Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Mine Amazon reviews for product, listing, and marketing intelligence. Extracts recurring complaints, feature requests, the words customers use, and the reasons they chose the product, then turns them into listing fixes, product improvements, and copy. Use when a user pastes reviews and asks to analyze them, find complaint patterns, mine reviews, understand customer sentiment, pull insights from competitor reviews, or turn reviews into improvements. Trigger phrases: "analyze reviews", "review analysis", "complaint patterns", "what customers say", "mine reviews", "competitor reviews". Works with zero tools. the user pastes the reviews.
Diagnose whether a seller is stuck in the "ACoS trap" by reading ACoS, TACoS, organic rank, and conversion trends together. Identifies whether bid cuts are killing organic sales and outputs a rebalancing plan. Use when a user asks about TACoS, ACoS trap, ads cannibalizing organic, organic rank dropping, or rebalancing ad spend. Trigger phrases: "TACoS", "ACoS trap", "ad cannibalization", "organic rank dropping", "ad spend rebalance". Works with zero tools. the user pastes trend data.
Use when a clinical CSV/Excel dataset needs profiling and cleaning before analysis (missing values, outliers, duplicates, type mismatches). Profiles, flags and generates cleaning code in three stages, each gated on the researcher's approval. Never auto-cleans.
Guides clinical and health science research through PICOT question formulation, evidence hierarchy assessment, bias evaluation (Cochrane RoB 2, ROBINS-I), outcome prioritization, and GRADE certainty rating. Use when formulating clinical research questions, evaluating health evidence quality, prioritizing patient-important outcomes, conducting systematic reviews or meta-analyses, creating evidence summaries for guidelines, or assessing regulatory evidence.
Comparable company analysis using SEC EDGAR financial data. Use when asked to: compare multiple stocks; analyze valuation multiples for a peer group; benchmark margins, growth, or valuation across companies; find the cheapest or most expensive stock in a group; see how a company stacks up against peers. Triggers: "compare AAPL vs MSFT vs GOOGL", "tech sector comps", "which mega-cap has best margins", "peer valuation analysis", "compare these stocks", "how does NVDA compare to AMD". Note: For discovering/screening stocks by theme or criteria, use stock-screening instead.
Use when: the user asks to make or render charts with flint-chart, visualize tabular data, generate a ChartAssemblyInput, validate/render through MCP, or add Flint to a JS/TS project. Author the semantic spec, transform data before Flint when needed, install/import Flint only when executable code is needed, and reserve backend-specific style tweaks for after compiling from Flint.
Use when working with R ggplot2 package, especially ggplot2 4.0+ features. Covers S7 migration (@ property access), theme defaults with ink/paper/accent, element_geom(), from_theme(), theme shortcuts (theme_sub_*), palette themes, labels with dictionary/attributes, discrete scale improvements (palette, continuous.limits, minor_breaks, sec.axis), position aesthetics (nudge_x/nudge_y, order), facet_wrap dir/space/layout, boxplot/violin/label styling, stat_manual(), stat_connect(), coord reversal.
Create KubeSense dashboards over metrics, logs, and traces — either directly with the create-dashboard MCP tool or as preset JSON the user imports — with the exact schema, the fields that hard-fail import, and the fields that silently discard your data instead of erroring. Includes validate-dashboard-json for checking a preset before you commit to it.
Operate Playwright for browser automation end to end: author and debug E2E test suites (robust locators, network interception and mocking, parallel workers, accessibility snapshot checks), wire them into CI, and drive headless browsing and scraping with an extract -> validate -> save loop. Use when writing, running, fixing, or scraping with Playwright, when a Playwright CI failure or JSON report needs triage, or when the bundled pwrun script should analyze a run. Do not use for QA strategy or test framework selection (route to qa-methodology), for frontend component or architecture design (route to frontend-engineering), or for Cloudflare/DDoS-GUARD challenge bypass (use flaresolverr).
Build the measurement layer of a product — one candidate North Star, the tree of levers that hold it up with where each is measured, one metric per active hypothesis with its value today or a gap with an owner, and the antimetrics with their alarm thresholds — and write the Metric Brief. Manually triggered, or entered when the metric gate of product-strategy does not pass.
Quicknode blockchain infrastructure for Solana — RPC endpoints, DAS API (Digital Asset Standard) for NFTs and compressed assets, Yellowstone gRPC streaming, Priority Fee API, Streams (real-time data pipelines), Webhooks, Metis Jupiter Swap integration, IPFS storage, Key-Value Store, Admin API, and x402 pay-per-request RPC. Supports 80+ chains including Ethereum, Polygon, Arbitrum, Base, and more. Use when setting up Solana RPC infrastructure, querying NFTs/tokens/compressed assets via DAS API, building real-time gRPC streams, configuring data pipelines, estimating priority fees, or integrating Jupiter swaps via Metis. Triggers on mentions of Quicknode, qn_ methods, DAS API, getAssetsByOwner, searchAssets, Yellowstone, gRPC, Geyser, Streams, IPFS, Key-Value Store, qnLib, Metis, x402, or Quicknode RPC.
