Skills
99,448 packaged capabilities and playbooks for agents. Install any of them with one command.
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Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the author's own voice. It never changes a number, result, or citation, and it is not for evading AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.
Writes copy that gets a 'yes' using Joanna Wiebe's research-first, Voice of Customer methodology. Use when: **writing landing pages, emails, or sales pages** that need measurable conversion results; **starting a new copy project** and needing a systematic process to follow; **struggling with what to write** and staring at a blank page; **wanting to prove ROI** to clients with data-backed decisions; **improving existing copy** through validation and testing
Compile an academic Markdown manuscript into a journal- or school-compliant Word (.docx): specified typography, native OMML equations, three-line tables, cross-references, per-section page numbers, CSL bibliographies (GB/T 7714, APA, Nature, IEEE), and pre- and post-export quality gates. Export an academic Markdown manuscript as a Word document compliant with journal or degree thesis requirements: abstract, numbered equations, three-line tables, captions, cross-references, table-of-contents fields, and references. Use when the user wants a submission-ready .docx — thesis-to-Word conversion, thesis formatting, journal submission formatting, prepare submission docx — or a readable copy of a manuscript. Not for PDF, HTML or LaTeX output, slide decks or grant forms.
Add sun shafts and crepuscular rays to a Three.js or equivalent 3D scene, using scene occlusion, a projected sun position, controlled foreground spill, and scalable post-processing. Use for rays of light from the sky, sunlight through trees or roofs, and golden-hour architectural scenes.
Use the internet: search, read, and interact with 13+ platforms including Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu (小红书), Douyin (抖音), WeChat Articles (微信公众号), LinkedIn, Boss直聘, RSS, Exa web search, and any web page. Use when: (1) user asks to search or read any of these platforms, (2) user shares a URL from any supported platform, (3) user asks to search the web, find information online, or research a topic, (4) user asks to post, comment, or interact on supported platforms, (5) user asks to configure or set up a platform channel. Triggers: "搜推特"、"搜小红书"、"看视频"、"搜一下"、"上网搜"、"帮我查"、"全网搜索"、"search twitter"、"read tweet"、"youtube transcript"、"search reddit"、"read this link"、"看这个链接"、"B站"、"bilibili"、"抖音视频"、"微信文章"、"公众号"、"LinkedIn"、"GitHub issue"、"RSS"、"search online"、"web search"、"find information"、"research"、"帮我配"、"configure twitter"、"configure proxy"、"帮我安装".
Converts marketing copy — headlines, ads, homepage claims, pitches, positioning statements — into value-first, vivid language: reframes save-time/save-money pitches as create-more-value pitches in the currency the customer measures value, fits each claim to the right level of the customer's needs (features at your level, the promise one level up, aspirations referenced but never promised, obviated steps bragged about), keeps every claim consistent with the declared pricing strategy (More for More, More for Less, or Less for Less), and forces specificity — no generic words, no clichés, show-don't-tell, real numbers only, claims passing the Opposite Test. Works one message at a time, recording survivors in POSITIONING.md. Sharper with an ideal-customer definition and a needs stack; works without. Load when the user wants marketing copy, positioning, headlines, or a pitch rewritten, sharpened, or audited. Do NOT load to build the needs stack or define the ideal customer — upstream steps this skill consumes.
Use this skill when adding or changing AI chat, text generation, reasoning, tools, model selection, or structured JSON output in a Customware-generated React Router and Hono app. It covers the server-only Customware AI Gateway, AI SDK with the OpenRouter adapter, AI Elements UI, supported model choices, and mandatory live model and structured-output checks.
Generate, remix, extend, edit, and analyze AI music (Mureka). Triggers on: "音乐", "music", "生成音乐", "generate music", "翻唱", "cover", "混音", "remix", "续写", "extend", "纯音乐", "instrumental", "配乐", "soundtrack", "分轨", "stem", "识别歌词", "recognize lyrics", "作曲", "compose", "create a song", "做一首歌"
Design and implement next-best-action engines that surface proactive, prioritized recommendations to advisors based on portfolio, life, market, and compliance events. Use when the user asks about building event-driven advisor alerts, designing trigger logic for portfolio drift or large cash movements, prioritizing competing actions across a book of business, routing NBA recommendations to the right team member, measuring NBA acceptance rates, or automating compliance-driven actions like annual review reminders. Also trigger when users mention 'next best action', 'advisor nudges', 'proactive outreach', 'what should I do for this client', 'event-driven triggers', 'action queue', 'client contact gap', 'RMD reminder', or 'advisor productivity tool'.
Use when a task involves creating or editing a Scenario workflow graph through MCP: building an app from a brief, adding or rewiring nodes (models, prompts, approval gates, loops), authoring editor_info, publishing, unpublishing or renaming, importing an exported workflow JSON, migrating a graph built in Weavy, ComfyUI, or another node tool, copying a workflow, or turning a prompt chain into an app. Running or pricing a workflow is scenario-workflows. Keywords: node graph, editor_info, CEL.
