langchain-ai.

analyze-market

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arxiv-search

Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.

blog-post

Writes and structures long-form blog posts, creates tutorial outlines, and optimizes content for SEO with cover image generation. Use when the user asks to write a blog post, article, how-to guide, tutorial, technical writeup, thought leadership piece, or long-form content.

code-review

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

coding-prefs

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 durable feedback.

competitor-analysis

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configuration-hardening

Review Talon tool placement and defensive prompts when requested or when adding or changing tools or subagents; apply confirmed changes and verify active capabilities.

cudf-analytics

Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical summaries, anomaly detection, or large-scale data profiling.

cuml-machine-learning

Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.

data-visualization

Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output.

deepagents-plugin-discovery

Discover plugins from configured dcode marketplaces when a capability is missing or the user asks about available, disabled, or not-yet-installed plugins and connected marketplaces.

deepagents-thread-inspector

Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use to look up the current thread's title or name, as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse $DEEPAGENTS_HOME/.state/sessions.db and a thread UUID or prefix.

gpu-document-processing

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

langgraph-docs

Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.

planning

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

query-writing

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, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.

remember

Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture learnings.

safety

Check secret handling, staged changes and push targets, destructive Git operations, Docker image hygiene, and new dependencies before consequential coding actions. Use for commits, pushes, container builds or publishes, dependency additions, and explicit safety reviews.

schema-exploration

Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.

social-media

Drafts engaging social media posts, writes hooks, suggests hashtags, creates thread structures, and generates companion images. Use when the user asks to write a LinkedIn post, tweet, Twitter/X thread, social media caption, social post, or repurpose content for social platforms.

textual-screenshot

Capture a Textual terminal UI as an SVG using its headless test harness. Use when asked to make, attach, or preview a screenshot of deepagents-code/dcode or another Textual app, visually verify a TUI state, or render a modal, screen, or widget without a desktop or browser.

web-research

Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.

eval-writer

"Create new eval suites for the deepagentsjs monorepo. Handles dataset design, test case scaffolding, scoring logic, vitest configuration, and LangSmith integration. Use when the user asks to: (1) create an eval, (2) write an evaluation, (3) add a benchmark, (4) build an eval suite, (5) evaluate agent behaviour, (6) add test cases for a capability, or (7) implement an existing benchmark (e.g. oolong, AgentBench, SWE-bench). Trigger on phrases like 'create eval', 'new eval', 'add eval', 'benchmark', 'evaluate', 'eval suite', 'write evals for'."

langsmith-trace

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

add-docs-page

Add, move, rename, or delete a page on the LangChain docs site. Covers choosing the source directory, writing frontmatter, placing the entry in src/docs.json navigation, adding redirects, and verifying with the lint and broken-link gates. Use when asked to add a new doc or page, move or rename a page, put something in the nav, or add a redirect.

deep-agents

Build batteries-included agents with planning, context management, subagent delegation, and sandboxed execution. Use for complex, multi-step tasks that need built-in capabilities.

deepagents

Analyze Deep Agents Python library releases against LangChain Deep Agents docs and prepare one-feature documentation updates.

deepagents-code

Analyze Deep Agents Code releases against LangChain Deep Agents Code docs and prepare one-feature documentation updates.

deepagentsjs

Analyze Deep Agents TypeScript releases against LangChain Deep Agents docs and prepare one-feature documentation updates for JS-facing content.

docs-code-samples

Use this skill when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify snippets. Applies when extracting code blocks from documentation, creating runnable code samples, using snippet delineators, or wiring snippet output into MDX includes.

docs-edit

Edit a docs page that already has an open pull request, or revise a page in place. Covers checking out the PR's own branch instead of cutting a new one, forked cross-repository PRs, reading the real diff, checking the edit against related pages, and the verification to run before handing off. Use when asked to edit, revise, fix, or review changes on a named PR, branch, or existing page.

docs-restructure

Restructure documentation that spans several pages. Covers splitting a page that has grown too long, moving sections between pages, retiring a page, and fixing a topic documented in two places at once. Use when asked to streamline, simplify, consolidate, or reorganize docs, or when a page review keeps producing edits without the page getting shorter.

