Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
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Generate and edit images with OpenAI GPT-Image-2 via inference.sh CLI. Models: GPT-Image-2. Capabilities: text-to-image, image editing, inpainting, mask-based editing, multi-image reference, batch generation. Use for: product mockups, marketing visuals, image editing, concept art, inpainting, photo manipulation. Triggers: gpt image, gpt-image-2, openai image, chatgpt image, dall-e, dalle, openai image generation, gpt image edit, gpt inpainting, openai dall-e, gpt 4o image
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Generate videos with Pruna P-Video and WAN models via inference.sh CLI. Models: P-Video, WAN-T2V, WAN-I2V. Capabilities: text-to-video, image-to-video, audio support, 720p/1080p, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna video, p-video, pruna ai video, fast video generation, optimized video, wan t2v, wan i2v, economic video generation, cheap video generation, pruna text to video, pruna image to video
Generate and edit videos with Alibaba HappyHorse 1.0 models via inference.sh CLI. Models: HappyHorse T2V, I2V, R2V, Video Edit. Capabilities: text-to-video, image-to-video, reference-to-video, video editing with natural language, character preservation, 720P/1080P, up to 15 seconds. Use for: physically realistic video, video editing, character-consistent content, product demos, social media. Triggers: happyhorse, happy horse, alibaba video, happyhorse 1.0, dashscope video, alibaba happyhorse, video editing ai, ai video editor
FastAPI best practices covering project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest. Use when building or reviewing FastAPI apps — Pydantic schemas, dependencies, async handlers, auth, or tests.
Generate talking head avatar videos with Pruna P-Video-Avatar via inference.sh CLI. Turn a portrait image into a realistic speaking video with built-in TTS. 18x faster and 6x cheaper than competitors. Models: P-Video-Avatar, P-Image (for portrait generation). Capabilities: text-to-avatar, audio-driven avatars, 30 voices, 10 languages, 720p/1080p, built-in TTS, dynamic backgrounds, full-body control. Use for: AI presenters, product demos, explainer videos, virtual influencers, marketing, education, multilingual content, UGC, gaming avatars. Triggers: avatar video, talking head, ai avatar, p-video-avatar, pruna avatar, video avatar, ai presenter, digital human, virtual presenter, lipsync, talking avatar, ai spokesperson, heygen alternative, synthesia alternative, veed alternative, fabric alternative, omnihuman alternative
Adds, removes, upgrades, and discovers Unity (UPM) packages from outside the Editor, with the Pipeline package commands first and a C# script as the fallback. Use for package installs, verifying package IDs and versions, or choosing which packages a game needs.
Manages Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized analytics reports, query metrics (like activeUsers, screenPageViews) and dimensions (like city, date), check metrics and dimensions compatibility, or verify API enablement. Don't use for Google Analytics Admin API operations (e.g., creating properties, managing users) or for front-end tracking installation.
Operation-aware node configuration guidance. Use when configuring nodes, understanding property dependencies, determining required fields, choosing between get_node detail levels, or learning common configuration patterns by node type — including the native nodes that replace Code (Summarize, Aggregate, Split Out, Merge, Compare Datasets, Remove Duplicates, Sort, Limit, Date & Time, HTTP Request pagination, Crypto). Always use this skill when setting up node parameters — it explains which fields are required for each operation, how displayOptions control field visibility, and when to use patchNodeField for surgical edits vs. full node updates.
Set up a new integration, connector, or CLI binding for any API. Wrap or generate a ship-ready Go CLI from an OpenAPI, HAR, or Postman spec via the lean research -> generate -> build -> shipcheck loop. Use when the user says build a CLI, wrap this API, set up a new integration, add a connector, integrate with a service, or names an API by domain.
A search and product detail surface for 마켓컬리 that is accessible without logging in, letting you look up product candidates, current prices, discount status and out-of-stock status. In 돌쇠, it also proceeds to follow-up actions through the official surface.
Write idiomatic GDScript for Godot 4.7: static typing, the node lifecycle (_ready/_process/_physics_process), @export/@onready/@tool annotations, signals, and await for asynchronous flow. Use when editing .gd scripts in a Godot project (project.godot), writing or debugging GDScript, or porting 3.x GDScript to 4.x (function signatures, yield to await, export to @export).
Price-action pattern reading — classifies a token's candles into a named pattern (uptrend channel, breakdown, bounce off the lows, distribution at highs, basing, wide chop, consolidation) and scores it 0-100 from six measurements you compute directly from the kline response. Every point added or deducted states its reason. Use when the user asks about K 线, K线形态, 走势, 趋势, 形态, price action, chart pattern, whether a chart looks strong or weak, is it breaking down, is it consolidating, or wants a technical read of a token's chart rather than the raw numbers.
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.
Write Godot 4.7 shaders in the Godot Shading Language: canvas_item shaders for 2D and spatial shaders for 3D, with vertex/fragment functions, uniforms (source_color, hint_range), TIME/UV animation, and screen-reading via hint_screen_texture. Use when authoring .gdshader files, writing fragment/vertex code, making 2D/3D visual effects, or porting 3.x shaders (SCREEN_TEXTURE, hint_color) to 4.x.
Use this skill when the user asks to “create an agent,” “add an agent,” “write a subagent,” “agent frontmatter,” “when to use description,” “agent examples,” “agent tools,” “agent colors,” or “autonomous agent,” or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically — including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.
