Foundational Clojure patterns covering functional programming, REPL-driven development, immutable data structures, and idiomatic code.
The C++ ecosystem pack — the exact build, test, lint, format and dependency commands, plus the idioms, that the cpp-developer agent uses. Load when a task is implemented in C++.
Foundational C++ patterns covering core syntax, classes, templates, RAII, move semantics, and modern C++ features (C++11/14/17/20).
Foundational Cypher (Neo4j) patterns covering graph pattern matching, MATCH/CREATE/MERGE/DELETE operations, relationships, path patterns, aggregation, filtering, and common graph…
The Dart & Flutter ecosystem pack — the exact build, test, analyse, format and dependency commands, plus the idioms, that the flutter-dart-developer agent uses.
Use when preparing the data, annotation, and reproducibility materials for a Language (LSA) manuscript — shared datasets and code, glossed corpora, sound files, and the ethics of…
Foundational Elixir patterns covering modules, pattern matching, processes, OTP behaviors (GenServer, Supervisor), Phoenix framework basics, and functional programming idioms.
Foundational Erlang patterns covering OTP behaviors, fault-tolerant systems, distributed computing, pattern matching, processes, and supervision trees.
Foundational F# patterns covering functional-first programming, type providers, computation expressions, and domain modeling.
Use when writing, auditing, or generating documentation for projects in unsupported languages — covers docstring conventions, API doc extraction, and generic heuristic patterns.
Foundational Go patterns covering types, interfaces, goroutines, channels, and common idioms. Use when writing Go code, understanding Go's concurrency model, or needing guidance…
Foundational Haskell patterns covering pure functional programming, type system, type classes, monads, and common idioms.
Foundational Objective-C patterns covering classes, protocols, categories, memory management (ARC/retain-release), blocks, GCD, and Foundation framework.
Build React SPAs where components are declarative UI consuming external state (Zustand/XState/TanStack Query). Logic lives in stores, not components.
Use when anticipating how a Language (LSA) manuscript will be judged — the double-anonymous review, the general-audience and cross-framework bar, the desk-return filters…
Rust 1.92+ development specialist covering Axum, Tokio, SQLx, and memory-safe systems programming. Use when building high-performance, memory-safe applications or WebAsse — from…
Foundational Scala patterns covering immutability, pattern matching, traits, case classes, for-comprehensions, and functional programming.
Scala-specific library development patterns. Use when creating Scala libraries, designing public APIs with immutability, configuring sbt/Mill build tools, managing cross-Scala…
Foundational SPARQL patterns covering RDF querying, triple patterns, graph patterns, and semantic web fundamentals. Use when querying RDF data or working with knowledge graphs.
Traducción de contratos jurÃdicos respetando 1:1 las sentencias locales de derecho legal.
Use when deciding whether a linguistics project fits Language (Linguistic Society of America) and how to frame it.
Fornece padrões de engenharia e tipografia digital moderna usando Typst. Cobre sintaxe de marcação, funções customizadas, criação de templates reutilizáveis, regras de exibição…
Use when polishing the prose and house style of a Language (LSA) manuscript so it reads for a general linguistics audience and conforms to the Language Style Sheet / Unified Style…
Automate Langbase tasks via Rube MCP (Composio). Always search tools first for current schemas. — from general/automation
Scaffold a basic LangChain/LangGraph ReAct agent with a tool, wired to any OpenAI-compatible endpoint.
Building LLM agents with LangChain and LangGraph, covering tool-calling model initialization, state management, and observability with LangSmith.
Create and use LangChain agents with createAgent - includes agent loops, ReAct pattern, tool execution, and state management
Guides building LLM applications with LangChain's chain/agent abstractions and, for stateful multi-step agents, LangGraph's graph-based orchestration (nodes, edges, cycles,…
Guide to using ChatPromptTemplate and MessagesPlaceholder in LangChain for conversational AI. Use when building chatbots, conversational interfaces, or AI assistants that need to…
Diagnose and fix common LangChain errors and exceptions. Use when encountering LangChain import errors, auth failures, output parsing issues, agent loops, or version conflicts.