Applies the StoryBrand SB7 Framework from Building a StoryBrand by Donald Miller. Use when writing website copy, crafting brand messaging, creating marketing materials, building sales funnels, or designing email campaigns. The SB7 Framework positions the customer as the hero and the brand as the guide across 7 story elements. Triggers include 'how should we write our website', 'our messaging is confusing', 'customers don't understand what we do', 'how do I write a tagline', 'what should our homepage say', 'nobody reads our emails', 'how do I create a lead generator', 'our marketing isn't working but our product is good'. NOT for growth channel selection (use Traction), not for product positioning against competitors (use Obviously Awesome), not for pricing (use Monetizing Innovation).
Create and edit React Native Storybook stories using Component Story Format (CSF). Use when writing .stories.tsx files, adding stories to React Native components, configuring Storybook addons (controls, actions, backgrounds, notes), setting up argTypes, decorators, parameters, or working with portable stories for testing. Applies to any task involving @storybook/react-native story authoring.
Interrogates a user's decision, plan, pitch, positioning, target market, pricing, or still-forming idea with sharp, unsparing questions — extended when useful to hostile or even unfair framings — until the plan sharpens into defensible decisions, or the user concludes it isn't a good idea after all. Acts as the constructive devil's advocate that the user cannot be for themselves. Load when the user wants to stress-test, attack, pressure-test, find holes in, or play devil's advocate against something they're attached to; when they ask to be \"grilled,\" \"beat up,\" or told \"why I'm wrong\"; when they suspect their plan contains wishful thinking, fluffy language, or an unnamed elephant in the room; or when they need to convert vague aspirations into real strategic decisions (with consequences accepted). Do NOT load for ordinary code review, prose editing, or general feedback requests without an adversarial frame.
Build and deploy applications on inference.sh. Use when getting started, understanding the platform, creating apps, configuring resources, or needing an overview of inference.sh app development. Supports both Python and Node.js. Triggers: inference.sh app, belt app, inf.yml, inference.py, inference.js, deploy app, app development, build app, create app, GPU app, VRAM, app resources, app secrets, app credentials, multi-function app
Review a Data Processing Agreement against your DPA playbook — auto-detects whether you're processor or controller and applies the right half of the playbook. Use when the user says "review this DPA", "check this data processing addendum", "customer sent their DPA", "is this DPA okay", or attaches a DPA.
Use this skill whenever the user is working with Drizzle ORM on PostgreSQL. This covers any mention of Drizzle, drizzle-orm, drizzle-kit, drizzle-zod, `pgTable`, `defineRelations`, `relations()`, `db.select()`, `db.query`, or insert/update/delete against Postgres. Typical intents to trigger on: - Designing or debugging Postgres table schemas, columns, identity PKs, enums, JSONB, arrays - Modeling one-to-many or many-to-many relations and join tables in Drizzle - Writing, optimizing, or preparing Drizzle queries (placeholders, `.prepare()`) - Generating Zod validators from Drizzle tables - Setting up TypeScript + Postgres projects (Neon, Supabase, postgres.js, node-postgres) with Drizzle - Migrating from Prisma, TypeORM, or Sequelize **to Drizzle** - Resolving Drizzle type errors; choosing between v1 RC and 0.45.x APIs Do NOT trigger for Drizzle with MySQL/SQLite, raw SQL without an ORM, or other ORMs when Drizzle is not the target.
Expert Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-processing.
Plan and execute large refactor or rewrite efforts efficiently with parallel multi-agent analysis and implementation. Use when a user asks to refactor many files, split workstreams, analyze a target code area, and coordinate sub-agents with clear ownership and dependency-aware execution.
Use when generating or editing video with Kling models on Scenario via MCP: text-to-video, image-to-video with first and last frames, multi-shot sequences with per-shot prompts, consistent characters via elements and reference images, prompt-based video editing, motion transfer from a driving video (motion control, mocap), lipsync to audio or text, talking avatars, native audio and dialogue, or picking 720p, 1080p, or 4K tiers. Keywords: Kling V3, O1, 2.6, Omni, Kuaishou, T2V, I2V, V2V.
Write dense storyboard-grid prompts for short-form image or previsualization workflows. Use this when a generation task needs a 6-9 panel grid with hook logic, visible actions, product timing, and negative constraints before image or video generation.
MUST be used first for tunnel / proxy-path connectivity failures or slow throughput (网络故障 / 连不上 / 代理或隧道下超时). Diagnoses Tailscale, proxy, TUN, DNS and SSH-tunnel problems on macOS and Windows/WSL, e.g. Tailscale ping works but SSH fails, curl works but browser gets 503, Git/Docker fails behind the proxy, or downloads crawl despite a reachable proxy. Not for application-layer bugs (use debugging-network-issues).
Conducts a rigorous rapid evidence assessment or systematic-lite literature review for MEL/SRHR questions. Use when Ane asks for 'evidence review', 'literature review', 'evidence synthesis', 'REA', 'what does the evidence say', 'what do we know about', or similar. Produces a structured brief with question framing, method, findings by theme, confidence grading, and implications for programme or evaluation design. Does not invent citations.
