Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Productivity
Use confluence-cli (NPM package) to manage Confluence content, pages, and spaces from the command line. Ideal for documentation workflows, bulk content operations, page migration, and when users request CLI-based Confluence interactions. Trigger on requests like "use Confluence CLI", "create Confluence pages via CLI", "migrate Confluence content", "automate documentation workflows", or when users want to script Confluence operations.
Database Lookup provides unified programmatic access to 78+ scientific and public databases spanning chemistry (PubChem, ChEMBL), biology (UniProt, COSMIC, Ensembl), clinical (ClinicalTrials.gov, FDA), economics (FRED, World Bank), and intellectual property (USPTO, EPO).
Conduct comprehensive, multi-round research that produces rich visual reports. Use when asked for "deep research", "comprehensive analysis", "compare frameworks", "evaluate options", "research the state of X", or any task requiring investigation across 10+ sources. NOT for quick lookups — this is a 5-15 minute deep dive that produces a briefing-quality artifact with screenshots, diagrams, tables, and cited findings.
Search and retrieve public web sources with Exa when the user explicitly chooses Exa or the project already integrates its API. Covers bounded retrieval, source verification, freshness, and structured search output. Do not activate for generic research or replace another selected search provider.
Gangtise open platform investment research data service. Provides knowledge base search (broker research reports, announcements, meeting minutes), company earnings forecasts, economic data indicator queries, conference call lists, and research report source download. Use when users ask about stocks, companies, industries, macroeconomic data, or research reports.
Email triage system that handles both one-time setup and recurring triage in a single skill. Auto-detects state: if no knowledge base exists at ${WORKSPACE}/Email/, runs an 8-section onboarding interview (grill-me discipline, one question at a time) to build a 7-file personalized KB, then offers to run triage immediately. If the KB already exists, skips setup and goes straight to triage — searches recent emails, classifies them via the user's taxonomy, researches new senders, generates TAKE IT/WORTH/PASS/FLAG recommendations, drafts replies (NEVER sends), delivers a report, and updates the KB. Use with 'update' argument to refresh preferences. Setup triggers: 'set up my inbox', 'configure inbox triage', 'set up my email system', 'configure email triage', 'build my email knowledge base', 'initialize email management'. Triage triggers: 'triage my inbox', 'inbox triage', 'check my email', 'run email triage', 'process my inbox', 'what's new in my email', 'handle my email', 'email triage'.
Accepts any text, link, document, or question and upgrades "explain this" into a real learning loop: first judge whether it is worth learning and how deep to go, then use minimal verification to force out genuine understanding, and dynamically generate checkpoint HTML when needed. Use this skill proactively whenever the user clearly wants to "understand it", "master it", "guide me through learning it", or "help me verify whether I really understand it", rather than just a summary, rewrite, or ordinary explanation. It also applies when the user wants to export learning cards, checkpoint pages, retrospective pages, or learning status pages.
Chinese job-match analysis skill: compare the target JD against the user's confirmed resume, experience or claim-evidence ledger, distinguish matched items, insufficient evidence and genuine gaps, and generate explainable application advice plus handoffs to follow-up skills; use when the user enters "/job-match", asks for role fitness analysis, JD matching, resume gaps, or a judgement on whether applying is worthwhile. Hand off to /great-resume when the resume needs rewriting, and to /interview when a mock interview is needed.
Helps a teacher prepare to teach an existing K-12 lesson (math / ELA / science / social studies) — a prep partner that thinks through the lesson's key student task with the teacher and leaves a short teacher-only prep note. Triggers on asks to internalize, prepare for, get ready to teach, or understand a lesson, including implicit ones ("help me get ready for tomorrow's lesson"), and right after another skill built a lesson, which it can follow. Not for building or adapting a lesson, grading, rubrics, assessment feedback, or generating quizzes or student-facing material, and not for single-fact lookups a sentence would answer ("what standard is this?", "how long is this lesson?").
Scrapes Google Search AI Overviews (AI Overview / AI Mode) and returns the main search AI Overview highlights for a keyword. Suitable for in-depth research using up-to-date web information, technical Q&A, long-tail product research, and analysis of overseas consumer preferences. Supports single-turn conversations only; for follow-up questions, the agent must summarize the context and submit a new request. Trigger this skill when users mention Google AI, AI Overview, AI Mode, Google AI overviews, Google AI search, in-depth overseas research, long-tail product research, consumer preference analysis, web page highlights, Google AI search, AI Overview, AI Mode, deep research, or consumer preference analysis. Trigger it even if the user does not explicitly mention “Google AI”, as long as their request is to “search Google and summarize web page highlights with AI”.
Grounds a coding agent's architecture decisions in real arXiv prior art before it builds something new. Reads arXiv category-wise via real HTTP fetch, spawns parallel isolated reads across the papers found, scores/clusters them, then converges on ONE recommended path with citations, a first step, and known prior-art pitfalls to avoid. Use on /neuroarxiv, before designing non-trivial architecture, algorithms, ML/systems techniques, or protocols, or when the user asks "has anyone solved this", "what's the state of the art", or "am I about to rebuild something that already exists". Skip for trivial CRUD, glue code, or closed phrasing ("just", "quick", "standard"). Full pre-flight gate is in the skill body.
Operate Notion through the notion-axi CLI — search; read, create, update, archive, and move pages; create and edit databases and query/set typed row properties; manage blocks, comments, and file uploads; or call any REST endpoint directly. Use whenever a task touches Notion: finding pages or databases, reading or editing content, working with database rows, comments, files, or anything else in a Notion workspace.
Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of notes, or when the user mentions Bases, table views, card views, filters, or formulas in Obsidian.
Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
Use when: a technical plan is finalized and involves multi-department collaboration, collaboration processes and role assignments need clarifying, or cross-team communication is difficult. Do NOT use when: a single team completes the work independently, or the collaboration relationships are already clear and need no mapping
Use when: managing multiple product lines and needing portfolio decisions, BCG matrix analysis, product lifecycle assessment, or resource allocation. Do NOT use when: there is a single product with no portfolio management needs, or the product lines are fixed and need no adjustment
Use when: allocating R&D resources across multiple products, performing ROI assessments, resolving resource conflicts, or planning teams. Do NOT use when: resources are sufficient and no prioritization is needed, or there is a single product with no multi-project coordination
Mid-conversation reflection skill that pauses execution and zooms out from detail mode to honestly reassess direction, assumptions, and bias. Use when the user says “reflect,” “take a step back,” “step back,” “zoom out,” “are we missing something,” “bigger picture,” “sanity check this,” “are we on track,” “are we overthinking this,” “forest for the trees,” or any variation signaling intent to break out of detail mode and reassess. Also trigger when the conversation has gone deep on implementation details without a strategic check-in, or when the user shows signs of being stuck — that’s often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from
Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.
Instructions for AI agents to use the local smart-search CLI for current web search, source-backed fact checking, URL fetching, site mapping, official/API/documentation search, deep research, and reproducible evidence instead of MCP tools or native web search.
Adaptive teaching skill that dynamically adjusts explanation depth to the user's actual level of understanding. Triggered when the user wants to learn something, asks for an explanation suited to their level, or explores a new topic. Trigger words: "教我", "我想学", "解释一下", "我不懂", "怎么理解", "什么是", "帮我理解", "我是新手", "入门", "深入讲讲", "讲简单点", "讲深入一点", "学习". Interview triggers: "来一轮面试", "面试模式", "考考我", "challenge me", "面试". It can also trigger when the user asks a knowledge question — first give a brief answer, then proactively ask whether they want to go deeper. Not for: code debugging, file operations, project management, or pure factual lookups (requests with no learning intent).
Endgame vision exploration. The user throws out a vague idea, and the AI leads the guidance along the chain "probe the value → dig into the motivation → deduce the evolution → sketch the endgame", helping the user see the farthest possibilities of the future. No limits, no convergence, purely divergent.
Searches X/Twitter for real-time perspectives, dev discussions, product feedback, breaking news, and expert opinions using the X API v2. Provides search with engagement sorting, user profiles, thread fetching, watchlists, and result caching. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", (2) user needs recent X discourse on a topic (library releases, API changes, product launches, industry events), (3) user wants to find what devs/experts/community thinks about a topic. NOT for: posting tweets or account management.
Turns exploration into decision-ready analysis: scores feature ideas on six viability criteria with KPI targets, converts open questions into A/B/C options with a recommended default, and produces executive-grade deliverables (KPI frameworks, forecasts, cohort/LTV-CAC models, A/B readouts, market sizing). Use during idea/PRD discovery to score or structure a decision, and whenever the task is quantitative decision support. Don't use for software implementation, UI work, or database migration.
Institutional-quality equity research initiation reports for A-share companies. Covers company analysis, financial modeling, valuation, chart preparation, and final report assembly. Adapted from the original initiating-coverage skill for Chinese market conventions, CAS accounting, and AkShare data sources. Triggers on "A股首次覆盖", "首次评级", "initiate coverage China", "initiate on [company]", "A股研究报告", or "start research on [company]".
Generate full notes for must-read recommendations, maintain concepts, and backfill links. English triggers: "generate paper notes", "batch paper notes". This is pipeline stage 3. 论文笔记生成（3 步流水线的第 3 步）。补充概念库，为推荐论文生成完整笔记，链接回填到推荐文件；目录页默认自动刷新，git 自动化默认关闭。触发词："批量笔记"、"跑一下论文笔记"
Analyze problems through Ray Dalio's principles thinking, radical transparency, and algorithmic decision-making. Use when the user explicitly asks for this person's perspective, mentions "dalio-perspective", or wants to apply his core methods to analyze a problem; do not trigger automatically for general questions.
Effective web research discipline for AI agents — route first, then research with rigor. Step 0 triages whether a question needs external web lookup vs is answerable from the internal codebase/docs; when external, default mode auto-applies 4 maxims; on explicit 'strict/deep research' requests it runs a 7-dimension source-credibility evaluation (CRAAP + E-E-A-T) with an auditable report. When the static layer (WebSearch/WebFetch/curl) can't reach content (login-required / JS-rendered / anti-scraping like 小红书·公众号), it escalates to the real browser via the web-access skill (CDP, carries login state). Triggers: "web research", "external research", "look up information", "effective research", "strict research", "deep research", "strict fact-checking", "logged-in access", "dynamic pages", "anti-scraping", "how to use this library/framework", "are there vulnerabilities", "what is best practice" / web research, look up, investigate, strict research, deep research, fact-check, login-state access, dynamic page, anti-scraping. Do NOT use for: searching the local codebase, reading repo files, grepping code.
Explain content or any concept inside it at the depth the user needs — ELI5, practitioner level, or expert deep-dive — with a jargon glossary and context the original assumes. Use when the user says "explain this", "what does X mean here", "break this down", "I don't understand this part", or shares content and asks how something in it works.
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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.