Skills
100,344 packaged capabilities and playbooks for agents. Install any of them with one command.
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This Skill summarizes common TypeScript issues and their solutions in Lynx development, mainly covering environment configuration, type extending, event handling, components, and ReactLynx advanced usages. Trigger Scenarios: - User inputs TypeScript error messages related to Lynx and seeks fix suggestions - LSP diagnoses Lynx-related TypeScript errors, proactively invoke query to get fix solutions - User asks about TypeScript best practices or common errors related to Lynx, proactively invoke query to provide guidance - User requests to configure the TypeScript environment of the current project to support Lynx development, proactively invoke query to provide configuration steps
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions "psychology," "mental models," "cognitive bias," "persuasion," "behavioral science," "why people buy," "decision-making," or "consumer behavior." This skill provides 70+ mental models organized for marketing application.
End-to-end conference talk pipeline: paper → slide outline → Beamer + PPTX → per-page polish → assurance checks (claim / citation / anonymity) → final export and report. Default-good for academic conference talks (NeurIPS / ICML / ICLR / VALSE / 投稿 talks). Trigger phrases: "做 talk", "做 PPT 全流程", "talk pipeline", "end-to-end slides", "做演讲", "conference talk full workflow". Use when the user wants the complete talk artifact, not just a slide deck.
Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear, features are complex (>2 days), or involve cross-team coordination. Ask two core questions - Why? (YAGNI check) and Simpler? (KISS check) - to ensure clarity before coding.
Per-page Codex review + targeted python-pptx / Beamer fixes for academic talk slides. Use AFTER /paper-slides (or any externally generated PPTX/Beamer) when the deck looks 'mostly OK' but the user wants a final pass that aligns visual weight with a reference, bumps PPTX fonts to projector-readable size, kills italic style leaks, fixes text-frame overflow, and catches per-slide layout drift. Trigger phrases: "polish slides", "slides 排版不对", "PPTX 字体太小", "和 Beamer 比一下", "per-page review", "和 codex 一页一页过".
Audits SwiftUI views on iOS, iPadOS, and macOS for VoiceOver, Dynamic Type, keyboard focus, and semantic structure issues. Use when reviewing or fixing SwiftUI accessibility — returns P0/P1/P2 findings with patch-ready fixes and manual verification steps.
When the user wants to build or improve a sales bot's ability to adapt formality based on how the prospect communicates. Also use when the user mentions "matching tone," "adapting formality," "mirroring communication style," "voice adaptation," or "matching prospect style."
An end-to-end workflow that automatically generates a complete whiteboard animation video from an SRT subtitle file. It proceeds through three stages: storyboard analysis, image generation, and video generation. Trigger when the user provides an SRT file and asks to generate a whiteboard animation video, or says “generate a whiteboard video from subtitles” or “whiteboard video workflow.”
Runtime iOS app security testing with Objection (Frida): inspect Keychain and filesystem data, explore app internals at runtime, and validate or bypass client-side protections during authorized mobile assessments.
Security-focused code review checklist and automated scanning patterns. Use when reviewing pull requests for security issues, auditing authentication/authorization code, checking for OWASP Top 10 vulnerabilities, or validating input sanitization. Covers SQL injection prevention, XSS protection, CSRF tokens, authentication flow review, secrets detection, dependency vulnerability scanning, and secure coding patterns for Python (FastAPI) and React. Does NOT cover deployment security (use docker-best-practices) or incident handling (use incident-response).
Emit and maintain a dagr run file — a live, contract-valid JSON description of recursive projects, tasks, attempts, gates, evidence, policies, events, and operator-message resolutions that `dagr view` renders as a DAG. Use when orchestrating agents or tracking multi-step work that a dagr pane should display.
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
Design the types, function signatures, and module layout first, agree on that structure, then implement against it. Use when work is non-trivial and coding straight away could lock in the wrong structure, or for /ds-blueprint, “design this first,” or “architect this.”
Design, build, and maintain comprehensive knowledge bases. Bridges document-based (RAG) and entity-based (graph) knowledge systems. Use when building knowledge-intensive applications, managing organizational knowledge, or creating intelligent information systems.
ARK-style single-stock disruptive-innovation diagnostic. Suitability gate on 4 dimensions (platform fit / innovation revenue / R&D intensity / management vision); if it passes, builds TAM (low/base/high), Wright's-Law cost curve with sourced learning rate, three-scenario 5-year target (Bull/Base/Bear, 15% discount), risks, conditional action frame. Data: Longbridge CLI first, MCP fallback, WebSearch only for TAM / learning rates / industry runway. Runs cross-statement reconciliation BEFORE analysis. Closes with a data-source appendix whose final row is the reconciliation summary. Independent implementation — not affiliated with ARK Invest. Triggers: "木头姐", "ARK", "ARKK", "颠覆式创新", "莱特定律", "TAM", "5年目标价", "情景分析", "AI 与大数据", "自动化与机器人", "能源存储", "基因革命", "区块链与金融科技", "木頭姐", "顛覆式創新", "萊特定律", "5年目標價", "Cathie Wood", "ARK Invest", "disruptive innovation", "Wright's Law", "learning rate", "bull base bear", "scenario analysis".
Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.
When the user wants to optimize their sales process, improve deal flow, reduce friction in the buying journey, or streamline qualification steps. Also use when the user mentions "sales process," "deal flow," "sales funnel optimization," "buyer friction," "sales cycle reduction," "process efficiency," "deal progression," or "conversion optimization." For building playbooks, see sales-playbook-scaling. For CRM documentation, see deal-documentation.
Rules and guidelines for working with Spring Data JDBC in the project. ALWAYS use this skill when adding, removing, or modifying Spring Data JDBC entities (@Table from org.springframework.data.relational.core.mapping), aggregates, AggregateReference links, embedded objects, @MappedCollection associations, or Spring Data JDBC repositories (CrudRepository / ListCrudRepository). Trigger on any request that touches @Table, @Column, @MappedCollection, @Embedded annotations from spring-data-relational, AggregateReference fields, @PersistenceCreator constructors, or @Query methods on JDBC repositories. Do NOT use for JPA (jakarta.persistence) entities — use the spring-data-jpa skill instead.
Optimize Remotion Studio render performance by minimizing frame-reactive component surfaces, preferring imperative timeline state reads, and memoizing stable render boundaries. Use when code reads the current frame with `useTimelinePosition()` or components unnecessarily render on every frame.
Guide for querying DeFi yield and APY data using get_yield_pools. Covers pool filtering by token, chain, protocol, category, stablecoin-only mode, and capacity assessment. Explains APY conventions, lending vs borrowing rates, and sort options. Use when users ask about yields, APY, lending rates, borrowing costs, best pools, or DeFi yield strategies
This skill should be used when a system design needs a diagram — "draw the architecture", "diagram this system", "show the components", "make an architecture/infrastructure/topology diagram", or visualizing boxes-and-arrows, data flow, regions, or failure paths for a design. It generates a self-contained dark-theme HTML + SVG diagram (with PNG/PDF export). Use it whenever the `system-design` orchestrator or a building block reaches the "draw it" step, even if the user doesn't say "diagram".
Use when the user wants to build or improve a sales bot’s ability to reference previous interactions within and across channels. Also use when the user mentions “remembering conversations,” “context persistence,” “conversation history,” “cross-session memory,” or “past interactions.”
When the user wants to generate referrals from customers, build partner sales channels, or leverage relationships for new business. Also use when the user mentions "referrals," "customer introductions," "partner sales," "channel sales," "word of mouth," "warm introductions," "reference selling," or "network selling." For account expansion, see account-expansion. For prospecting, see outbound-prospecting.
Sync to dondon: Read recent chats from local Claude Code, Codex, WorkBuddy, and Grok; redact sensitive information locally, write a work update of no more than 800 Chinese characters, and find documents used in the chats. After the user confirms, send it to dondon bot (if sending is not configured, copy it for the user to paste) so its morning briefing and reminders reflect the user's current work. Daily automatic syncing is available with the user's consent. Use when the user says “sync to dondon,” “send my update to the bot,” “start/stop automatic sync,” or similar. Chat transcripts, keys, and passwords are not uploaded.
Use when creating, writing, fixing, or reviewing tests in a Flutter project. Covers unit tests, widget tests, integration tests, Riverpod provider testing, and Mockito mocking. Provides Given-When-Then patterns, layer isolation strategies, and test setup for GetIt, SharedPreferences, and FakeDatabase.
Document frontend data needs for backend developers. Use when frontend needs to communicate API requirements to backend, or user says 'backend requirements', 'what data do I need', 'API requirements', or is describing data needs for a UI.
When the user wants to build or improve a sales bot's ability to detect prospect language and respond appropriately or route to the right team. Also use when the user mentions "language detection," "multilingual," "non-English," "translation," "language routing," or "international prospects."
Academic paper search powered by OpenAlex -- the free, open catalog of 240M+ scholarly works. Use when the user needs to find academic papers, research articles, literature for a topic, citation data, author publications, or any scholarly source. Triggers on: 'find papers on', 'academic research about', 'what studies exist', 'literature review', 'find citations', 'scholarly articles about', 'who published on', 'papers by [author]', 'highly cited papers on', any request for peer-reviewed or academic sources. Also use during deep research when you need to ground findings in academic literature. Do NOT use for general web searches -- use web-search for that.
Use when doing dev-stage self-review on the current branch before pushing or opening a PR — runs an auto-loop of codex review (cross-model, OpenAI) + per-finding fix + re-review until findings converge or stop conditions fire. Codex follows pr-review's multi-role methodology (security / staff-engineer / sdet / spec-auditor). Triggers — 'self review', 'self-review', '自己 review', '自我 review', 'cross-model review', 'pre-push review', 'review and fix my branch'. NOT for live PR review with sticky/inline comments (use pr-review), NOT for managed PR babysitting (use pr-babysit), NOT for first-time review without intent to fix (use mode=review-only opt-in).
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.