Build trading systems in the style of Renaissance Technologies, the most successful quantitative hedge fund in history. Emphasizes statistical arbitrage, signal processing, and rigorous scientific methodology. Use when developing alpha research, signal extraction, or systematic trading strategies.
Use when building NEW Datex Studio reports from scratch. This is the entry point for all new report work — it orchestrates requirements gathering, schema exploration, datasource creation, layout prototyping, and deployment as a single workflow. Trigger for: "create a report", "build a report", "report from work item", "report from requirements". For modifying EXISTING reports, use `report-editor`.
Best-practices reference for the ScraperAPI Python SDK (scraperapi-sdk package). Consult whenever the user is writing, debugging, or reviewing Python code that calls ScraperAPI. Use when user asks: "scrape a website with Python and ScraperAPI", "how do I use ScraperAPIClient", "ScraperAPI Python render example", "Python ScraperAPI async", "batch scraping Python ScraperAPI", "ScraperAPI premium Python", "how to handle ScraperAPI errors in Python", "ScraperAPI structured data Python". Covers client setup, all request parameters, the escalation ladder (standard → render → premium → ultra_premium), async jobs for batches, structured data endpoints, error handling, and credit budgeting. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Use when standing up a relational data layer with SQLAlchemy 2.x (2.0/2.1) ORM that must run on more than one dialect — SQLite, PostgreSQL, and MySQL — from a single codebase. Covers the typed 2.x ORM (DeclarativeBase / Mapped[...] / mapped_column), engine + connection-string + pooling, short-lived sessions and transaction scope, the sync-first / async (AsyncSession / await) split with a per-dialect driver matrix, and the cross-dialect gotchas that bite when one schema runs on all three engines: row locking (with_for_update / SKIP LOCKED), JSON columns, upsert (ON CONFLICT vs ON DUPLICATE KEY UPDATE), autoincrement / identity, and transaction isolation. Multi-dialect, portable, ORM-first. Not migrations (see alembic), not job-queue/lease patterns (see sql-job-queue), not a full API reference.
Guides CockroachDB version upgrades with tier-appropriate procedures. Self-Hosted covers manual rolling binary replacement with finalization control. Advanced/BYOC covers Console-initiated major upgrades, maintenance windows for patches, and release channel selection. Standard and Basic upgrades are fully automatic with no customer action required. Use when planning, executing, or monitoring a version upgrade.
UTM parameter strategy, campaign tracking, and marketing attribution modeling. Build consistent UTM taxonomies, configure attribution models, measure cross-channel performance, and connect marketing spend to revenue. Use when the user asks about UTM parameters, campaign tracking, attribution models, marketing measurement, or cross-channel analytics.
Reconcile Amazon's 1099-K against actual deposits, fees, and refunds for tax filing. Explains the gap between gross sales on the 1099-K and what hit the bank, maps the numbers to Schedule C lines, and produces the worksheet the seller or their CPA needs. Use when a user asks about 1099-K, Amazon tax documents, reconciling gross vs net sales, year-end tax prep, or why the 1099-K is higher than their deposits. Trigger phrases: "1099-K", "tax forms", "Amazon tax document", "gross vs net", "Schedule C", "year end". Works with zero tools. the user pastes the 1099-K and Payments summary.
Calculate the full Amazon FBA fee stack and the true net profit and margin for a product, and compare FBA versus FBM on cost. Walks the referral fee, the FBA fulfillment fee by size tier and weight, dimensional weight, monthly and Q4 storage, long-term storage risk, inbound shipping, removal fees, and the optional cost lines, then returns net profit, net margin, and break-even ACoS. Use when a user asks to calculate FBA fees, profit per unit, net margin, break-even, whether a product is profitable, fulfillment cost, shipping cost, dimensional weight, storage fees, inbound shipping, removal fees, or to compare FBA versus FBM. Trigger phrases: "FBA calculator", "FBA fees", "profit per unit", "net margin", "break-even", "is this profitable", "shipping calculator", "fulfillment cost", "FBA vs FBM", "dimensional weight", "storage fees", "inbound shipping", "removal fees". Works with zero tools. The user provides price, cost, dimensions, weight, and rates.
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when the user is writing, reviewing, or refactoring server-side code: API endpoints/routes, controllers, services, middleware, database access, or backend architecture. Server-side scope only — for React/Next client-side components use react-best-practices; for cross-cutting TS/JS standards use coding-standards.
