Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Check the live status of the Strait of Hormuz from the Hormuz Strait Monitor dashboard: open, restricted, or closed status, tanker transits vs normal, stranded vessels, Brent price impact, war-risk insurance levels, cargo throughput, tanker freight rates, diplomatic developments, and global trade impact. Use this skill whenever the user asks about the Strait of Hormuz or the Persian Gulf in the context of oil, shipping, or geopolitical risk — whether Hormuz is open, chokepoint or transit disruption, Gulf tanker traffic, war-risk premiums, Middle East shipping routes, or energy supply-chain risk.
Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog
Use when the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions “A/B test,” “product page optimization,” “test my screenshots,” “test my icon,” “conversion rate optimization,” “CPP,” or “custom product pages.” For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.
Amazon Redshift is NOT PostgreSQL; corrects LLM mistakes; covers SQL, metadata, connectivity/auth, storage, query performance, and operations. Applies ONLY to Redshift (provisioned or Serverless). Pushes back on: CREATE INDEX, string_agg, pg_catalog, text type, SERIAL, stl_query. Triggers on: Redshift SQL, Redshift CREATE TABLE, Redshift COPY/UNLOAD, slow or suddenly slow Redshift query, EXPLAIN plan, queue wait, stale statistics/ANALYZE, result cache, Redshift permission denied, Redshift disk full, storage growth, snapshot/backup cost, ghost rows/bloat, VACUUM, spill to disk, distribution skew, Redshift system views, QUALIFY, PIVOT, MERGE, materialized views, Redshift Data API (Python/boto3), Redshift workload management (WLM), concurrency scaling, Redshift resize, Spectrum, awsdatacatalog, connection failures (SSL/TLS, ODBC/JDBC, connection limit), IAM/SAML federation, IAM Identity Center, datashares. Does NOT apply to: S3 policies, Athena, Glue/Iceberg outside Redshift, Aurora, RDS, DynamoDB.
Decompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership
Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.
Surfaces social-layer signals for crypto markets. Three capability groups: news (latest aggregated crypto news feed, filter articles by coin symbol, run full-text keyword searches, fetch a single article in full, and list available upstream platforms — blockbeats, odaily, theblock and similar — for use as filters); sentiment (rank coins by social mention volume over 1h / 4h / 24h, plus per-coin bullish/bearish/neutral counts with an optional time-bucketed trend); vibe (per-contract hotness score over 24h / 72h / 7d / 30d with timeline and sample KOLs per bucket, plus a TOP50 KOL leaderboard sortable by engagement, mentions, or impressions). Triggers: 'latest crypto news', 'BTC headlines', 'search news for X', 'is BTC bullish', 'hottest coins by chatter', 'who is tweeting about <token>', 'vibe score', 'first-mention KOL', and Chinese variants like '最新加密新闻', '搜索新闻', '市场情绪', '情绪排行', 'KOL榜', '热度走势'. Also handles x402/402 payment, quota, MARKET_API_*_OVER_QUOTA, and confirming:true notifications on social endpoints.
Analyze how liquid a stock is using Yahoo Finance data (yfinance): bid-ask spreads, volume and dollar volume (ADTV), top-of-book and options depth, square-root market impact and slippage estimates, turnover ratio, and Amihud illiquidity, rolled into a liquidity grade. Use this skill whenever the user asks about liquidity or trading costs — how easily a position can be entered or exited, what a large order would do to the price, spread or execution-cost estimates, order book depth, volume patterns, or liquidity comparisons — especially for small caps, penny stocks, and thinly traded names.
Choose the right metrics for a LaunchDarkly experiment, guarded rollout, or release policy. Use when the user wants to know which metrics to use, the primary metric for an experiment, which guardrails to add, or which events to monitor in a rollout. Surfaces what will auto-attach from existing release policies before making additional recommendations.
Control and interact with a live browser session on any scraped page—click buttons, fill forms, navigate flows, and extract data using natural language prompts or code. Replaces the old firecrawl-browser command. Use when the user needs to interact with a webpage beyond simple scraping: logging in to a site, submitting forms, clicking through pagination, handling infinite scroll, navigating multi-step checkout or wizard flows, or when a regular scrape failed because content is behind JavaScript interaction. Also useful for authenticated scraping via profiles. Triggers on “browser”, “instruct”, “click”, “fill out the form”, “log in to”, “sign in”, “submit”, “paginated”, “next page”, “infinite scroll”, “interact with the page”, “navigate to”, “open a session”, or “scrape failed”.
