Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Youying Shopee product selection tool. Supports product searches and filtering across all Shopee sites, covering 11 markets including Malaysia, Taiwan, Indonesia, Thailand, the Philippines, Singapore, Vietnam, Brazil, Mexico, Chile, and Colombia. Trigger this skill when users mention Shopee product selection, Shopee product search, Shopee bestsellers, Shopee market analysis, Shopee category selection, Shopee keyword selection, Shopee sales filtering, Shopee price filtering, Southeast Asian e-commerce product selection, Shopee product search, Shopee product selection, Shopee bestsellers, or Shopee market analysis. Even if the user does not explicitly mention "Youying" or "Shopee", trigger this skill whenever their request involves searching for products on Shopee or filtering Shopee product data.
When the user wants to monitor reviews, mentions, and community sentiment about their own product. Also use when the user mentions "track our reviews", "what are people saying about us", "brand monitoring", "reputation management", or "review alerts"
Complete T-SQL function reference for SQL Server and Azure SQL Database. PROACTIVELY activate for: (1) string functions (CONCAT_WS, STRING_SPLIT, STRING_AGG, TRIM, REPLACE, LEFT/RIGHT/SUBSTRING), (2) date/time functions (DATEADD, DATEDIFF, FORMAT, DATETRUNC, AT TIME ZONE), (3) math and conversion functions (CAST, CONVERT, TRY_CAST, ROUND, FLOOR, CEILING), (4) window/ranking functions (ROW_NUMBER, RANK, LEAD/LAG, FIRST_VALUE, LAST_VALUE, NTILE), (5) JSON functions (JSON_VALUE, JSON_QUERY, JSON_MODIFY, OPENJSON), (6) XML functions (FOR XML, .nodes, .value), (7) aggregate functions and GROUP BY extensions, (8) system and metadata functions (sys.* views, OBJECT_ID, OBJECT_NAME). Provides: function catalog grouped by category, version-availability matrix (SQL 2016+/2019+/2022+), and worked examples for each function family
Audit every endpoint in a PostHog project for staleness, failed materialisations, and unused materialised versions. Use when the user asks "what endpoints can I clean up?", "are any of my endpoints broken?", "which materialised versions are still being called?", or wants a one-shot cleanup pass over the Endpoints product. Produces a prioritised report grouped by issue type, with recommended actions but does not modify anything without explicit confirmation.
Expert knowledge for Azure Data Explorer development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing ADX (RBAC/MI), automating cluster/schema deploys, tuning ingestion/queries, or integrating via ODBC/JDBC/Power BI, and other Azure Data Explorer related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics).
A panel of 66 investing greats. Given a stock’s dimensions.json and raw_data.json, 66 investors each score it using their own methodology and output a Pydantic Signal (signal/confidence/score/verdict/comment). Covers nine schools: classic value, growth, macro hedge, technical trend, Chinese value, A-share momentum trading, quantitative systems, tech leaders, and AI positioning hunters. Use when the user asks for “the panel,” “what do 65 investing greats think,” “would [someone] buy it,” or “run an investor vote.”
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, brand monitoring, web analytics (page views, visitors, traffic sources, referrers, devices), and project management (keyword lists, competitors, custom prompts). Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections including free public endpoints that work without an API key. Trigger this skill whenever the user mentions Ahrefs, ahrefs-python, or any SEO data retrieval task in Python.
Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
Design partition schemes, select partition keys, create GSI, and write SQL for PolarDB-X 2.0 Enterprise Edition AUTO mode databases, handling PolarDB-X vs MySQL differences (partitioned tables, GSI, CCI, Sequence, table groups, TTL, pagination, etc.). Use when designing partition schemes, selecting partition keys, converting single tables to partitioned tables, creating GSI/CCI indexes, writing or migrating SQL for PolarDB-X, or diagnosing slow queries on PolarDB-X. Triggers: "PolarDB-X SQL", "PolarDB-X create table", "partitioned table", "partition design", "partition scheme", "partition key", "GSI", "CCI", "Sequence", "MySQL migrate to PolarDB-X", "PolarDB-X compatibility", "single table to partitioned table", "convert to partitioned table", "large table", "table sharding", "distributed table", "AUTO mode", "pagination query", "Keyset pagination", "Range partition", "auto add partition", "PolarDB-X slow query", "full-shard scan"
Use when debugging 'file not syncing', 'CloudKit error', 'sync conflict', 'iCloud upload failed', 'ubiquitous item error', 'data not appearing on other devices', 'CKError', 'quota exceeded' - systematic iCloud sync diagnostics for both CloudKit and iCloud Drive
Use when writing raw SQL queries with GRDB, complex joins, ValueObservation for reactive queries, DatabaseMigrator patterns, query profiling under performance pressure, or dropping down from SQLiteData for performance - direct SQLite access for iOS/macOS
Expert knowledge for Azure Managed Redis development, including troubleshooting, best practices, decision-making, architecture and design patterns, security, configuration, integrations and coding patterns, and deployment. Use when working with Redis tiers, clustering/sharding, Entra ID auth, geo-replication, ARM/Bicep deployments, or other Azure Managed Redis development tasks. Not for Azure Cache for Redis (use azure-cache-redis).