Use for backlink work - finding, qualifying, submitting, analyzing or verifying backlinks, directory and blog-comment placements, competitor link sources, anchors, toxic links and disavow, outreach templates, and reading Ahrefs, Semrush or Similarweb backlink and traffic reports (外链、反链、去哪发、提交目录、评论外链、发出去没有、毒性、disavow、竞品外链、竞品导流、数据面板). This Skill owns what to read from those reports and which placements to pursue. Not for generic logged-in browser driving, sessions or doctor faults (use opencli), and not for sitemap, IndexNow or Search Console indexing operations (use rankup); index-submission here is only a reference kept apart from backlinks.
Automate browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to: - Navigate websites and interact with web pages - Fill forms and click buttons - Take screenshots of web content - Test web applications - Extract information from web pages - Debug frontend issues in the browser - Monitor console logs and network requests - Record browser sessions as video This skill uses agent-browser for comprehensive browser automation. RECOMMENDATION: Disable Chrome DevTools or Playwright MCP when using this skill to save context.
Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.
Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow.
Complete knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch scraping
Use when tuning MariaDB, configuring Redis memory, sizing Gunicorn workers, setting up CDN, or profiling slow queries. Prevents performance bottlenecks from default configurations, memory exhaustion, and unoptimized database queries. Covers MariaDB tuning, Redis configuration, Gunicorn worker sizing, CDN setup, slow query log analysis, Python profiling, request profiling. Keywords: performance, MariaDB, Redis, Gunicorn, CDN, slow query, profiling, tuning, optimization, workers, slow page, loading time, ERPNext slow, why is it slow, page takes long, timeout.
Use when creating or modifying Datex Studio functions (configurationTypeId=9) on a Datex Studio branch. Covers the full lifecycle: requirements, intellisense, code authoring, validation, and upload. Trigger for: "create a function", "modify a function", "update xxx_flow", "write a function that does X", "add a parameter to xxx_flow", "change the function code".
GoldRush CLI — terminal-first blockchain data tool with MCP support for Claude Desktop and Claude Code. Use this skill whenever the user wants to query blockchain data from the command line, stream DEX pairs or wallet activity in a terminal, set up GoldRush as an MCP tool provider, or run quick one-off queries without writing code (e.g., 'check a wallet balance', 'what's the gas price', 'search for a token'). Also use this when the user mentions 'goldrush' CLI commands, 'npx @covalenthq/goldrush-cli', or MCP integration with GoldRush. The CLI is the fastest path for ad-hoc blockchain lookups from the terminal. If the user needs programmatic API access in an application, use goldrush-foundational-api or goldrush-streaming-api instead. If the user needs pay-per-request access without an API key, use goldrush-x402 instead.
Google Analytics 4 data analysis and reporting via google-analytics-cli. Use when the user wants to check GA4 traffic, run reports with dimensions and metrics, monitor realtime users, explore account structure, or analyze user behavior. Triggers: "Google Analytics", "GA4", "analytics report", "active users", "page views", "traffic source", "sessions", "engagement", "realtime", "bounce rate".
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
Huawei Cloud MRS Spark SQL specification checking skill. Performs comprehensive SQL statement checking for MRS Spark, including syntax validation, specification compliance, and performance risk detection. triggers: "Spark SQL review", "check Spark SQL", "检查Spark SQL", "Spark SQL检查", "Spark SQL规范", "Spark SQL语法".
JoinQuant official strategy development guide, covering backtesting, paper trading, data APIs, trading functions, factors, and technical indicators. Use when the user writes JoinQuant strategies, backtests, paper trading, queries the JoinQuant API, get_price/order/run_daily, Alpha factors, technical indicators, or mentions joinquant, 聚宽, or jqdata strategies. For local data retrieval use the jqdatasdk skill.
Orchestrates the entire E2E Looker developer onboarding journey. This is the MUST-READ parent skill that coordinates the overall process. Start here when beginning a new Looker setup. It guides you to act as an informative, transparent guide, leading the user in sequence through: Pre-flight, Discovery, CLI, Auth, Connect, Project, Model, Dashboard.
AI-powered browser automation SDK for web scraping, testing, and workflow automation. Use when automating web browsers, extracting data from websites, testing web applications, or building web automation workflows. Supports both API key and AWS credential authentication.
Product-usage reference for ScraperAPI's Async Jobs API — submit scraping jobs in the background and retrieve results via polling or webhook, including batch jobs up to 50,000 URLs. Consult when the user is scraping many URLs, needs non-blocking requests, or wants webhook delivery. Use when user asks: "how do I scrape 1000 URLs with ScraperAPI", "ScraperAPI async jobs", "batch scraping with ScraperAPI", "submit a scraping job and poll for results", "ScraperAPI webhook callback", "scrape URLs in the background", "ScraperAPI batchjobs endpoint". Covers single jobs, batch jobs (up to 50k URLs), webhook callbacks, all apiParams, async-exclusive parameters, binary response decoding, retention policy, and error handling. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
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.