Skills de Langchain Ai

OpenAPI documentation and REST API design patterns

Search arXiv for preprints and academic papers by topic with abstract retrieval. Query-based search across physics, mathematics, computer science, biology, statistics, and related fields Configurable result limit (default 10 papers) with results sorted by relevance Returns title and abstract for each matching paper Requires the arxiv Python package; install via pip if not already present

Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields

Long-form blog post writing with research delegation, structured content templates, and AI-generated cover images. Delegates research to subagents before writing, storing findings in markdown for reference and context Enforces a five-part post structure: hook, context, main content (3–5 sections), practical application, and conclusion with call-to-action Generates SEO-optimized cover images using detailed prompts covering subject, style, composition, color, and lighting Outputs posts to...

Perform a structured code review of changes, checking for correctness, style, tests, and potential issues.

Read the user's coding preferences from /memory/coding-prefs.md before making non-trivial style decisions, and append new preferences when the user gives…

When asked to analyze competitors:

Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical…

Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality…

Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating…

Database migration patterns and schema versioning

deep-agents-memory-&-filesystem — an installable skill for AI agents, published by langchain-ai/langchain-skills.

Foundation framework for building multi-step agents with built-in planning, memory, and skill delegation. Provides six core middleware options: task planning, filesystem context management, subagent delegation, persistent memory, human approval workflows, and on-demand skill loading Includes three always-present built-in tools: write_todos for task tracking, filesystem operations ( ls , read_file , write_file , edit_file , glob , grep ), and task for spawning specialized subagents Supports...

INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.

Pluggable memory and file backends for Deep Agents with ephemeral, persistent, and hybrid routing options. Four backend types: StateBackend (thread-scoped, ephemeral), StoreBackend (cross-session persistent), FilesystemBackend (real disk access for local dev), and CompositeBackend (route different paths to different backends) FilesystemMiddleware provides six file operation tools: ls , read_file , write_file , edit_file , glob , grep CompositeBackend uses longest-prefix matching to route...

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent),…

Orchestrate subagents, plan multi-step tasks, and require human approval for sensitive operations. Delegate work to specialized subagents via the task tool; custom subagents support isolated tool sets and system prompts, while the default "general-purpose" subagent inherits main agent configuration Plan and track complex workflows with write_todos , organizing tasks across pending, in-progress, and completed states; requires a thread_id for persistence across invocations Implement...

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

Best practices for Docker containerization and multi-stage builds

Create new eval suites for the deepagentsjs monorepo. Handles dataset design, test case scaffolding, scoring logic, vitest configuration, and LangSmith…

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating…

Framework selection guide for LangChain, LangGraph, and Deep Agents layered architecture. Layered frameworks where LangChain provides foundation primitives, LangGraph adds orchestration and control flow, and Deep Agents adds planning, memory, file management, and skill delegation Decision table guides framework choice based on task complexity: LangChain for single-purpose agents, LangGraph for custom control flow and loops, Deep Agents for multi-step planning and persistent sessions Deep...

INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the…

Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user…

ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph open source agent project. It is the required starting point for any LangChain open source agent…

langchain-middleware-&-hitl — an installable skill for AI agents, published by langchain-ai/langchain-skills.

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI),…

langchain-structured-output-&-hitl — an installable skill for AI agents, published by langchain-ai/langchain-skills.

Use this skill for ANY coding question involving LangChain products (LangChain, LangGraph, LangSmith SDK). Covers agent development patterns, primitives,…

LangChain ecosystem package versions, dependencies, and installation guidance for Python and TypeScript. Start all new projects on LangChain 1.0 LTS; version 0.3 is legacy maintenance-only. Always install langchain-core explicitly alongside other packages. Choose one orchestration layer: LangGraph for fine-grained graph control, or Deep Agents for batteries-included planning and memory. Pin langchain-community conservatively to exact minor versions (e.g., >=0.4.0,<0.5.0 ) since it does not...

INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph,…

Build production LangChain agents with create_agent(), tools, and middleware patterns. Use create_agent() with model, tools list, and system prompt; configure state persistence with checkpointer and thread_id for conversation memory across invocations Define tools via @tool decorator (Python) or tool() function (TypeScript) with clear descriptions so agents know when to call them Add middleware like HumanInTheLoopMiddleware for approval workflows, custom error handling, and human-in-the-loop...

Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

Human-in-the-loop approval, custom middleware, and structured output patterns for LangChain agents. HumanInTheLoopMiddleware pauses execution before dangerous tool calls, allowing humans to approve, edit arguments, or reject with feedback Per-tool interrupt policies let you configure different approval rules based on risk level; requires a checkpointer and thread_id for state persistence Command resume pattern continues execution after human decisions, with support for editing tool arguments...

INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of…

ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Required starting point before choosing other skills or writing any…

Complete RAG pipeline for document ingestion, embedding, retrieval, and LLM-powered response generation. Supports multiple document loaders (PDF, web pages, directories) and persistent vector stores (Chroma, FAISS, Pinecone) with configurable chunk size and overlap for optimal context preservation Includes similarity search, MMR (Maximal Marginal Relevance) retrieval, and metadata filtering to balance relevance and diversity in results Works with OpenAI embeddings and integrates seamlessly...

langchain-rag — an installable skill for AI agents, published by langchain-ai/skills-benchmarks.