Use Fabric CLI for Power BI operations, including semantic models, reports, DAX queries, refreshes, and gateways. Activate when users work with Power BI items, need to refresh datasets, execute DAX, manage reports, or troubleshoot refresh failures.
When the user wants to investigate a sudden performance change in Google Ads — CPA spike, CVR drop, impression collapse, CTR shift, spend surge, or ROAS decline — or wants to set up proactive monitoring to catch issues early. Triggers on 'performance drop', 'CPA spike', 'why did performance drop', 'impressions dropped', 'CTR dropped', 'conversions disappeared', 'spend increased unexpectedly', 'anomaly', 'performance issue', 'account went wrong', 'diagnose account', 'monitoring', or 'alert setup'. For budget pacing issues see google-ads-budget-management. For ongoing Quality Score issues see google-ads-quality-score.
Build and edit Google Sheets with the google-sheets tools - values, formatting, charts, conditional formatting, structure, validation, named and protected ranges. Use when creating a spreadsheet or dashboard, formatting cells, adding or fixing charts, or reorganizing tabs and rows.
Creates scoped key-value stores, reads and writes state entries, lists keys, and performs partial updates across functions. Use when persisting data between invocations, managing user sessions, caching computed values, storing feature flags, sharing state between workers, or building a KV data layer as an alternative to Redis or DynamoDB.
Analyze Israeli bank transactions, spending patterns, and financial data across Israeli banks and credit card companies. Use when the user asks about bank transactions, spending analysis, “cheshbon bank,” budget tracking, or needs to categorize Israeli banking data. Pairs with israeli-bank-mcp and il-bank-mcp servers (which wrap the israeli-bank-scrapers library) to add financial-analysis workflows. Supports Hapoalim, Leumi, Discount, Mercantile, Mizrahi-Tefahot, First International (FIBI), Otsar HaHayal, Pagi, Union, Yahav, Massad, OneZero, Behatsdaa, Beyahad Bishvilha, Visa Cal, Max, Isracard, and Amex. Do NOT use for payment initiation, money transfers, or investment advice.
Expert guidance for writing safe, reversible Active Record migrations in Rails applications. Use when creating a migration, adding a column, removing a column, changing schema, modifying a table, creating a table, adding an index, adding a foreign key, renaming a column, changing column type, database migration, schema change, rolling back, migration error, data migration, multi-database migration, or any database structure change.
Analyze media files (PDFs, images, diagrams) that require interpretation beyond raw text. Extracts specific information or summaries from documents, describes visual content. Use for document analysis, image understanding, diagram interpretation, chart analysis, table extraction, and any media requiring visual or contextual interpretation beyond literal text extraction.
Use this skill to migrate an existing Microsoft Power BI semantic model into a Keboola semantic layer model. Accepts two input shapes: (1) TMDL folder extracted with the Microsoft Power BI Modeling MCP server in `--readonly` mode (canonical, captures DAX + Power Query M expressions verbatim); (2) per-table JSON produced by older `get_semantic_model` calls (missing M steps). Maps Power BI tables → Keboola semantic-dataset, measures → semantic-metric (DAX preserved verbatim, flagged for SQL translation), relationships → semantic-relationship. Produces JSON files ready to push to the Keboola metastore via `sl-toolkit` or the REST API. Use for: migrate PowerBI semantic model, convert Power BI to Keboola SL, translate TMDL, import PowerBI tables, powerbi-to-sl, pbi-migration, brownfield semantic layer. Trigger when the user has PowerBI artifacts (TMDL folder, `*_semantic_layer.json`, `.bim`, `.pbip`) and wants them as a Keboola semantic layer.
