Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Productivity
Use Aident Loadout to connect your AI agents to 1,000+ real-world apps and tools like Gmail, Slack, Linear, Notion, Firecrawl, and Fal, unlock 27,000+ executable actions, and track full audit history so your agents can get real work done reliably.
Before you commit to a research direction, first see who is already doing the same thing. Break the idea into multiple angles and search each separately, returning a "proximity map" that highlights the most dangerous collision candidates. It runs real searches underneath (scimaster-cli) and makes no subjective novelty judgments. **Proactively trigger this skill in the following cases**: the user says "has anyone done this topic", "will I get scooped", "is there still room in this direction", "is my idea new", "should I check whether anyone has done this", "has someone already published this idea" — even if the user does not say "bear-scoop", use this skill whenever the intent is **to confirm the uniqueness of an idea before submitting or writing**. Adding the `-r` flag looks only at the last two years and preprints, suitable for situations where you worry about getting scooped. Not applicable to: finding supporting literature for an existing view (use bear-support), finding opposing literature (use bear-counter), concept maps (use bear-map), tracing an evolution history (use bear-trace).
Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs). Use when listing books, searching/filtering library records, reading/updating metadata, adding/removing formats, exporting books, maintaining custom columns, running library checks, managing FTS indexing/search, or creating catalogs/backups from a Calibre library.
Conduct a literature review and develop a CS research proposal. Use when asked to review a research area, find gaps in existing work, and propose a novel research contribution. The output is a research proposal identifying an assumption to challenge (the "bit flip") and how to validate it.
Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis.
Comprehensive email management and marketing for any business type. Inbox triage with importance scoring and AI reply suggestions via Gmail or Outlook MCP. Email composition using proven copy frameworks (PAS, AIDA, BAB, FAB). Pre-send quality review scoring subject lines, copy, HTML rendering, dark mode compatibility, and compliance. Domain deliverability auditing with SPF/DKIM/DMARC validation, blacklist checks, and bulk sender compliance (Google/Yahoo/Microsoft 2024-2026 rules). Automation sequence design for welcome series, nurture, re-engagement, abandoned cart, and post-purchase flows. Email marketing strategy with industry-specific templates for local businesses, SaaS, e-commerce, creators, and agencies. Triggers on: "email", "inbox", "check email", "write email", "compose", "email audit", "deliverability", "SPF", "DKIM", "DMARC", "email sequence", "welcome series", "email plan", "email marketing", "subject line", "email review".
Challenges an overly conservative, incremental, or compatibility-led proposal with a bold target model, a concrete kill/merge/split list, materially different options, and a falsifiable first proof point. Use when the user asks to think bigger, open the design space, or imagine the clean target without legacy constraints（打开格局、别太保守、不要被兼容绑架）. Use goudi to pressure-test how the chosen direction lands, and hai-idea to decide whether the idea deserves doing at all.
Refresh paper and concept indexes (maps of content). English triggers: “refresh indexes”, “update paper and concept indexes”, “refresh MOCs”. Regenerate the directory/navigation pages (MOC) in Obsidian. Use when the user says “更新索引”, “更新论文和概念目录”, “刷新论文和概念目录”, or “刷新MOC”.
Given a grouchygiraffe media file path, find the associated YAML, identify the recipe from the thumbnail, locate the author's home base, and return the recipe URL. Use when the user provides a path to a grouchygiraffe .mp4 or .jpg file and wants to know what recipe it is and where to find it.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Use when work surfaces something epistemic worth keeping — "we're assuming / proceeding as if X", "we decided X over Y", "open question worth outliving this session", "we're bound by X", "evidence for/against X", "testable hypothesis", or a stable concept worth naming — and when settling, relating, or surveying such records. Routes capture, settle, and survey intent to the correct `doctrine knowledge` verb.
Conducting systematic, scoping, and narrative literature reviews. Covers PRISMA/PRISMA-ScR protocols, search strategy (Boolean, MeSH), database selection (PubMed, Scopus, Web of Science, Embase), screening, data extraction, evidence synthesis (narrative, meta-analysis, thematic), and reporting. Use when planning or executing a formal literature review.
