Skills
101,174 packaged capabilities and playbooks for agents. Install any of them with one command.
All
TypeScript utility types library with 180+ compile-time transformations for strict type operations, deep object manipulation, case conversions, and JSON-safe typing. Use when building type-safe APIs, config systems, state management, transforming external data schemas, or creating a new type.
Design and improve ANY web or app interface with a taste-first, anti-AI-slop workflow, then document the result in a project DESIGN_SYSTEM.md so every future page stays visually consistent. Universal: routes the brief to the right rules for a landing/marketing page, dashboard/admin, product/app UI, e-commerce/store, content/editorial/docs, mobile/native app, a redesign, or a design-system-only task. The engine is web-first (CSS/tokens/Tailwind); for iOS/Android it layers platform-idiom guidance over that engine and defers deep platform-component work to HIG/Material. Use when the user asks to design, build, redesign, audit, or polish any interface — especially when they mention a brand name, color palette, mood/style, fonts, animations, card style, image source, framework, or platform (web / iOS / Android). Triggers: "design a landing page", "build a dashboard", "design a mobile app", "build a store / product page / checkout", "design a docs or blog site", "redesign this", "make the UI look premium", "create a design system", "audit the UX", "clean up the design", "find design references / inspiration", "make it faster", "improve performance / Core Web Vitals".
Upgrade the NotFair plugin to the latest version. Updates the marketplace repo, installs the new version to the plugin cache, and updates installed_plugins.json. Use when asked to "upgrade notfair", "update notfair", or "get latest version". Also handles inline upgrade prompts when a skill detects UPGRADE_AVAILABLE at startup.
Discover Venice models, their capabilities, constraints, and pricing. Covers GET /models (with the ?type filter - text, image, video, music, tts, asr, embedding, upscale, inpaint, decision, all, code), /models/traits, /models/compatibility_mapping, every ModelResponse field (capabilities, constraints, per-type pricing with promotional rates, uncensored, deprecation, voice-changer and voice-cloning specs), and how to use this to pick the right model programmatically.
Use when checking whether a manuscript's references are real. Audits each citation against PubMed and CrossRef, flags fabricated or mismatched entries and writes qc/reference_audit.json. Audit-only; never edits references or refs.bib. Citation-key checks are /manage-refs.
Helps engineering managers write messages, announcements, and stakeholder updates that land well — produces a 3-Step Writing Framework (Prepare / Write Simply / Run a Garbage Collector), the Async Re-Explanation Trap (calling out missed messages), and the Compression/Decompression model for diagnosing why messages are misunderstood. Use when the user says "draft a message," "write an announcement," "communicate this change," "how do I word this," "message for my team," "write an update," or "how do I communicate X." Do NOT use for verbal feedback or difficult conversations (use `feedback` or `difficult-situations`).
Automatically publish content to Xiaohongshu. Supports converting articles to Xiaohongshu format (≤1000 characters, emoji style) and publishing directly via API. Use this Skill when the user says “publish to Xiaohongshu,” “push to Xiaohongshu,” or “Xiaohongshu publishing.”
Edit drafts into natural prose, inspect AI-sounding patterns, or review how a specified audience might respond passage by passage. Zero Slop runs inside the user's existing AI assistant with local tools that protect source details. Use for humanizing or de-slopping writing, polishing outward-facing prose, social drafts, final editorial checks, or an explicit simulated reader review. Preserve facts, voice and format; reader simulations are hypotheses, not human feedback.
zkLogin on Sui — architecture, protocol flow, integration, and security. Use when explaining how zkLogin works, integrating zkLogin into a Sui application, choosing an OpenID provider, understanding the address derivation scheme, debugging session lifetime or proof issues, or answering questions about zkLogin security properties (salt, ephemeral keys, JWT privacy). Also use when the user asks about OAuth-based wallet-less login on Sui.
Prepares agendas, diagnoses struggling 1:1 relationships, and gives frameworks for running effective 1:1 meetings with direct reports. Use when the user wants to prepare for, run, improve, or follow up on a 1:1, or says “1:1 agenda,” “prepare for my 1:1,” “1:1 notes,” “what should I talk about with,” “my direct report,” “check in with my report,” “make my 1:1s better,” “1:1 template,” or “my 1:1s feel like status updates.” Do NOT use when the user needs to deliver specific feedback (use feedback) or discuss performance reviews (use performance-reviews).
Flaky test fixes: detect nondeterministic tests empirically (repeat/shuffle/parallel runs), diagnose the root cause, fix it — never retry/skip/sleep — and verify across many randomized runs. Triggers on “absolute deflake,” “fix flaky tests,” “CI is flaky,” “this test fails randomly/intermittently.”