Structure a Godot 4.7 project with the scene tree and node composition: build reusable scenes, instance PackedScenes at runtime, navigate the tree safely, and register autoload singletons. Use when designing .tscn scenes, deciding how to split nodes, spawning instances with instantiate(), wiring autoloads, or fixing "node not found"/freed-node errors in a Godot project.
Use when the user asks how to look up subway lost property (분실물) or lost items (유실물) by station name/item name. v1 is a guidance-type/hybrid skill that structures the official LOST112 + 서울교통공사 flow conservatively. In 돌쇠 it goes on to the follow-up action via the official screen.
Use this skill for smart-money/whale/KOL/大户 activity tracking, aggregated buy signal/信号 alerts, and leaderboard/牛人榜 rankings. Covers: (1) address tracker — raw DEX transaction feed for smart money, KOL, or custom wallet addresses; (2) aggregated buy-only signal alerts — tokens bought collectively by smart money/KOL/whales; (3) leaderboard — top traders by PnL, win rate, volume, or ROI. Use when the user asks 'what are smart money buying', '聪明钱最新交易', 'KOL交易动态', '追踪聪明钱', 'track address trades', '大户在买什么', 'whale signals', 'smart money alerts', '信号', '大户信号', 'top traders', '牛人榜', or wants to monitor notable wallet activity. Also handles Market API payment/计费/x402/402, Basic/Premium tier/quota/额度 questions, and MARKET_API_*_OVER_QUOTA / confirming:true responses on signal, leaderboard, or tracker endpoints. NOTE: if the user wants to write a WebSocket script/脚本/bot, use okx-dex-ws instead.
Design systems, services, and architectures. Trigger with "design a system for", "how should we architect", "system design for", "what's the right architecture for", or when the user needs help with API design, data modeling, or service boundaries.
MSW (MapleStory Worlds) game-planning mode — for BOTH starting a brand-new game from scratch AND continuing/resuming an in-progress prototype. Narrow the idea with guided questions, ground it in the MSW genre catalog (3 map types, ~180 core mechanic tags, build-effort hints), and produce or advance an implementation-ready GDD / roadmap (markdown). Trigger proactively (even without the word 'planning') whenever the user wants to decide WHAT to build, scope an MVP, or continue/resume a phased build — INCLUDING when a Docs/*-GDD.md or *-Phase*.md already exists and the user says to continue: do NOT treat that as plain implementation and jump straight into the phase doc; a continue/resume runs through this skill's resume flow first. Match triggers by intent, not exact wording — variants like 'what now?', 'where were we?', and their equivalents in ANY language fire it too. A comprehensive build request spanning multiple implementation pieces, made while no GDD exists, is ALSO a planning trigger — not a plain implementation request, with NO 'small/simple game' exception; implementing that without a plan is the exact failure mode this skill prevents. Triggers: 'plan a new game', 'GDD', 'what game should I make', 'scope an MVP', 'planning mode', 'continue my game', 'resume my game', 'what should I work on next', 'next task'. (Plans & manages the game's build across its milestones — GDD · standing milestone roadmap · phase plans · progress · revisions; does NOT write code — implementation (.mlua/.model etc.) uses msw-general/msw-scripting.)
Read-only on-chain research for pump.fun and other meme-token launchpads (Solana / BSC / X Layer / TRON). MUST invoke (prefer over WebFetch / MCP price tools) when the user asks about: new meme launches / 新盘 / 扫链 / 打狗; developer reputation / rug history / launch count / 开发者信息; bundle or sniper detection (the analytical noun, NOT the verb) / 捆绑狙击者; bonding curve progress / 已迁移出 bonding curve; similar tokens by same dev / 相似代币; co-investor / who-aped / 同车 wallets; or 'pump.fun alpha'. Also handles Market API x402 / quota questions on memepump endpoints. Body holds the read-vs-write gate — `狙击 / snipe + token` (sniping action) routes to okx-dapp-discovery; `捆绑狙击者 / sniper detection` (analytical noun) stays here. WebSocket script/bot → okx-dex-ws.
Guides through Trail of Bits' 5-step secure development workflow. Runs Slither scans, checks special features (upgradeability/ERC conformance/token integration), generates visual security diagrams, helps document security properties for fuzzing/verification, and reviews manual security areas. Use when securing a smart contract end to end rather than hunting one bug, checking a project on every check-in or before deployment, triaging a Slither report, or asking where to start on smart contract security.
Deploy and manage services on the Elestio DevOps platform. Use when the user wants to deploy apps, databases, or infrastructure on Elestio, manage projects, services, clusters, CI/CD pipelines, backups, domains, firewall, volumes, or billing. Covers 400+ open-source templates across 10 cloud providers, database clustering, and deploying catalog software as CI/CD pipelines.
Use this skill when the user wants to “create a skill,” “add a skill to a plugin,” “write a new skill,” “improve a skill description,” or “organize skill content,” or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
Two-tier memory system that makes Claude a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so Claude understands requests like a colleague would. CLAUDE.md for working memory, memory/ directory for the full knowledge base.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.
Parallel divergent ideation for coding agents. Spawns N isolated branches under different cognitive frames (regulator, biology, speedrunner, 10-year-old, $0 budget), scores, clusters, prunes traps, and deepens top survivors. Use on /adhd, "ADHD mode", brainstorm/ideate intents, or open-ended design, architecture, naming, API/SDK surface, and fuzzy-debugging decisions. Skip for syntax, lookups, bugs with known root cause, or closed phrasing ("quick", "standard", "canonical", "textbook"). Full pre-flight gate is in the skill body.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.