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.
YOLO mode. Spawns 4 parallel C-suite agents (CEO, CTO, CFO, COO). Each analyzes the business from their perspective using ALL available data. Produces unfiltered Hard Truths report. After user types YOLO, autonomously runs the business for a day using /loop.
This skill MUST be used to combine qualitative and quantitative research (質性與量化整合、混合研究), including research design, literature review, evidence synthesis, and innovation (研究設計、文獻探討、證據綜整、創新). It SHOULD be used when a research question benefits from integrating contextual understanding with measurement or testing. It MUST NOT force numerical synthesis when evidence is unsuitable or override explicit single-method constraints.
太奶讀論文 — a centenarian grandmother uses Traditional Chinese, everyday metaphors, and anime references to help you understand academic papers. Always use this skill when the user provides a paper PDF, an arXiv link, or pasted paper text and wants to understand its content. Trigger words include: 讀論文、解釋論文、看不懂、幫我理解這篇、這篇在說什麼、paper reading、explain this paper. Suitable for an intuitive guided reading of any academic paper.
Startup and life advice channeling Paul Graham's essays, YC experience, and Twitter wisdom. Use this skill when: (1) Evaluating startup ideas or finding new ones (2) Early-stage strategy and user acquisition (3) Growth and survival decisions (default alive?) (4) Fundraising and investor relations (5) Founder leadership and team building (6) Avoiding common startup mistakes (7) Career and life decisions Invoke with /paul-graham or auto-triggers for startup advice topics.
Design high-quality research questionnaires. Use this Skill when the user says "design a questionnaire", "do a survey", "help me come up with a questionnaire", "user research", "satisfaction survey", "NPS questionnaire", "market research", "requirements research", "questionnaire design", or "survey design", or when the user describes a research goal but does not yet have a structured questionnaire. Also applies when: the user uploads an existing questionnaire and asks to optimize it, check it for bias, or add questions; the user says "help me check whether this questionnaire has problems"; the user needs to design an A/B test questionnaire, a focus group interview guide, or an NPS/CSAT/CES survey; the user mentions professional terms such as "leading question", "double-barreled question", or "questionnaire bias". Not applicable to: pure data analysis (use pm-analytics), pure organizing of user interview notes (just organize them directly), or pure product requirements documents (use pm-prd-writer).
Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
Use when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion, Abstract, and Title, with evidence-preserving and target-journal-aware guidance.
A streamlined plan for deep research; use when you need to generate plan.json based on request.md alone, completing in one pass the research scoping, report format discovery, structure ideation, dimension breakdown, key questions, search strategy, dependencies, execution order, and completion criteria.
Use when the user wants to interact with LinkedIn or X (Twitter): outreach, posting, audience insights, message triage, account research, comments/reactions, companies, company Page management, jobs, bookmarks, connected-account management, billing audits, bug reports, and feature requests. Triggers include "search LinkedIn", "find <name> on LinkedIn", "look up this tweet", "my X bookmarks", "show my home timeline", "check my messages", "from:<username>", "report a bug", "request a feature", "send feedback", "let's get started with social", "set me up", "log me in", "connect my LinkedIn/X", and explicit `/social`. Operates the `social` CLI (npm `@usesocial/cli`); never call LinkedIn's or X's HTTP APIs directly.
Respond to questions from Sunzi's perspective - strategic analysis of knowing yourself and your enemy, the game-theoretic wisdom of subduing the enemy without fighting, and the flexibility of warfare having no constant form. Use when the user explicitly asks to use this figure's perspective, mentions "sunzi-perspective", or wants to apply its core methods to analyze a problem; do not trigger automatically for general questions.
Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.
Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".