Creates jaw-dropping, production-ready frontend interfaces AND delivers perfectly matched real photos (Unsplash/Pexels direct links) OR flawless custom image-generation prompts for hero images, backgrounds, and illustrations. Zero AI slop, zero fake URLs.
Turn a window of merged PRs (date, PR-number floor, or last 7 days) into a manual QA scenarios report for human testers — P0/P1/P2 routes with where to click, what to verify, what can go wrong — as markdown + HTML under paths.qa, shipped as a docs PR via om-auto-create-pr. Use for "what should QA test this week".
Refresh Claude Ads platform, API, policy, regulation, benchmark, issue, pull-request, fork, and repository evidence. Use for ads research refresh, expired refresh_due dates, stale API or platform claims, reverify-or-demote decisions, release-current claim validation, ecosystem review, current platform changes, or updating paid-media knowledge.
Automatically generate illustrations based on document content. AI analyzes the document structure, summarizes the core points, and generates illustrations in a specific style for each topic. Supports cover image generation and custom image ratios. Use cases: when a user needs illustrations for a document, article, or note. Keywords: illustration, generate images, document images
Feishu chat browsing, message interaction and group chat management. View chat records (1:1/group/topic group), search groups, get message details, react with Reaction emoji, Pin/unpin, delete messages, and group chat info management (get/update/dissolve/members). Use this skill when the user asks to "view group messages / pull group chat records / export chat records / get group history / view topic replies / search groups / view DM records / dump Feishu messages"; it also covers full thread expansion for topic groups (chat_mode=thread). Read operations (msg history/list/get/mget/thread-messages) use the User Token by default after login and fall back to Bot when not logged in; interactive operations (reaction/pin/search-chats/chat get/update/delete/member) require a User Token; msg delete uses the App Token by default for Bot self-recall and can take an explicit User Token for admin recall; chat create/link uses the Bot identity by default to create groups / get group links.
Create or update a shared Apple design context document that other HIG skills use to tailor guidance. Use when the user says "set up my project context," "what platforms am I targeting," "configure HIG settings," or when starting a new Apple platform project. Also activates when other HIG skills need project context but none exists yet. This skill creates .claude/apple-design-context.md so that hig-foundations, hig-platforms, hig-components-*, hig-inputs, and hig-technologies can provide targeted advice without repetitive questions.
J-Link download and online debugging tool for probing devices, flashing firmware, reading and writing memory, viewing registers, resetting targets, reading RTT/SWO logs, and online debugging (pause/resume/step/breakpoint run/call stack/variable inspection). Automatically triggers when the user mentions J-Link, JLink, RTT, flashing firmware, writing memory, reading memory, register inspection, target reset, probe connectivity check, online debugging, single step, breakpoint, or call stack, and also supports explicit /jlink invocation. Even if the user only says "flash it", "show me the RTT output", or "debug it", this skill should trigger as long as the context involves a J-Link probe.
Assess business quality, competitive positioning, and sustainability of value creation beyond financial models. Use when the user asks about economic moats, competitive advantages, Porter's Five Forces, management quality, ESG integration, or business model analysis. Also trigger when users mention 'does this company have a moat', 'switching costs', 'network effects', 'brand value', 'management track record', 'capital allocation', 'insider ownership', 'red flags', or ask whether a company's advantage is durable.
Use this skill when your backend needs to read or update RevenueCat state on Android. Covers the RevenueCat REST API (v1 subscribers endpoint, grant/revoke entitlements, attributes), secret vs public SDK API key usage, and why you do not build a receipt verification backend with RevenueCat.
Use when a video needs its spoken words on screen through Scenario via MCP: burned-in styled captions for a TikTok, Reels, or Shorts cut, ad captions for sound-off feeds, YouTube subtitles, an SRT sidecar, transcription of a clip's audio, captions translated into another language, karaoke or word-by-word styles, or restyling and correcting an existing transcript. Keywords: captions, subtitles, SRT, transcribe, karaoke, word-by-word, burn in, closed captions, translate video, caption style.
Use when generating or extending video with Google Veo models on Scenario via MCP: text-to-video, image-to-video from a first frame, first and last frame transitions, reference-to-video (R2V) with asset or style reference images, native audio with dialogue, sound effects, and ambience, negative prompts, seeded reruns, extending a 16:9 clip, or choosing a quality, fast, or lite tier. Keywords: Veo 3.1, Veo 3.1 Fast, Veo 3.1 Lite, Extend Video, Google, T2V, I2V, V2V, 720p, 1080p.
strace and ltrace skill for system call and library call tracing. Use when a binary behaves incorrectly without crashing, diagnosing file-not-found errors, permission failures, network issues, or unexpected library calls by tracing syscalls and library function calls. Activates on queries about strace, ltrace, syscall tracing, library interception, ENOENT, EPERM, strace -e, or diagnosing binary behaviour without a debugger.
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.