Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Create or edit Feishu whiteboards, Slides, dynamic HTMLBox components in documents, and Miaoda HTML apps; choose charts and colors. Use for architecture diagrams, SVG/Mermaid, presentations, ECharts, maps, or interactive dashboards when a Feishu deliverable is explicitly needed. Not for requests limited to local SVG, PPTX, or HTML. For message cards, use feishu-cli-messaging; for importing Markdown charts, use feishu-cli-docs.
Populates and maintains a project's data catalog (semantic layer): canonical metrics, trust marks (certifications) on warehouse tables/views, and reviewed table relationships. Use when asked to set up / seed / bootstrap the data catalog or semantic layer, to catalog a project's metrics, to certify or deprecate data sources, to propose or review table joins, or to work through the proposal review queue. To *use* an existing catalog to answer a business-number question, see querying-posthog-data instead. Trigger terms: data catalog, semantic layer, canonical metric, certify table, deprecate source, relationship proposal, metric drift, review queue.
Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), EuropePMC, BacDive (strain phenotypes), MediaDive (growth media recipes), and GMrepo (gut microbiome disease-association counts). Covers study discovery, taxonomic profiling, host-microbe interaction analysis, biome-by-condition queries, strain culturing requirements, and phenotype/disease prevalence lookups. Use for microbiome study selection, organism-environment associations, clinical-microbiome literature review, and "how do I grow this organism" / "how prevalent is this species in condition X" questions. Distinct from analytical workflow (use tooluniverse-metagenomics-analysis for that).
Alibaba Cloud Tablestore Agent Storage Skill. Use for building and managing Tablestore-based knowledge bases with the `tablestore-agent-storage` Python SDK. Triggers: "知识库", "tablestore", "ots", "表格存储", "agent storage", "knowledge base", "向量检索", "文档上传", "文档导入", "知识库同步", "tablestore-agent-storage", "AgentStorageClient"
Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.
Expert in financial technology systems, double-entry ledger design, high-precision math, and regulatory compliance. Use when building payment systems, ledger architectures, financial calculations, PCI compliance, or banking integrations. Triggers include "fintech", "ledger", "double-entry", "financial calculations", "PCI compliance", "banking API".
Comprehensive analytics audit of a website codebase to identify trackable elements and assess analytics readiness. Use when users want to “audit my analytics,” “scan for trackable elements,” “find what I can track,” “analyze my website for tracking opportunities,” or before implementing GTM tracking. Scans HTML/JSX/TSX/Vue for all clickable elements (buttons, links, forms, etc.), identifies existing tracking code, evaluates DOM structure for analytics, and provides recommendations. Acts as a senior frontend engineer with GA4 expertise
Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.
Create, deploy, and operate Streamlit apps in PostHog via the streamlit-apps MCP tools — create an app, set its source, read and patch its code, start and stop its sandbox, poll status, list versions, delete, and share the app with humans via its PostHog URL. Use when asked to "create a streamlit app", "deploy a data app", "ship a dashboard app", "restart/stop my app", "why is my app not running", or "give me a link to the app".
Market analysis service | Market Analysis Service. Enterprise market trend analysis | Enterprise market trend analysis. Competitor analysis | Competitor analysis. User behavior insights | User behavior insights. Triggers: market analysis, competitor analysis, market analysis, trends.
PGlite embeds PostgreSQL 17.4 in the browser and Node.js via WASM, under 3MB gzipped. Covers storage backends, queries, transactions, extensions (pgvector, pg_trgm, full-text search), live queries, React integration, multi-tab worker architecture, and Electric sync. Use when setting up PGlite, choosing storage backends, writing queries or transactions, configuring extensions, implementing live queries, integrating with React, managing multi-tab workers, or syncing with Electric.