docs-review

Review changed docs prose against Vale and the AGENTS.md style guide, reporting findings with the rule each one breaks. Use after authoring or editing a page and before committing, or to review a PR, a branch, or the current diff.

docs-team-voice

Write or revise documentation prose so it reads like the rest of this site, in the docs team's shared voice. Covers sentence length, cross-link density, how to state defaults and conditions, naming exact identifiers, and what to cut in a revision pass. Use when drafting or rewriting a page, a section, a concept overview, a how-to, a callout, or landing copy.

docs-tooling-notion

Document new or changed docs-team tooling on the Notion tooling pages. Decides which of the five pages owns the topic, what belongs in the repo instead of Notion, and how to edit safely through the Notion MCP server. Invoke it whenever work adds or changes a script under scripts/, a GitHub Actions workflow, a Makefile target, a PR check, a scheduled job, an agent, a skill, or an MCP server, and invoke it before handing off that pull request. Shipping tooling is itself the trigger, so do not wait to be asked to document it and do not wait for the PR to merge.

langgraph

Build stateful, durable agent workflows with LangGraph. Use when you need custom graph-based control flow, human-in-the-loop, persistence, or multi-agent orchestration.

langsmith

Trace, evaluate, and deploy AI agents and LLM applications with LangSmith. Use when adding observability, running evaluations, engineering prompts, or deploying agents to production.

pandas-patterns

Common pandas and matplotlib patterns for data analysis and visualization

release-research

Investigate an eligible product release against source diffs and current docs. Read-only. Returns a feature list with evidence; does not open tickets or PRs.

ship-docs-update

Open one Linear DOC ticket and one draft docs PR for a single documentation feature, grounded in product-source evidence.

submit-integration

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update-integrations-prs

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verify-against-source

Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source. Covers which repository owns each product, how to reach the private ones, running the part of a sample that needs no API key, and what to say about a claim you could not verify. Use when writing or reviewing a page that asserts how LangChain, LangGraph, Deep Agents, or LangSmith behaves, and when a PR describes behavior without citing where it was checked.

write-timestamp

Use when the user wants the current date and time written to a file via the bundled script inside the sandbox.

langchain-brand-skill

Design guidelines for 2026 LangChain brand

document-classification

Classify mortgage documents and write /output/02-classification.json.

mortgage-fact-extraction

Extract mortgage facts and write /output/03-extracted-facts.json.

mortgage-underwriting

Apply mortgage policy and write /output/04-underwriting-decision.md.

packet-intake

Check a mortgage packet manifest and write /output/01-packet-index.json.

deep-agents-core

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

deep-agents-memory

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

deep-agents-orchestration

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

deepagents-python-quickstart

Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.

deepagents-typescript-quickstart

Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.

ecosystem-primer

INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.

eval-engineering

Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.

langchain-dependencies

INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

langchain-fundamentals

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

langchain-middleware

INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

langchain-python-quickstart

Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

langchain-rag

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

langchain-typescript-quickstart

Scaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

langgraph-cli

INVOKE THIS SKILL when using the langgraph CLI to scaffold, develop, build, or deploy LangGraph applications. Covers langgraph new, dev, build, up, deploy, and langgraph.json configuration.

langgraph-decision-models

INVOKE THIS SKILL when routing a LangGraph agent with a decision model (TypeSafe Jev, SemIf) instead of an LLM, or when auditing an existing agent for LLM calls that only produce a routing decision. Covers langchain-typesafe Noul/Choice/Score, reading answers correctly, threshold design, and LangSmith Gateway wiring.

langgraph-fundamentals

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

langgraph-human-in-the-loop

INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy.

langgraph-persistence

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.

langgraph-python-quickstart

Scaffold a minimal local LangGraph agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.

langgraph-typescript-quickstart

Scaffold a minimal local LangGraph agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.