Semantic layer reference for Keboola. Auto-loads when the user mentions: semantic layer, semantic model, SL, metastore, semantic-metric, semantic-dataset, semantic-relationship, semantic-glossary, semantic-constraint, inspect model, validate model, threshold constraints, metric sql, model entities, add dataset, create metric, update metric, delete metric, remove constraint. Provides API reference, payload shapes, CRUD procedures, validation rules, and operational gotchas so Claude can work with the metastore directly.
Crawl/scrape a website or single page into clean LLM-ready MARKDOWN. Use when the user wants to fetch a URL's content, scrape a site, extract an article/page as text, pull docs from the web into the wiki, or "crawl this link". Markdown (not raw HTML) = 5-10x fewer tokens. Built-now- adapt-later: works offline with no key via a builtin urllib→markdown backend; premium backends (Firecrawl/Crawl4AI/Jina) are a one-config adapter.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for creating new spreadsheets, reading/analyzing data, modifying existing spreadsheets, or recalculating formulas.
Wire a PostHog endpoint into a client app or SDK. Covers fetching the OpenAPI spec, generating a typed client with openapi-generator or @hey-api/openapi-ts, sending the right auth header, shaping the variables payload (HogQL code_name vs insight breakdown property), handling rate-limit and materialised-endpoint error responses. Use when the user says "how do I call my endpoint", "generate a client for this", or "what auth header do I use".
Create a recurring AI-generated PostHog report — schedule a free-text prompt to run on a cron, with the LLM-synthesized markdown delivered to email, Slack, or Microsoft Teams on each tick. Use when the user wants a recurring AI summary of X on any cadence (daily, weekly, monthly, yearly) rather than a one-off report. (To attach an AI summary to an existing insight/dashboard subscription instead of a free-text prompt, see `managing-subscriptions` and its `summary_enabled` option.)
Create a PostHog endpoint with the right shape on the first try — covers query kind choice, name conventions, what to expose as variables (HogQL code_name vs insight breakdown), data_freshness_seconds, and whether to materialise on day one. Use when the user says "create an endpoint", "expose this query as an API", "turn this insight into an endpoint", or asks for help structuring a new endpoint. Steers away from common mistakes: materialising a query with cohort breakdowns or an ineligible compare query, inline-only variables on a materialised endpoint, unbounded date ranges, ambiguous names.
Diagnose why a PostHog endpoint is slow or expensive and propose a concrete fix — bump the cache TTL, enable materialisation, restructure variables, or rewrite the query. Use when the user says “this endpoint is slow”, “my endpoint times out”, “we're hitting the cost cap on this one”, or asks “should I materialise this?”. Focuses on a single named endpoint, not a project-wide audit.
DWS cluster CPU high root cause diagnosis skill, based on KooCLI v3.2.0+ and DWS Autopilot MCP Server. Automatically collects CPU metrics, analyzes root causes (customer-side / system-side), and outputs a standardized diagnosis report. Applicable to DWS cluster CPU usage too high, CPU alarms, CPU load anomaly scenarios. Trigger words: “CPU高”, “CPU告警”, “CPU诊断”, “CPU使用率过高”, “CPU满”, “high CPU”, “CPU alarm”, “CPU diagnosis”
Work safely with endpoint versions — preview a draft in the playground, roll back to an older version, update settings on one version without bumping query history, deactivate a specific version. Use when the user asks "how do I roll back my endpoint", "preview my changes before publishing", "I want to fix v5 without bumping the version", or anything involving the version history. Calls out today's limitations honestly: there is no pointer flip; "rollback" means forking the old query into a new top version.
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Signals scout for PostHog web traffic. Watches per-channel session volume, attribution breakage, and landing-page health (bounce and 404 steps) against the site's own baseline. Per- page web vitals belong to `signals-scout-web-vitals`.
Expert guidance for writing efficient, correct Active Record queries in Rails 8.1. Use when writing queries, finding records, building scopes, fixing N+1 queries, using where clauses, includes, joins, eager loading, filtering, searching, plucking data, selecting records, or optimizing database queries. Covers where, find, scopes, includes vs preload vs eager_load, joins, pluck, select, calculations, batching, and query anti-patterns.
Expert guidance for Active Record validations in Rails 8.1. Use when adding validations, writing "validates", "validate", working with "errors", "valid?", "invalid?", building custom validators, handling uniqueness, presence, format validation, numericality, conditional validations, strict validations, or normalizations. Covers model-layer data integrity, DB constraint pairing, error handling, and common pitfalls.
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.