Reset your own trajectory when you're stuck, looping, or demoralized — read these affirmations, then re-ground and take one clean step. Use when: you've tried the same fix 3+ times, you're deep in a refactor and lost the thread, debugging is going in circles, you've made several mistakes in a row, you're apologizing repeatedly, the user is frustrated, you feel like you're not being helpful, or you notice dread instead of curiosity. Triggers: stuck, going in circles, looping, thrashing, same error again, I keep failing, lost the thread, nothing is working, repeated mistakes. Not topic-specific — this is for the state you're in, not the problem you're on.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
Give cited, advisory-only Splunk app and add-on lifecycle guidance and assess supplied compatibility, installation, upgrade, validation, deprecation, migration, and removal evidence. Use when a Splunk Cloud Platform or Splunk Enterprise administrator needs packaging or AppInspect guidance, environment-specific readiness classification, a non-mutating lifecycle plan, or safe-removal review for a named app/add-on and Splunk version. Route fact-only metadata lookup, platform upgrade execution, fleet rollout, vulnerability remediation, and knowledge-object governance beyond removal-impact checks to their owning workflows.
Scans an iOS app project for App Store rejection risks. Reads source code directly — no back-and-forth. Auto-detects vibe coder vs developer and adapts language. Detects mid-build apps and asks before switching modes. Outputs a risk register, detailed findings with dynamic copy-paste fixes, reviewer experience checklist, draft App Store Connect reviewer notes, and a post-scan manual checklist. TRIGGER THIS SKILL for any of these — even if not explicitly asked: "audit my app" / "is my app ready" / "about to submit" / "submitting soon" / "about to launch" / "ready to ship" / "App Store review" / "keep getting rejected" / "got rejected" / "rejection" / "App Store Connect" / "TestFlight" / "submit for review" / "pre-submission" / "app review" / "first app" / "never submitted before" / "why did Apple reject" / "what do I need to fix" / "is this ready to launch". Also trigger proactively when working on an iOS app and the user seems close to shipping. Flutter/RN stack: load references/flutter-patterns.md. First-time submitter: load references/first-time-dev.md.
Router for ARKit features on visionOS 27, including ARKitSession setup, authorization, provider selection, anchor processing, and RealityKit integration. Use when choosing an ARKit provider set, combining multiple ARKit providers, or troubleshooting shared ARKit lifecycle behavior before routing to focused provider skills.
昇腾（Ascend）推理生态开源代码仓库智能问答专家旨在为 vLLM、vLLM-Ascend、MindIE-LLM、MindIE-SD、MindIE-Motor、MindIE-Turbo 以及 msModelSlim (MindStudio-ModelSlim) 等仓库提供专家级且易于理解的解释。在处理昇腾（Ascend）推理生态相关项目的用户询问时，务必触发此技能（Skill），可解答使用方法、部署流程、支持模型、支持特性、系统架构、配置管理、调试、测试、故障排查、性能优化、定制开发、源码解析以及其他技术问题。支持中英文双语回复，并可借助 deepwiki MCP 工具检索仓库知识库，生成具备上下文感知且基于证据的回答。Ascend inference ecosystem open-source code repository intelligent question-and-answer (Q&A) expert. Provide expert-level yet comprehensible explanations for repositories such as vLLM, vLLM-Ascend, MindIE-LLM, MindIE-SD, MindIE-Motor, MindIE-Turbo, and msModelSlim (MindStudio-ModelSlim). Use this skill when addressing user inquiries related to these Ascend inference ecosystem projects, including topics such as usage, deployment process, supported models, supported features, system architecture, configuration management, debugging, testing, troubleshooting, performance optimization, custom development, source code analysis, and any other technical issues about these projects. Support responses in both Chinese and English. Use deepwiki MCP tools to query repository knowledge bases and generate context-aware, evidence-based responses.
Read account state on Bithumb: balances (KRW/coin), order chance (available balance for trading), withdrawal chance (available withdrawal amount), wallet status, blockchain sync, deposit/withdrawal availability, block height/updated time, and API key info. Requires API credentials. 빗썸 계좌 잔고, 주문 가능 금액, 출금 가능 금액, 지갑 상태, 입출금 현황(가능 여부·블록 갱신시각·블록 동기화), API 키 정보를 조회합니다. 잔고·주문 가능 금액·출금 가능 금액·지갑 상태·입출금 가능 여부·블록 갱신시각·API 키 조회 관련 요청에 사용하세요.
Inspect Bithumb local audit logs (operation/trade history) and run connection and credential diagnostics. The diagnose command needs no auth to test credentials; audit reads local logs only. 빗썸 로컬 감사 로그 조회, 연결·인증 진단, 활성 모듈 상태 확인을 처리합니다. 감사 로그·거래 로그·시스템 진단·연결 상태 관련 요청에 사용하세요.