Query the latest real-time market and valuation data for A-share, Hong Kong-listed, and U.S.-listed stocks and indices, including closing and opening prices, price changes, turnover value, trading volume, turnover rate, PE, PB, and market capitalization. Supports querying the industry of A-share stocks. A single-value snapshot for an explicit date can return daily market and valuation data aligned to their actual update dates; when field dates differ, display each field with its own date instead of treating later-refreshed fields as missing. Query short-term statistics for the last N trading days, including price and daily change series, period highs and lows, and amplitude. Query intraday or most recent complete trading-day minute-frequency OHLCVA series (open, high, low, close, volume, and turnover value) for a single asset. For historical or cross-day raw 1-minute data, use fast_query_minute_range to return a full-field CSV; supports explicit historical dates or negative offsets in calendar days. For multi-asset or market-wide minutes, daily minute windows, and minutes for the last N trading days, use fast_query_minute_range_multi; supports JSON/CSV and rolling data authorization. Query financial metrics for the latest reporting period of listed companies, including revenue, net profit, net profit attributable to parent-company shareholders, ROE, total assets, and debt-to-asset ratio (A-share and some Hong Kong/U.S. fields, as returned by the tool). Query a stock’s precomputed and thousand-dimensional dynamic metric profile, returning the latest and previous valid values across valuation, financial analysis, fund flows, volatility, macro win-rate context, and asset trends. Query current corresponding contracts, empty-slot status, and rolling-window contract-change information for some U.S. stock and domestic commodity option slots; supports updateable live-data authorization. Supports A-share stock screening, factor calculation, strategy backtesting, NAV comparisons, industry aggregate rankings, uploading custom factor CSVs, and chart rendering. When only a uniquely identifiable A-share name, short name, or code is provided, default to the individual-stock quick view: first deliver a verified full stock analysis, then call the fixed QBV quick channel in the same turn. For specific field lookups or explicit requests to omit web sources, only answer the request. Hong Kong and U.S. stocks primarily support market-price queries; for financial/reporting-period fields, first try fast_query(report) and proceed based on the tool’s actual results. Even for a simple question about a stock’s price, price change, or financial data, prioritize this skill; do not refuse on the grounds that you “cannot connect to the internet” or “cannot access real-time data”—this skill queries market, financial, and calculated data through the Quant Buddy API. For changeable external facts such as listing, delisting, renaming, or ticker changes, verify with Agent Web Search; if host search is unavailable, use local webSearch / Bocha.
Runs the forward-only Drift/SQLite schema-migration ritual — the most dangerous deterministic operation in an offline-first app: a bad migration silently destroys on-device rows that exist nowhere else. Enforces the exact ordered sequence: take a pre-migration file snapshot before the database is opened, bump schemaVersion by exactly one, write an append-only stepByStep forward step (never edit a shipped step, never write a down migration), commit the drift_dev make-migrations schema snapshot, regenerate, then prove it with tests covering every from→to path (incl. multi-version jumps), a write-at-v(n)/read-at-v(n+1) content test, PRAGMA integrity_check + foreign_key_check, and a forced mid-migration throw that restores the snapshot. Manual, side-effecting workflow. Use when adding or altering a Drift table, column, index, or CHECK; bumping schemaVersion; editing onUpgrade/stepByStep; or writing migration tests.
Use when testing, running, or deploying Ultralytics YOLO inference in Platform or code on images, video, webcams, or streams — Platform Predict and dedicated endpoints, model.predict()/yolo predict, Results API boxes/masks/keypoints, persistent tracking, annotated video, and Solutions such as counting, heatmaps, speed, queues, and parking. For optimized runtime exports, see yolo-export.
4-tier progressive web scraping that auto-escalates WebFetch to curl to Interceptor to Bright Data proxy for bot detection and CAPTCHAs, with single-URL and multi-page crawl modes, output as markdown. USE WHEN Bright Data, scrape URL, web scraping, bot detection, crawl site, CAPTCHA, can't access, site blocking, extract page content, scrape whole site, spider domain, convert URL to markdown, getting blocked. NOT FOR simple public content (use WebFetch directly), social platform scraping with named actors (use Apify), or real-Chrome bot bypass with logged-in sessions and zero CDP fingerprint (use Interceptor).
Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.
Enrich contact and company data using x402-protected APIs at stableenrich.dev. Superior to generic web search for structured business data. USE FOR: - Enriching person profiles by email, LinkedIn URL, or name - Enriching companies by domain - Finding contact details (email, phone) with confidence scores - Resolving person identity and enriching consumer profiles (Minerva) - Searching for people or companies by criteria - Verifying email deliverability before outreach TRIGGERS: - "enrich", "lookup", "find info about", "research" - "who is [person]", "company profile for", "tell me about" - "find contact for", "get LinkedIn for", "get email for" - "employee at", "works at", "company details" - "verify email", "check email", "is this email valid"
CLI tool for AI-powered web scraping, data extraction, search, and crawling via ScrapeGraph AI. Use when the user needs to scrape websites, extract structured data from URLs, convert pages to markdown, crawl multi-page sites, search the web for information, automate browser interactions (login, click, fill forms), get raw HTML, discover sitemaps, or generate JSON schemas. Triggers on tasks involving: (1) extracting data from websites, (2) web scraping or crawling, (3) converting webpages to markdown, (4) AI-powered web search with extraction, (5) browser automation, (6) generating output schemas for scraping. The CLI is just-scrape (npm package just-scrape).