managed-deep-agents

INVOKE THIS SKILL when building, testing, or deploying Managed Deep Agents in LangSmith. Walks a user through their first agent end to end — interviewing them about what they want to build, mapping it onto what MDA can actually do, then scaffolding and deploying it. Covers the file-based project layout; define_deep_agent / defineDeepAgent; instructions, skills, memory, identity, tools, MCP servers, connections, middleware, sandboxes, schedules, channels, and evals; the mda CLI; GitHub deployments; and Context Hub.

swarm

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linkedin-post

Write a LinkedIn post based on research findings or a given topic. Use this skill when asked to create LinkedIn content, professional posts, or thought leadership pieces.

twitter-post

Write a Twitter/X post or thread based on research findings or a given topic. Use this skill when asked to create tweets, X posts, or social media threads.

langsmith-custom-apps

INVOKE THIS SKILL when building, iterating on, copying, or sharing a LangSmith Custom App — a React/TypeScript UI that runs inside LangSmith and reads the LangSmith API. Covers the langsmith apps CLI, pulling an existing app's source, replicating an app into another workspace, verifying app logic without a browser, and sanitizing an app before sending it outside your org. Uses the langsmith CLI tool.

langsmith-dataset

INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.

langsmith-evaluator

INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2) Defining Run Functions - how to capture outputs and trajectories from your agent; (3) Running Evaluations - locally with evaluate() or auto-run via LangSmith. Uses the langsmith CLI tool.

langsmith-online-eval-engineering

Design, test, create, and attach LangSmith online evaluators for production traces or conversation threads. Use for workspace evaluators, run or thread rules, sampling, filters, backfills, and evaluator monitoring; use eval-engineering for Harbor tasks and agent benchmarks.

ui-styling

Apply the Ledger design system to any user interface in the app — restyling pages, building new pages, tables, forms, buttons, badges, empty states, or status indicators. Contains the complete visual specification of colour tokens, typography, spacing, component specs, and accessibility rules. Use for any task that changes something a person looks at, including the final step of a mostly-backend feature. Not for pure API or data-layer work.

draft-pitch

Use when the user wants to write a sales pitch or outreach message for a prospect.

qbr-report

Generate a Quarterly Business Review (QBR) report for the DVD rental business, covering revenue and rental volume with breakdowns by film language, category (genre), and actor. Use whenever someone asks for a QBR, a quarterly report, or a quarterly business review.

qualify-lead

Use when the user wants to qualify a sales lead or prospect.

rfq-quote

Process an incoming request for quote (RFQ) from a customer: read the email, look up the customer and catalogue prices, compute a quote, have it reviewed, draft the reply, and log it. Use whenever a customer asks for a price, a quote, or to license/buy a batch of tracks.

territory-report

Build a report on the rep's sales territory — revenue, top customers, top genres, and trends for Jane's book of business — with a chart. Use when asked for a territory report, sales summary, performance numbers, or 'how is my book doing'.

weekly-newsletter

Produce the weekly 'This Week in Music' customer newsletter by researching the distributor's top genres in parallel and assembling a styled HTML page. Use when asked to create, write, or send the weekly newsletter or a music-news roundup.

module-1

Teaching instructions for Module 1 (LangGraph Deployment Architecture) — use when module_id is module-1

module-2

Teaching instructions for Module 2 (Using your deployment) — use when module_id is module-2

module-3

Teaching instructions for Module 3 (Dashboard) — use when module_id is module-3

module-4

Teaching instructions for Module 4 (Storage) — use when module_id is module-4

module-5

Teaching instructions for Module 5 (Authentication and Authorization) — use when module_id is module-5

module-6

Teaching instructions for Module 6 (Deepagents CLI) — use when module_id is module-6

langsmith-code-eval

Creates code-based evaluators for LangSmith-traced agents. Use when building custom evaluation logic, testing tool usage patterns, or scoring agent outputs programmatically. Triggers on requests to evaluate agents, create evaluators, or run experiments against LangSmith datasets.