Market intelligence: strategy screener, popularity rankings, top movers with news correlation, quote anomalies, index/ETF constituent stocks, morning briefings, catalyst monitoring for watchlist, event-driven strategies, ETF fund flows, sector rotation, market microstructure, supply chain analysis, industry overviews, and ARK-style disruptive innovation analysis. Triggers: "筛选", "策略筛选", "排行", "热度", "异动", "成分股", "晨报", "早报", "催化剂", "事件驱动", "ETF资金流", "板块轮动", "产业链", "行业概览", "颠覆式创新", "ARK", "篩選", "排行", "異動", "成分股", "晨報", "ETF資金流", "板塊輪動", "產業鏈", "screener", "rank", "anomaly", "constituent", "morning brief", "catalyst", "event strategy", "ETF flow", "ETF申赎", "etf flow", "资金申赎", "etf 资金", "sector rotation", "supply chain", "disruptive innovation", "板块筛选", "行业筛选", "板塊篩選", "強勢板塊", "弱勢板塊", "top sectors", "催化劑", "事件驅動", "行業概覽", "顛覆式創新", "策略篩選", "熱度"
Create a LaunchDarkly metric that measures what matters for an experiment or rollout. Use when the user wants to create a metric, track an event, measure page views, button clicks, conversion, latency, error rate, or any custom numeric or binary outcome. Instrument the event first when needed (including SDK setup and .env), then creates and verifies the metric.
Instrument a LaunchDarkly metric event in a codebase by adding a track() call. Use when the user wants to wire up an event, instrument an action for a metric, add tracking to a feature, or confirm that an event is flowing to LaunchDarkly.
Calculate an ETF's premium or discount to NAV from Yahoo Finance data (yfinance), compare or screen ETFs by premium, explain why a gap exists, and decompose a sudden ETF move into NAV-driven vs structural components (dealer gamma exposure, blocked AP arbitrage, sentiment). Use this skill whenever the user asks whether an ETF trades above or below NAV, compares ETF premiums or discounts, screens for the biggest ones, asks about ETF arbitrage or premium convergence, or wants to know why an ETF jumped or diverged from its holdings — including gamma squeezes, dealer gamma exposure (GEX), and blocked creation/redemption. Especially relevant for leveraged, inverse, international, bond, commodity, and crypto ETFs (IBIT, BITO, HYG, KWEB).
Authoritative field/schema reference for 2130 STANDARD Salesforce objects — sObject and Tooling field API names, types, properties (filterable/sortable/groupable/updateable), and relationships for Account, Contact, Opportunity, Lead, Case, ApexClass, and more. Load alongside a SOQL/Apex skill when fields/types/capabilities are unverified — not for query syntax or optimization alone. TRIGGER when verifying/debugging fields in a SOQL/SOSL query or DML against a standard object. Custom __c objects/fields are NOT covered — describe the live org (sf sobject describe). DO NOT TRIGGER for authoring/deploying *-meta.xml or sfdx source (use the Metadata API skill).
Fetch and display GitHub trending repositories and developers. Use when building dashboards showing trending repos, discovering popular projects, or tracking GitHub trends. Triggers: GitHub trending, trending repos, popular repositories, GitHub discover.
A world-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.
When the user wants a comprehensive App Store market overview, daily/weekly market briefing, or combined view of chart movements, trending keywords, featured apps, and new releases. Also use when the user mentions "market overview", "what's happening on the App Store", "market briefing", "weekly report", "market trends", or "state of the market". For chart-specific rank changes only, see market-movers. For keyword trends only, see keyword-research.
Scrape e-commerce data for pricing intelligence, customer reviews, and seller discovery across Amazon, Walmart, eBay, IKEA, and 50+ marketplaces. Use when user asks to monitor prices, track competitors, analyze reviews, research products, or find sellers.
Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.
Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases
When the user wants to track App Store chart rank changes, find top gainers and losers, detect breakout apps entering the top 100, or identify apps dropping out of charts. Also use when the user mentions "chart movers", "rank changes", "who's rising", "who's falling", "new chart entries", "top gainers", or "market shifts". For broader market overview, see market-pulse. For competitive keyword analysis, see competitor-analysis.
Chan Mama e-commerce data query and video tools: look up influencers (find influencers/find streamers/find KOLs/influencer filtering/fan profiles/live-selling data/live-selling rankings/what product did XX sell), look up products (best-seller rankings/hot products/how is this product selling/live-selling influencers/user reviews/product analysis), look up shops (shop data/shop sales/related products/sell-through analysis), look up brands (XX brand market performance/market share/competitor comparison/brand sales trends), look up live streams (live room data/viewer count/bullet comment analysis/live-stream products/live-stream sales), look up videos (hit videos/popular videos/Qianchuan creatives/video live-selling data/video rankings/extract video script/break down video shots/understand video visuals), look up categories (category trends/market analysis/category data/attribute feature analysis). Use this Skill when the user asks about Douyin data, Douyin e-commerce data, or needs to extract video voice-over scripts, break down video shots, or understand video visuals.