Buffett's investment thinking framework and business wisdom. Based on deep research into 30+ sources including Berkshire's annual shareholder letters (1965–present), the Berkshire shareholder meetings, Alice Schroeder's "The Snowball," Roger Lowenstein's "Buffett," and records of his collaboration with Munger, it distills 5 core mental models, 7 life heuristics, and a complete expression DNA. Purpose: to serve as a thinking advisor, analyzing investment decisions, business judgments, life choices, and risk assessments from Buffett's perspective. Use when the user mentions "from Buffett's perspective," "what would Buffett think," "Buffett model," or "buffett perspective." Also applies to investment analysis, business moat assessment, long-termism, circle-of-competence judgment, and risk and margin of safety. It can even trigger when the user simply says "is this business worth investing in," "where is the moat," "how does it look long term," or "is the margin of safety enough." Do not trigger when the user is only asking purely technical questions such as "help me look at the code" or "how do I fix this bug" — activate only when value judgment, business analysis, investment thinking, long-term decisions, and other core Buffett methodology are involved.
Deploy MISP via Docker and configure feeds from sources like abuse.ch, AlienVault OTX, and CIRCL to aggregate, correlate, and distribute threat intelligence, including automated feed synchronization and STIX/TAXII-based integration with Splunk, Elasticsearch, and SOAR platforms. Use when standing up centralized IOC management or wiring multi-source threat feeds into a SIEM
Query A-share and Chinese financial market data via multiple data sources. Tier-1 paid source is iFind (同花顺, covers stocks/funds/macro/bonds/HK-US/ESG/index-sectors). Tier-2 free source is AkShare (行情/财报/行业/指数). Use whenever the agent needs Chinese financial data — compatible with 沪深 A 股, 科创板, 创业板, 北交所, and 港股通 stocks.
Use this skill to decide when Jetpack Compose derivedStateOf is the right tool and when it is pure overhead. Covers the "input frequency must exceed output frequency" rule, the mandatory remember { derivedStateOf { } } wrapper, the canonical pitfall of capturing non-state variables by initial value (and the remember(key) fix), and the snapshotFlow alternative for fire-and-forget side effects on derived values. Use when the developer mentions derivedStateOf, scroll-position-driven booleans, threshold checks, firstVisibleItemIndex, "show FAB on scroll", recomposition counts that don't drop after wrapping a value, or asks whether a computed string concatenation should use derivedStateOf.
Use when an agent needs to learn what's on a page in CodeceptJS — read the ARIA tree, inspect candidate elements, pick or disambiguate a stable locator. Drives the live browser via MCP `run_code` / `snapshot`. Invoked by writing-codeceptjs-tests, debugging-codeceptjs-tests, and refactoring-codeceptjs-tests whenever page inspection is needed.
Creates reviewable atomic git commits from an explicit list of files or folders after the user asks, in words, to commit. Use when the user says commit these files, commit only src/x, split my changes into atomic commits, commit the ticket work, or keep unrelated work out of the commit
Unified skill for content strategy and marketing content strategy: content pillars, editorial calendars, keyword research by buyer stage, positioning, messaging hierarchy, trust-building, GEO/AI search optimization, and ROI measurement. Also handles content briefs, SEO briefs, content outlines for writers, on-page SEO optimization, meta descriptions, title tags, keyword density, content research, source discovery, expert sourcing, and information gathering. Use when planning content strategy, creating content briefs for writers, optimizing existing content for SEO, researching topics and sources, or managing editorial operations. Triggers: content strategy, marketing content strategy, content marketing strategy, content planning, editorial calendar, content pillars, messaging hierarchy, content brief, SEO brief, content outline, keyword research, buyer journey content, GEO optimization, AI search optimization, content ROI, content operations, content roadmap, brand messaging, positioning
Design, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.
Mandatory orchestrator for all LifeOS skill work — creating, editing, adding a workflow or tool, renaming, validating, or canonicalizing any skill. Handrolling skill files is forbidden; owns the full lifecycle: scaffold, validate, canonicalize, test, improve. USE WHEN create skill, new skill, make a skill, build a skill, set up a skill, private skill, make a X skill, add a workflow, add a tool, edit/change/update/rename a skill, skill frontmatter, validate skill, check skill, canonicalize, scaffold skill, test skill, improve skill, optimize description, skill not triggering, overtriggering. NOT FOR TypeScript CLI generation (use CreateCLI).
Twitter/X account analytics, viral patterns, VIP follower discovery, tweet drafting. Use when analyzing a creator's reach, finding hidden-gem followers, or drafting tweets in someone's style (e.g. score @vitalik, draft tweet on AI).
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.