Skills
99,951 packaged capabilities and playbooks for agents. Install any of them with one command.
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Run any question, plan, PR, or code through a Diverse Multi-Agent Debate (DMAD) council of 5 AI advisors with distinct reasoning methods. Advisors collaborate, peer-review each other anonymously, and a chairman synthesizes a verdict. Empirically outperforms adversarial debate (M3MADBench 2026, DMAD ICLR 2025). Use when: 'council this', 'run the council', 'council review', 'pressure-test this', 'stress-test this', 'war room this', or when facing a genuine decision with stakes and tradeoffs
Provision and migrate Fusion databases using the fusion-infra-cli (finf). USE FOR: provision a database for a service, run SQL migrations, provision PR-specific ephemeral databases, check database state. DO NOT USE FOR: application code changes, service deployments, role management, or infrastructure other than databases.
Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.
Corporate event opportunity scanner for A-share companies via Longbridge — identifies and analyses events that may create pricing dislocations: M&A / restructuring (asset injection / reverse merger), major shareholder increases / buybacks (positive signal), equity incentive plans (management alignment), index inclusion / exclusion (forced passive flows), and lockup expiry (potential selling pressure). Provides historical statistical patterns and trading window recommendations per event type. Triggers: "捕捉机会", "事件机会", "并购重组机会", "增持机会", "回购信号", "指数调整机会", "解禁压力", "事件套利", "捕捉機會", "事件機會", "並購重組機會", "增持機會", "回購信號", "指數調整機會", "解禁壓力", "event opportunity", "corporate event", "M&A opportunity", "buyback signal", "index inclusion", "lockup expiry", "event catalyst", "special situation", "event-driven".
Pairs trading / statistical-arbitrage strategy via Longbridge Securities — tests cointegration between two correlated assets using the Engle-Granger (ADF) method, computes the optimal hedge ratio via OLS, calculates spread Z-score, half-life of mean reversion, and generates entry/exit signals (long spread when Z > 2, short spread when Z < -2, exit when |Z| < 0.5). Triggers: "配对交易", "统计套利", "协整", "价差交易", "对价交易", "双股套利", "配對交易", "統計套利", "協整", "價差交易", "pairs trading", "statistical arbitrage", "cointegration", "spread trading", "mean reversion pairs", "hedge ratio", "half-life", "ADF test", "Kalman filter", "Z-score spread", "spread mean reversion".
Financial regulatory knowledge base — A-shares (10% / 5% ST price limits, T+1 settlement, short-selling rules, new delisting rules), HK (T+0 short-selling, no price limits, odd-lot / grey market, profit test, insider dealing ordinance), US (PDT rule $25k threshold, Reg T margin, circuit breakers, SEC rules), crypto regulation, cross-border tax basics. Triggers: "监管规则", "涨跌停", "T+1", "融券", "退市", "做空规则", "PDT规则", "熔断", "保证金", "印花税", "監管規則", "漲跌停", "融券", "退市", "做空規則", "PDT規則", "熔斷", "保證金", "印花稅", "regulatory rules", "circuit breaker", "short selling rules", "PDT rule", "margin requirements", "stamp duty", "delisting rules", "trading rules", "settlement rules".
Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfolio", "risk-adjusted return", "asset class allocation", "portfolio optimisation", "mean variance".
Tax-loss harvesting via Longbridge — identify unrealised-loss positions in the account, evaluate tax benefit of realising losses, suggest substitute securities to maintain market exposure (avoiding wash-sale rules), and track the 30-day wash-sale window. Suited for year-end US tax planning. Triggers: "税损收割", "亏损锁定", "wash sale", "税务规划", "节税", "税务优化", "年末税务", "未实现亏损", "稅損收割", "虧損鎖定", "稅務規劃", "節稅", "年末稅務", "未實現虧損", "tax loss harvesting", "wash sale", "tax planning", "realized loss", "unrealized loss", "tax optimization", "year-end tax", "substitute securities".
Tech hype vs. fundamentals analysis via Longbridge — identifies valuation bubbles and fundamental disconnects in A-share / HK tech stocks. Compares PE / PS / EV-EBITDA historical percentile against actual revenue / profit growth. Analyses which AI / EV / semiconductor theme plays have fundamental support vs. pure sentiment-driven momentum. Triggers: "科技炒作", "AI泡沫", "估值泡沫", "科技估值", "概念股", "主题炒作", "基本面背离", "炒作识别", "科技泡沫", "科技炒作", "AI泡沫", "估值泡沫", "科技估值", "概念股", "主題炒作", "基本面背離", "tech hype", "AI bubble", "valuation bubble", "tech valuation", "theme stocks", "hype vs fundamentals", "concept stocks", "narrative vs reality", "AI concept", "semiconductor bubble".
Use when structuring Unity 6.3 C# code: ScriptableObject data vs runtime state, events and services, inventory, health, save and load, Input System actions and key rebinding, async with Awaitable, assembly definitions, Enter Play Mode and domain reload, .meta files, Git, Smart Merge and LFS. Also when 'my save works in the Editor but not in the build', statics keep values on the second Play, events fire twice or never, an await runs after its object died, rebinds are lost on relaunch, a renamed class loses its data, a scene merge breaks.
Using SurrealDB with the Python SDK, covering both client/server mode (WebSocket) and embedded mode (in-memory or file-based). Use when connecting to SurrealDB from Python, using the surrealdb Python package, running SurrealDB embedded without a server, or performing CRUD operations from Python code. Triggers: surrealdb Python, Surreal(), AsyncSurreal(), Python SDK, embedded SurrealDB, mem://, file://.