LangChain v0.3 ядро (import langchain). ТОЛЬКО при: create_agent, @tool decorator, init_chat_model, SystemMessage/HumanMessage/AIMessage, middleware before_model/after_model,…
Compose LangChain 1.0 chains with RunnableParallel, RunnableBranch, RunnablePassthrough.assign, and RunnableLambda — correct input/output shapes, debug probes, and typed…
Build LangChain LCEL chains with prompts, parsers, and composition. Use when creating prompt templates, building RunnableSequence pipelines, parallel/branching chains, or…
Build LangChain chains and prompts for structured LLM workflows. Use when creating prompt templates, building LCEL chains, or implementing sequential processing pipelines.
Build LangChain agents with tool calling for autonomous task execution. Use when creating AI agents, implementing tool/function calling, binding tools to models, or building…
Implement LangChain RAG pipelines with document loaders, text splitters, embeddings, and vector stores (Chroma, Pinecone, FAISS).
Build a LangGraph 1.0 Deep Agent — planner + subagents + virtual filesystem + reflection loop — without the state-growth and prompt-inheritance traps.
LangChain AI Agent Master 교안의 파트별 리뷰를 자동 생성하는 스킬. 교안(docs/partXX_*.md)과 예제 코드(src/partXX_*/)를 분석하여 문서 갈무리, 오류/수정 피드백, 개선 제안, 리뷰어 종합 소견을 포함하는 리뷰 마크다운을 생성한다.
Complete guide for LangChain, LangGraph, and Deep Agents development. Covers framework selection, agents, tools, RAG, middleware, graphs, persistence, HITL, subagents, and memory.
Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs.
Implement role-based access control for LangChain applications with multi-tenant isolation, model access control, and usage quotas.
Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0 agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval LLM-as-judge, agent…
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
Add human oversight to LangChain agents using HITL middleware - includes interrupts, approval workflows, edit/reject decisions, and checkpoints
Incident response procedures for LangChain production issues: provider outages, high error rates, latency spikes, and cost overruns.
LangChain v0.3 интеграции (import langchain-openai/langchain-anthropic). ТОЛЬКО при: Chroma/FAISS/Pinecone/Qdrant langchain, OpenAIEmbeddings, ChatOpenAI, ChatAnthropic,…
Build a correct LangGraph 1.0 ReAct agent with `create_react_agent` — typed tools, error propagation, recursion caps, and stop conditions that actually stop.
Build a correct LangGraph 1.0 StateGraph — typed TypedDict state with reducers, nodes, edges, compile, and recursion budgets — without hitting the silent-termination and…
Initialize and use LangChain chat models - includes provider selection (OpenAI, Anthropic, Google), model configuration, and invocation patterns
Configure LangChain across dev/staging/production environments with isolated API keys, environment-specific settings, and secrets.
Work with multimodal inputs/outputs in LangChain - includes images, audio, video, content blocks, and vision capabilities
Set up comprehensive observability for LangChain applications with LangSmith tracing, OpenTelemetry, Prometheus metrics, and alerts.
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration
Wire LangChain 1.0 / LangGraph 1.0 traces into an OpenTelemetry-native backend (Jaeger, Honeycomb, Grafana Tempo, Datadog) with LLM-specific SLOs, safe prompt-content policy, and…
Building LLM applications with LangChain - chains, agents, RAG, memory, and production patterns for AI-powered apps.
Build Retrieval Augmented Generation (RAG) systems with LangChain - includes embeddings, vector stores, retrievers, document loaders, and text splitting
Create Obsidian clipping notes via Obsidian CLI using path pattern `{folder}/YYYY-MM-DD {title}.md`, with optional LLM-selected configured subfolders, auto-extract frontmatter…
Use when building agents with LangChain, LangGraph, or Deep Agents. 21 skills covering ecosystem primer, quickstarts, Deep Agents (memory, orchestration, managed), LangChain…
Stream outputs from LangChain agents and models - includes stream modes, token streaming, progress updates, and real-time feedback
Build production-ready LLM applications with chains, agents, memory, tools, and RAG pipelines using the LangChain framework