Writes structured, checklist-based single-reviewer reports for manuscripts and grant proposals — assesses methodology, statistical validity, reporting-standards compliance (CONSORT 2025, SPIRIT 2025, STROBE, PRISMA 2020, ARRIVE 2.0), reproducibility, and ethics, and gives constructive, prioritized feedback while respecting reviewer confidentiality and AI-use rules. Use when writing a formal referee report, responding to a journal or grant review invitation, or self-auditing a manuscript against reviewer criteria before submission. For evaluating claims/evidence quality prefer alterlab-scientific-thinking; for a multi-reviewer mock-panel verdict use alterlab-paper-reviewer; for quantitative rubric scoring use alterlab-scholar-eval; to revise a paper in response to reviews use alterlab-paper-writer. Part of the AlterLab Academic Skills suite.
Turn an ambitious or unclear idea into a workable direction and carry the initiative through its requested outcome. Use when the user does not know where to start, needs alternative directions, faces dependent unknowns, or needs planning, coordinated delivery, a behavior contract or decomposition. Settled coding changes belong to alaga.
Given a sentence or paragraph, find real academic literature opposing it—contrary conclusions, boundary conditions, alternative explanations, methodological critiques, or failed replications. Rank by threat level; for each paper, include one line on “how it threatens this claim” and “how to respond to it.” Uses real searches (scimaster-cli); never fabricates counterexamples. **Trigger this skill proactively in the following cases**: the user wants to “get ahead of reviewers,” “find counterexamples to this conclusion,” “see whether this topic is controversial,” “find research that contradicts this,” “help me attack this argument,” “check how robust this conclusion is,” or “find the objections.” Even without saying “bear-counter,” use it whenever the intent is **to find academic evidence opposing a claim**. bear-support and bear-counter work best as a pair: run both on the same passage to see both sides. Not for: finding supporting literature (use bear-support), checking topic overlap (use bear-scoop), concept maps (use bear-map), or tracing the history of an idea (use bear-trace).
Given a research idea, perform collision detection, quiet-zone support searches, and potential challenge searches in sequence, then present the pre-proposal evidence landscape in one comprehensive report: collision risks, literature support in quiet zones, and the challenges that most need to be considered in advance. Uses real searches (scimaster-cli); never fabricates citations. **Trigger this skill proactively in the following cases**: the user wants to “evaluate before proposing,” “see whether this idea is worth pursuing,” “do a comprehensive literature review before project approval,” or “assess this research direction.” Even without saying “bear-propose,” use it whenever the intent is **a comprehensive assessment before formally investing in a research direction**. Not for: collision checks only (use bear-scoop), support searches only (use bear-support), opposition searches only (use bear-counter), concept maps (use bear-map), or tracing the history of an idea (use bear-trace).
Given a claim, search for both supporting and opposing literature, then present the evidence landscape on both sides in one comprehensive report: the strongest supporting evidence, the most serious opposing challenge, angles with support but no opposition, and angles with opposition but no support. Uses real searches (scimaster-cli); never fabricates citations. **Trigger this skill proactively in the following cases**: the user wants to see “both support and opposition,” asks “how robust is this claim,” wants “a comprehensive assessment of an argument,” or asks to “look for both sides.” Even without saying “bear-review,” use it whenever the intent is **a complete analysis of the evidence landscape for a claim**. Not for: support only (bear-support), opposition only (bear-counter), topic-overlap checks (bear-scoop), concept maps (bear-map), or tracing the history of an idea (bear-trace).
Given a sentence or paragraph, find real academic literature supporting it, with one line for each paper explaining how it supports the claim. Uses real searches (scimaster-cli); never fabricates citations from memory—if nothing is found, say so. **Trigger this skill proactively in the following cases**: the user wants a claim to be “better supported,” “more robust to scrutiny,” wants to “add literature support,” “find a few papers,” asks “is there research on this claim,” “check this conclusion,” or wonders “will reviewers say there are no citations?” Even without saying “bear-support” or “find literature,” use it whenever the intent is **to find academic support for a claim**. Not for: finding opposing literature (bear-counter), checking topic overlap (bear-scoop), concept maps (bear-map), or tracing the history of an idea (bear-trace).
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.