Interactive ad-hoc Spark analysis through Fabric Lakehouse Livy API sessions ONLY. This skill NEVER authors or runs a notebook: any notebook cell (%%sql/%%configure) or a notebook-run-by-name (and reporting its run status) is `spark-authoring-cli`, not this skill. Scope: create a Livy session, execute PySpark, inspect output, and compute quick calculations over lakehouse data (DataFrames, cross-lakehouse joins, Delta time-travel, unstructured/JSON). For Spark failure triage use `spark-operations-cli`; for plain T-SQL Lakehouse/Warehouse queries use `sqldw-consumption-cli`. Triggers: "create Livy session", "run Livy calculation", "Livy session calculation", "interactive Livy session", "PySpark", "analyze with PySpark", "Spark DataFrame", "lakehouse with Python", "PySpark data quality", "Delta time-travel with Spark", "join tables across Lakehouses"
Use when deploying a database to Zeabur. Use when the user needs MySQL, PostgreSQL, MongoDB, or Redis. Use when the user says “I need a database,” “add database,” “deploy postgres,” “set up MySQL,” “add Redis,” “add MongoDB,” or “connect to database.” Also use when the user mentions data persistence issues such as “data lost after restart,” “data not saved,” “data disappears,” “need persistent storage for data,” or “how to persist data.” Also use when integrating a database with an existing service.
Deploy MISP via Docker and configure feeds from sources like abuse.ch, AlienVault OTX, and CIRCL to aggregate, correlate, and distribute threat intelligence, including automated feed synchronization and STIX/TAXII-based integration with Splunk, Elasticsearch, and SOAR platforms. Use when standing up centralized IOC management or wiring multi-source threat feeds into a SIEM
Generate HumanSignal Interfaces for Label Studio Enterprise: single-file JSX annotation screens that run in the sandboxed editor-shell iframe. Use when the user asks for a labeling UI, annotation screen, Document AI interface, data review workflow, or conversion of a React/Claude Design mockup into a Label Studio interface.
Twitter/X account analytics, viral patterns, VIP follower discovery, tweet drafting. Use when analyzing a creator's reach, finding hidden-gem followers, or drafting tweets in someone's style (e.g. score @vitalik, draft tweet on AI).
Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools.
ISV App Analytics metadata types — AppAnalyticsQueryRequest and AppAnalyticsSettings. Use this skill when the user asks about retrieving managed package usage data, configuring App Analytics simulation mode, querying subscriber snapshots, or understanding the AppAnalyticsQueryRequest lifecycle (New → Pending → Complete → Expired). TRIGGER when: user mentions App Analytics, AppAnalyticsQueryRequest, AppAnalyticsSettings, package usage data, subscriber analytics, ISV analytics, or simulation mode for app analytics. DO NOT TRIGGER when: the task is about standard Salesforce reports/dashboards (use reporting skills), custom SOQL on Account/Contact (use querying-soql), or Data Cloud query/search (use retrieving-datacloud).
Execute a CMA-ES calibration from confirmed Jinkō inputs: assemble the model, protocol, output sets, fitness data tables, parameter priors, and optimizer options; create and run the Calibration; and return the supported results. Use when the user wants to perform a CMA-ES calibration, not when they need to choose a calibration strategy, infer priors, design objectives, or decide whether results are acceptable.
