Agent Skills

by warpdotdev

Fix compilation errors, linting issues, and test failures in the warp Rust codebase. Covers presubmit checks, WASM-specific errors, and running specific tests. Use when the user hits build errors, clippy or fmt failures, test failures, or needs to run or interpret presubmit before a PR.

reproduce-bug-report

by warpdotdev

Launch Oz cloud agents with computer use to reproduce UI-focused bug reports, capture visual evidence, and report reproduction findings. Use when investigating a specific interactive or visual bug from an issue, ticket, support report, or prompt.

write-feature-docs

by warpdotdev

Draft a complete documentation page for a new Warp feature from its PRODUCT.md and/or TECH.md spec. Use when an engineer has written a spec and needs to produce a first-pass MDX draft for the warpdotdev/docs repo. Also handles features without specs by researching the codebase first. Invoke this skill whenever an engineer mentions writing docs for a feature, drafting a docs page, creating feature documentation, starting the eng-docs workflow, or converting a spec into documentation. Requires an interactive session with the engineer present - it confirms a content design plan, then an outline, before drafting, and cannot run unattended. For automated, release-triggered docs, use the missing_docs skill in warpdotdev/docs instead. Works from warp-internal or warp-server.

respond-to-pr-comments-in-blocklist

by warpdotdev

Walk users through PR review comments, fetching and displaying them first when needed, collect per-comment response decisions, apply requested fixes, and preview GitHub replies and resolutions before posting. Use when responding to PR review comments on the current branch.

update-skill

by warpdotdev

Create or update skills by generating, editing, or refining SKILL.md files in this repository. Use when authoring new skills or revising the structure, frontmatter, or guidance for existing ones.

diagnose-ci-failures

by warpdotdev

Diagnose CI failures for a PR using the GitHub CLI, extract error logs, and generate a plan to fix them. Use when the user asks to check CI status, pull CI issues, triage test failures, or investigate PR build failures.

spec-driven-implementation

by warpdotdev

Drive a spec-first workflow for substantial features by writing PRODUCT.md before implementation, writing TECH.md when warranted, and keeping both specs updated as implementation evolves. Use when starting a significant feature, planning agent-driven implementation, or when the user wants product and tech specs checked into source control.

validate-changes-match-specs

by warpdotdev

Validate that a branch or pull request implementation matches introduced product, technical, security, and related specs. Use when reviewing or finishing a spec-driven change and resolving mismatches between checked-in specs and implementation.

check-impl-against-spec

by warpdotdev

Compare a pull request's implementation against spec context in spec_context.md and feed any material mismatches into review.json. Use during PR review when approved or repository spec context is available.

scan-new-specs

by warpdotdev

Scan warpdotdev/warp and warp-server for recently merged PRODUCT.md specs that don't yet have a corresponding docs PR in warpdotdev/docs. When a complete spec is found, auto-generates a full docs draft PR and tags the engineer. When a spec is too thin to draft from, pings the engineer directly. Designed to run as a scheduled Oz ambient agent (e.g., every 2-3 days). Use when setting up the automated docs trigger or running a manual docs coverage sweep.

framework-selection

by langchain-ai

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-middleware

by langchain-ai

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-fundamentals

by langchain-ai

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

langchain-dependencies

by langchain-ai

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.

langgraph-persistence

by langchain-ai

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-human-in-the-loop

by langchain-ai

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.

ecosystem-primer

by langchain-ai

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.

api-docs

by langchain-ai

OpenAPI documentation and REST API design patterns

react-components

by langchain-ai

Modern React component patterns with hooks and TypeScript

langsmith-dataset

by langchain-ai

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.

deep-agents-orchestration

by langchain-ai

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

langchain-oss-primer

by langchain-ai

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.

langsmith-evaluator

by langchain-ai

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-trace

by langchain-ai

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.

deep-agents-core

by langchain-ai

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

langchain-rag

by langchain-ai

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

🔥🔥FreeQuick setup

langgraph-fundamentals

by langchain-ai

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

testing-patterns

by langchain-ai

Unit testing and integration testing best practices

deep-agents-memory

by langchain-ai

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

site-specification

★ 102

by automattic

Extract comprehensive site specifications from simple descriptions. Use when analyzing a user's theme request to determine site type, audience, tone, layout requirements, and typography.