learning basic LangChain patterns with chains and prompts.
Builds LLM-powered applications with LangChain.js for chat, agents, and RAG. Use when creating AI applications with chains, memory, tools, and retrieval-augmented generation in…
LangChain 1.0 / LangGraph 1.0 changes — create_agent replaces create_react_agent, middleware system, ToolRuntime, content_blocks, structured output strategies, simplified…
Persist LangGraph agent state correctly with MemorySaver and PostgresSaver — thread_id discipline, JSON-serializable state rules, time-travel, schema migration.
Build LangGraph 1.0 human-in-the-loop approval flows with `interrupt_before` / `interrupt_after` and `Command(resume=...)` — JSON-serializable state, clean resume semantics, and…
Pick the correct LangGraph 1.0 stream_mode ("messages" vs "updates" vs "values"), wire it into SSE or WebSocket without proxy-buffering gotchas, and filter astream_events(v2)…
Compose LangGraph 1.0 subgraphs correctly — shared state key propagation, Send / Command(graph=...) dispatch, callback scoping, per-subgraph recursion budgets, and testing each…
Configure LangChain local development workflow with testing and mocks. Use when setting up dev environment, creating test fixtures with mocked LLMs, or establishing a rapid…
LangChain MCP Adapters — connect LangChain agents to MCP (Model Context Protocol) servers. Load MCP tools, prompts, and resources as LangChain-compatible objects.
Build composable middleware for LangChain 1.0 chains and LangGraph 1.0 agents — PII redaction, caching, retry, token budgets, guardrails — with ORDERING rules that avoid cache-key…
Optimize LangChain application performance: latency, throughput, streaming, caching, batch processing, and connection pooling.
Production readiness checklist for LangChain applications. Use when preparing for launch, validating deployment readiness, or auditing existing production LangChain systems.
Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and…
Implement LangChain rate limiting, retry strategies, and backoff. Use when handling API rate limits, controlling request throughput, or implementing concurrency-safe batch…
Implement LangChain reference architecture for production systems: layered design, provider abstraction, chain registry, RAG pipelines, and multi-agent orchestration.
Implement LangChain callback handlers, streaming, webhooks, Server-Sent Events (SSE), and WebSocket integration.
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration.
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and…
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j),…
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone,…
LangChain.js - TypeScript framework for building LLM-powered applications with agents, chains, RAG, tools, memory, and integrations for OpenAI, Anthropic, Google, and hun — from…
LangChain.js - TypeScript framework for building LLM-powered applications with agents, chains, RAG, tools, memory, and integrations for OpenAI, Anthropic, Google, and hun — from…
LangExtract by Google is a Python library for extracting structured information from unstructured text using LLMs with precise source grounding.
A powerful Python-based visual framework for building and deploying AI-powered agents and workflows with Model Context Protocol (MCP) integration, drag-and-drop interface — from…
A powerful Python-based visual framework for building and deploying AI-powered agents and workflows with Model Context Protocol (MCP) integration, drag-and-drop interface — from…