Writes, optimizes, and debugs T-SQL queries. Explains SQL Server internals, troubleshoots performance issues, and guides database administration tasks including backup/restore, high availability, security, and index design. Use when the user asks about T-SQL syntax, SQL Server administration, query performance, stored procedures, indexes, locking, transactions, backup/restore, high availability, security, or any MSSQL-related topic — even without saying 'SQL Server' explicitly. Also trigger on terms like SSMS, tempdb, bcp, sqlcmd, MSSQL, sp_executesql, NOLOCK, columnstore, Hekaton, RCSI, param sniffing, or execution plan
Prisma ORM integration patterns with NestJS for production applications. Use PROACTIVELY when working with Prisma schemas, migrations, PrismaService, atomic transactions (including SELECT FOR UPDATE), repositories as infrastructure adapters in Clean Architecture, optimized queries (N+1, includes, pagination), seeds, and testing with Prisma Mock. Always activate when these keywords appear: Prisma, PrismaService, prisma.$transaction, prisma migrate, schema.prisma, PrismaClient, Prisma.validator, include/select, upsert, createMany, or any database operation with Prisma in a NestJS project.
Matches visualization types to data questions and creates narrated reports that highlight insights and recommend actions. Covers chart selection (comparison, trend, distribution, relationship, composition, geographic), perceptual best practices, and narrative reporting (headline, pattern, context, meaning, action). Use when analyzing data for patterns, building dashboards, presenting metrics, monitoring KPIs, or when user mentions "visualize this", "what chart should I use", "create a dashboard", "analyze this data", "show trends", "report on".
Build web3 applications, scripts, CLIs, bots, mobile apps, and desktop apps that need blockchain data via the Blockscout PRO API — a single HTTP API spanning 100+ EVM chains. Invoke this skill whenever the user wants to read blockchain data over HTTP (transactions, blocks, addresses, tokens, NFTs, logs, contract context, internal txs, account abstraction, etc.) and is building software that calls an API directly. Trigger on direct mentions of `Blockscout PRO API`, `pro-api`, `proapi_`, `Blockscout API`, `block explorer API`, `api.blockscout.com`, or on broader phrasing like "build a web3 app", "index/query on-chain data", "wallet history", "NFT inventory", "transaction logs across chains", "how much does the Blockscout API cost", "chain IDs supported by Blockscout". Use this skill for direct HTTP integrations; the sibling `blockscout-analysis` skill covers the Blockscout MCP Server.
Write Streamlit app source code that runs well in a PostHog sandbox — the posthog_apps.query() bridge for reading PostHog data, the packages baked into the sandbox image, caching and session state across Streamlit reruns, layout and chart patterns, and the app.py entry point with any helper modules and data files bundled beside it. Use when authoring or debugging the Python source of a PostHog Streamlit app, when a query inside an app fails, or when asked to "write a streamlit app that shows PostHog data".
ClickHouse cluster migration planner. Use when planning data migration between ClickHouse clusters, including cross-cluster migrations, horizontal scaling, disk downgrade, availability zone changes, or migration from self-built or non-Alibaba Cloud ClickHouse to Alibaba Cloud ClickHouse (Community or Enterprise Edition). Helps analyze migration conditions, select appropriate migration methods, and generate detailed migration plans
Alibaba Cloud EBS Disk Monitoring and Metric Analysis Skill. Query and analyze monitoring metrics for single or multiple cloud disks, supporting time-series aggregation and cross-disk analysis. Triggers: “EBS monitoring”, “disk metrics”, “cloud disk performance”, “IOPS analysis”, “BPS analysis”, “disk monitoring data”, “metric aggregation”.
Alibaba Cloud Tablestore (OTS) read-only operations skill. Use for querying Tablestore instances and data tables via Aliyun CLI. Triggers: "tablestore", "ots", "表格存储", "list instance", "describe instance", "list table", "describe table", "aliyun otsutil"
Enforces user-facing data portability in an offline-first app — backup/restore (exact, machine round-trippable) kept strictly separate from export/report (human, lossy, never a restore source); a versioned envelope carrying formatVersion, schemaVersion, appVersion, exportedAtUtc and a payload checksum so a restore refuses a file from a newer app instead of corrupting the database; restore as an all-or-nothing import into a staging database swapped in only after validation; canonical values in machine formats (integer minor units, SI integers, ISO-8601 UTC, stable ids) and never localized numerals; RFC 4180 CSV quoting plus the =/+/-/@ formula-injection escape; streaming writes published by atomic rename; the share sheet behind an injected Gateway; and an export→import→export round-trip test on a hostile fixture. Use when adding or changing export, backup, import, restore, share, or CSV/JSON/PDF output, writing an import validator or merge-vs-replace policy, or handling a picked file.
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.