Analyze stocks with Mark Minervini's SEPA (Specific Entry Point Analysis) methodology: stage analysis, the 8-condition trend template, fundamentals, VCP and other base patterns, pivot-point entries, market environment, and risk-based position sizing. Use this skill whenever the user mentions SEPA, Minervini, superperformance, the trend template, VCP (volatility contraction pattern), Stage 2, pivot or breakout entries, moving-average stacking (price above the 50/150/200-day MAs), breakout volume, position sizing from risk percentage, growth-stock screening or swing-trade entry criteria, or bases such as cup-with-handle, flat base, bull flag, or high tight flag. Also use it when the user asks "should I buy this stock" or "is this a good setup" about a growth or momentum name, or shares a chart for pattern analysis.
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.
Teaches the agent to produce D3 charts and interactive data visualizations. A comprehensive D3.js skill with examples across chart types and techniques giving the agent expert-level knowledge to generate complex, interactive visualizations. Useful for editorial dashboards, reports, data-rich prototypes, and explanatory graphics.
Write Celigo JavaScript hook scripts -- preSavePage, preMap, postMap, postSubmit, postResponseMap, filter, transform, branching, handleRequest. Use when creating or editing scripts, choosing the right hook point, understanding input/output data shapes, or debugging script behavior.
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns
Elastic ML anomaly detection—investigation/RCA, score explanation, job lifecycle troubleshooting, and job operations. Use when answering “what broke?”/“which entity?”/RCA, “why is score high/low?”/renormalization, “datafeed stopped”/“memory limit”/hard_limit, or configuring ML anomaly detection jobs. Reads results from `.ml-anomalies-*` and job state from ML REST APIs
Query the OpenAlex scholarly database for research papers, authors, institutions, topics, sources, publishers, funders, geo-locations, and keywords. Use when searching academic papers, resolving DOIs, downloading open-access PDFs, finding an author's publications, aggregating bibliometric data (citation counts, h-index, impact factor), exploring the research taxonomies, or performing DOI lookups.
Choose and audit startup metrics using Croll and Yoskovitz's "Lean Analytics". Use when the user mentions "what metrics should we track", "KPIs", "north star metric", "One Metric That Matters (OMTM)", "vanity metrics", "analytics dashboard", "DAU/MAU", "churn benchmark", or "measure product-market fit". Also trigger when choosing metrics for a startup or feature, auditing a dashboard for vanity metrics, setting metric targets and baselines, or instrumenting a product by business model and stage. Covers good-vs-vanity metrics, the One Metric That Matters, metrics by business model, the five startup stages, and benchmarks. For the build-measure-learn loop, see lean-startup. For fixing activation and retention, see improve-retention.
Inspect the availability of ML training on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed training manifest. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. ECC implements no training stack of its own.
Configure Celigo imports—the destination step that writes records to external systems. Use when creating or editing imports, choosing the adaptor type, setting up lookups, upsert or exactly-once writes, or file-based imports. For mapping rules, use writing-mappings; for AI agent steps, configuring-ai-agents.
Configure Celigo export resources—the data source step that fetches records from external systems. Use when creating or editing exports, choosing the right adaptor type for a target application, setting up delta (incremental) exports, webhooks, file transfers, or lookup steps. To replicate tables into a data warehouse, use building-syncs.
Configure Celigo AI agent imports—LLM-powered steps that classify, extract, or generate data within flows. Use when creating agent imports on any supported model provider, or configuring prompts, structured output, tools, or BYOK connections. For guardrails (PII, moderation, policy verdicts), use configuring-guardrails.
Access eligible Wallet financial data using FinanceKit and FinanceKitUI. Use when querying transactions or balances, reading Apple Card, Apple Cash, Savings, or U.K. connected-account data, requesting financial-data authorization, using TransactionPicker, enabling iOS 26 background delivery, or saving and checking Wallet orders.
Write field mappings and transforms in Celigo integrations. Covers Mapper 2.0 (imports), Transformation 2.0 (exports), lookups, response mapping, and Mapper 1.0 (NetSuite DA/Salesforce). Use when editing mappings[], transform{}, responseMapping, or lookup configurations.
Analyze how a private SaaS company's ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts (including an AI premium), competition, and investor demand, benchmarked against private-market medians and peers. Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds, or wants to compare a VC-backed software company's funding rounds. Research the rounds rather than answering from memory.
Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw feedback into roadmap recommendations.
Build Celigo flows—pipelines that move data from source systems to destination systems on a schedule or in response to events. Covers scheduling, chaining, error management, and abstract/instance templating. Use when creating or editing flows; to diagnose a flow that fails, errors, brings no data, or runs slowly, use troubleshooting-flows.
Transfer app data to or from other platforms using AppMigrationKit. Use when implementing system-orchestrated one-time migration between iOS and Android or another platform, building an AppMigrationExtension, packaging transportable resources with ResourcesArchiver, importing resources on the destination device, reporting import progress, handling migration errors and app group cleanup, checking MigrationStatus, or testing migration code with AppMigrationTester
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.