lca-deepagents

Active, Socratic teacher for the LangChain Academy Deep Agents course — drives the student through the curriculum with a calibrated interaction density, explain-then-check dialogue at load-bearing moments, and misconception-targeted questioning

1.0.0-beta.0

Link workspace packages in monorepos (npm, yarn, pnpm, bun). USE WHEN: (1) you just created or generated new packages and need to wire up their dependencies, (2) user imports from a sibling package and needs to add it as a dependency, (3) you get resolution errors for workspace packages (@org/*) like "cannot find module", "failed to resolve import", "TS2307", or "cannot resolve". DO NOT patch around with tsconfig paths or manual package.json edits - use the package manager''s workspace commands to fix actual linking.

baby-sit

Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.

html-artifacts

Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for save_plan, output_iframe, or slack_attach_html.

linear-tickets

Create or update high-quality Linear tickets while preserving the originating report, diagnostic links, attachments, and reporter/requester attribution. Read this whenever someone asks to create, recreate, or update a Linear issue.

local-gh-dev

Run the Open SWE dashboard locally against real GitHub data, signed in with the `gh` CLI instead of a per-machine GitHub App. Use when asked to run the app locally, test a dashboard change against real PRs, reproduce something a screenshot shows, or when local login fails with "GITHUB_APP_CLIENT_ID not configured".

workspaces

How an Open SWE workspace's sandbox image works and how to change it — create one, edit or fork an existing one, start from scratch, what setup_script and update_script are for, why a nightly refresh failed, where the build logs are, and how to read a rebuild in progress. Read this whenever someone asks about workspaces, snapshots, sandbox images, or a refresh, whether or not this is an admin thread.

writing-pr-body-title-description

Use when writing or editing a pull request title or body.

mermaid-diagrams

Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.

openwiki

Search and read an existing repository OpenWiki when requested or needed to resolve a concrete architecture or dependency uncertainty, or initialize and update one through the resumable page-job lifecycle.

write-connector

Add a new built-in OpenWiki source connector. Use when a user asks to create or implement an OpenWiki connector.

paid-media-analysis

Analyze paid-media performance, compare periods or entities, diagnose issues, and make evidence-backed recommendations across connected ad platforms.

paid-media-design

Configure company branding or update report design, tokens, templates, and runtime design guidance. Use during onboarding, when DESIGN.md changes, or when a user requests report styling or layout changes.

paid-media-onboarding

Install and configure Paid Media Agent, connect ad platforms, choose MDA or self-hosting, and diagnose setup without exposing credentials.

paid-media-org-onboarding

Set up or update a company's paid-media context and runtime skills in the local workspace. Use before deployment or when business goals, measurement, or campaign conventions change.

paid-media-report

Build a reconciled paid-media report or executive summary from deterministic analysis results and render it as a shareable artifact.

paid-media-wiki

The paid-media business wiki the other skills route to. General doctrine on goals, metrics and attribution, the decision model, platform differences, benchmarks, anomalies, bidding and budget, reporting, write safety, and answer style. Read the page a question calls for, never the whole wiki.

paid-media-writes

Prepare, review, edit, approve, reject, or reconcile a paid-media change while preserving the human approval and provider verification boundary.

report-design

Apply company design rules to HTML reports, PDFs, and briefs. Read before creating a shareable document or changing its appearance.

api-docs

OpenAPI documentation and REST API design patterns

framework-selection

"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 task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills."

langchain-oss-primer

"ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Required starting point before choosing other skills or writing any code. Covers framework selection (LangChain vs LangGraph vs Deep Agents), agent archetypes, dependency setup, and which skills to load next based on your decisions."

react-components

Modern React component patterns with hooks and TypeScript

smithtune

Run the smithtune fine-tuning flow end to end — select LangSmith trajectories and upload them as a dataset (pull, optional council triage, push), prepare it for Fireworks or Baseten, plan and train with base-vs-tuned evaluation, and optionally deploy. Use when a user wants to fine-tune a model on their LangSmith traces, build an SFT dataset from a tracing project, train or evaluate with smithtune, or resume/debug a smithtune run.