INVOKE THIS SKILL for LangGraph workflows, parallel execution, interrupts, or streaming. Covers Send API for fan-out, interrupt() for human-in-the-loop,…

langgraph-persistence-&-memory — an installable skill for AI agents, published by langchain-ai/langchain-skills.

Access LangGraph documentation to build stateful agents and multi-agent workflows. Fetches official LangGraph Python docs covering state machines, graph-based agent design, and human-in-the-loop patterns Prioritizes relevant documentation by query type: implementation guides for how-to questions, concept pages for theory, tutorials for end-to-end examples, and API references for technical details Automatically selects 2–4 most relevant documentation URLs and retrieves their content to answer...

Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.

Directed graph framework for building stateful, multi-step agent workflows with fine-grained control. StateGraph with typed state schemas, reducers for accumulating lists/values, and nodes that return partial state updates Static edges for fixed flow, conditional edges for branching, and Command for combining state updates with dynamic routing Send API for fan-out parallelism to worker nodes with result aggregation via reducers Invoke for single execution and stream modes (values, updates,...

INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.

Pause graph execution for human review, approval, or validation, then resume with their input. Requires three components: a checkpointer (InMemorySaver or PostgresSaver), a thread ID in config, and JSON-serializable interrupt payloads interrupt(value) pauses and surfaces data; Command(resume=value) resumes and returns that value to the paused node All code before interrupt() re-executes on resume, so side effects must be idempotent (use upsert, not insert) Supports approval workflows,...

INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...),…

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping.…

Durable graph execution with thread-scoped checkpoints, state history, and cross-thread long-term memory. Three checkpointer options: InMemorySaver for testing, SqliteSaver for local development, PostgresSaver for production; always pass thread_id in config to enable persistence Browse and replay from past checkpoints using get_state_history() , fork execution by updating state at a past point, or manually modify state before resuming Store API provides cross-thread memory for user...

INVOKE THIS SKILL when creating evaluation datasets from trace OR uploading datasets to LangSmith OR querying datasets. Covers dataset types (final_response,…

INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2)…

INVOKE THIS SKILL when working with LangSmith tracing OR querying traces. Covers adding tracing to applications and querying/exporting trace data. Contains…

Creates code-based evaluators for LangSmith-traced agents. Use when building custom evaluation logic, testing tool usage patterns, or scoring agent outputs…

INVOKE THIS SKILL when creating evaluation datasets from trace OR uploading datasets to LangSmith OR querying datasets. Covers dataset types (final_response,…

Create, manage, and upload evaluation datasets to LangSmith for testing and validation. Supports four dataset types: final_response (full conversations), single_step (individual node behavior), trajectory (tool call sequences), and RAG (question/chunks/answer/citations) CLI commands for dataset lifecycle management: create, list, get, delete, export, and upload from local JSON files SDK-based dataset creation in Python and JavaScript with programmatic example addition Example management...

INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response,…

INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2)…

Build evaluation pipelines for LangSmith with LLM-as-Judge and custom code evaluators. Three core components: creating evaluators (LLM-as-Judge or custom code), defining run functions to capture agent outputs and trajectories, and running evaluations locally or auto-running via uploaded evaluators Supports both offline evaluators (comparing run outputs to dataset examples) and online evaluators (real-time quality checks on production runs) Requires LangSmith API key and project...

INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2)…

Fetches LangSmith traces for debugging agent behavior. Use when troubleshooting agent issues, reviewing conversation history, or investigating tool calls.

INVOKE THIS SKILL when working with LangSmith tracing OR querying traces. Covers adding tracing to applications and querying/exporting trace data. Uses the…

INVOKE THIS SKILL when working with LangSmith tracing OR querying traces. Covers adding tracing to applications and querying/exporting trace data. Contains…

Add tracing to LangChain/LangGraph apps and query trace data via CLI for debugging and dataset generation. Automatic tracing for LangChain/LangGraph apps with environment variables; manual tracing via @traceable decorator and wrap_openai() for other frameworks in Python and TypeScript Query traces (complete execution trees) or runs (individual nodes) using langsmith CLI with filters for time, latency, errors, tags, and custom metadata Export traces to JSONL files preserving hierarchy;...

INVOKE THIS SKILL when working with LangSmith tracing OR querying traces. Covers adding tracing to applications and querying/exporting trace data. Uses the…

Break down a coding task into a structured implementation plan with clear steps, file identification, and risk assessment.

Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database,…

Modern React component patterns with hooks and TypeScript

Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback —…

Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about…

Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user…

Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user…

Drafts platform-specific social media posts with research-backed content and generated companion images. Supports LinkedIn posts (1,300 characters with professional tone) and Twitter/X threads (280 characters per tweet with 1/🧵 format) Requires delegating research to a subagent before writing, then reading findings to ensure accuracy and relevance Generates eye-catching social images automatically using generate_social_image tool with bold, high-contrast compositions optimized for small...

Dispatch a batch of tasks to subagents in parallel with bounded concurrency. Returns a summary object with {total, completed, failed, results[]} — iterate…

Unit testing and integration testing best practices

Orchestrates multi-source web research by delegating to subagents, synthesizing findings, and producing cited reports. Breaks research questions into 2–5 distinct subtopics, creates a research plan file, and spawns up to 3 subagents in parallel for efficient investigation Each subagent performs 3–5 web searches per subtopic and writes findings to local files with key facts, quotes, and source URLs Synthesizes results by reading local findings files, integrating insights across subtopics, and...

Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research