Best-practice authoring guidance AND a read-only policy gate for AWS Terraform generated by a migration skill. Load during any phase that writes a terraform/ directory — first as the "what to emit" posture rules + security-baseline spec, then after writing as the deterministic policy verdict. Read-only: it reports whether the generated Terraform passes; it never edits .tf files, never touches .phase-status.json, and never decides phase completion. Complements (does not replace) terraform fmt/init/validate
AEM as a Cloud Service Java/OSGi best practices, guardrails, and legacy-to-cloud pattern transformations. Use for Cloud Service–correct bundles, deprecated APIs, schedulers, ResourceChangeListener, replication, Replicator, JCR observation (javax.jcr.observation.EventListener), OSGi Event Admin (org.osgi.service.event.EventHandler), DAM AssetManager, BPA-style fixes, HTL (Sightly) Cloud SDK lint warnings (data-sly-test redundant constant value comparison), or any time you need the detailed pattern reference modules under this skill.
Character rigging in Blender 5.2 — Rigify metarig fitting/generation, bone naming/roll/collections, DEF/MCH/control layers, IK/FK, robust skinning (heat + proxy fallback), weight-painting rules, correctives, engine game skeletons (Unity, Unreal, Roblox R15), rig validation, and deformation stress tests. Use for armature, skinning, or weight tasks
Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
Drives a live multi-turn conversation with a LiveKit agent running locally, using `lk agent debugger`. Use when the user says "test my agent", "try my agent", "does this work", or "why did it call that tool", after editing an agent to check how it behaves, or when an agent on a speech-to-speech model or one that mishears users needs checking. The default for a bare "test"; for regression tests use testing-livekit-agents, for graded runs running-livekit-simulations.
Guides setup, development, and troubleshooting of the Next.js and Enonic XP headless integration (Next.XP framework). Covers Enonic adapter configuration, content type to React component mapping, Guillotine GraphQL data fetching, Content Studio preview mode, and draft/master branch switching. Use when building a Next.js frontend powered by Enonic XP, configuring the Next.XP adapter, mapping content types to components, fetching Enonic content via Guillotine in Next.js, or debugging Content Studio preview. Don't use for traditional server-side Enonic XP rendering, standalone Guillotine queries without Next.js, non-Next.js frontend frameworks with Enonic, or React4XP embedded rendering
Expert patterns for CharacterBody2D including platformer movement (coyote time, jump buffering, variable jump height), top-down movement (8-way, tank controls), collision handling, one-way platforms, and state machines. Use for player characters, NPCs, or enemies. Trigger keywords: CharacterBody2D, move_and_slide, is_on_floor, coyote_time, jump_buffer, velocity, get_slide_collision, one_way_platforms, state_machine.
Perform keyword research using the SemRush API. Use when the user says "find keywords", "keyword research", "what should I rank for", "keyword ideas", "search volume", "keyword difficulty", "topic clusters", "content gaps", or asks about SEO keywords for a topic or niche.
Small-cap growth stock discovery — identifies overlooked small/mid-cap high-growth companies (A-share 专精特新, HK growth stocks, US small-cap growth) by screening for market cap < 5–10B, revenue growth > 30%, ROE > 15%, and low institutional ownership. Triggers: "小盘成长", "小市值", "成长股", "中小盘", "专精特新", "隐形冠军", "小盘股", "成长潜力", "小盤成長", "小市值", "成長股", "中小盤", "專精特新", "隱形冠軍", "small cap growth", "small cap stocks", "growth stocks", "mid-small cap", "hidden champions", "high growth companies", "small cap discovery", "emerging growth", "小市值成长股", "中小市值".
Upgrade React Spectrum v3 (Spectrum 1) codebases to React Spectrum S2. Use when developers mention migrating or upgrading from React Spectrum v3, Spectrum 1, S1, @adobe/react-spectrum, @react-spectrum/* packages, or codemod-assisted upgrades to @react-spectrum/s2.
Explore a large codebase in parallel by launching multiple explore subagents that each investigate a different area simultaneously. Use when onboarding onto a new project, understanding architecture, or investigating a cross-cutting concern.
Professional PDF generation from markdown using Pandoc with Eisvogel template and EB Garamond fonts. Use when converting markdown to PDF, creating white papers, research documents, marketing materials, or technical documentation. Supports both English and Russian documents with professional typography and color-coded themes. Mobile-optimized layout (6x9) by default for Telegram bot context, desktop/print layout (A4) for other contexts.
Use this skill whenever the user wants to find trading opportunities, detect arbitrage, analyze a market, perform edge detection, find mispricing, do probability analysis, evaluate orderbook depth, find momentum signals, or assess Polymarket market quality. Triggers: "find opportunities", "detect arbitrage", "analyze market", "edge detection", "mispricing", "probability analysis", "orderbook analysis", "momentum scanner", "market inefficiency", "price gap", "volume surge", "trading edge", "market analysis".
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
Search across company knowledge bases (Confluence, Jira, internal docs) to find and explain internal concepts, processes, and technical details. When an agent needs to: (1) Find or search for information about systems, terminology, processes, deployment, authentication, infrastructure, architecture, or technical concepts, (2) Search internal documentation, knowledge base, company docs, or our docs, (3) Explain what something is, how it works, or look up information, or (4) Synthesize information from multiple sources. Searches in parallel and provides cited answers.
Optimize SurrealDB performance through record ID and key design, indexing strategy, and computed/derived fields. Use when queries are slow, when designing record IDs for locality and range scans, choosing between standard/unique/full-text/vector indexes, verifying index usage with EXPLAIN, or deciding whether to precompute values with computed fields, views, or events. Triggers: slow query, performance, record id design, range scan, DEFINE INDEX, EXPLAIN, computed field, FUTURE, DEFINE TABLE AS SELECT.
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.