Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use this skill any time a .xlsx file is involved—as input, output, or both. This includes creating spreadsheets, financial models, dashboards, or trackers; reading, parsing, or extracting data from any .xlsx file; editing, modifying, or updating existing workbooks; working with formulas, charts, pivot tables, or templates; and importing CSV/TSV data into Excel format. Trigger whenever the user mentions “spreadsheet,” “workbook,” “Excel,” “financial model,” “tracker,” or “dashboard,” or references a .xlsx/.csv filename.
sqlite-vec extension for vector similarity search in SQLite. Use when storing embeddings, performing KNN queries, or building semantic search features. Triggers on sqlite-vec, vec0, MATCH, vec_distance, partition key, float[N], int8[N], bit[N], serialize_float32, serialize_int8, vec_f32, vec_int8, vec_bit, vec_normalize, vec_quantize_binary, distance_metric, metadata columns, auxiliary columns
Expert-level Apache Airflow orchestration, DAGs, operators, sensors, XComs, task dependencies, and scheduling. Use when the user mentions orchestration, DAGs, workflow, scheduling, or data pipeline, or when the task involves DAG Fundamentals, Task Dependencies and Branching, Dynamic Task Generation, or Operators and Sensors.
Expert knowledge for Azure IoT Operations development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use when building MQTT/OPC UA data flows, WASM/Dapr pipelines, Arc Kubernetes deployments, or ISA‑95 UN topologies, and for other Azure IoT Operations development tasks. Not for Azure IoT (use azure-iot), Azure IoT Hub (use azure-iot-hub), Azure IoT Edge (use azure-iot-edge), or Azure IoT Central (use azure-iot-central).
Build a new dashboard, or update an existing one, from a set of insights — the same job the in-app assistant does with its upsert-dashboard tool, but over MCP. Use when a user asks to create a dashboard, put several metrics/charts together on one page, assemble a dashboard for a topic (product analytics, retention, revenue, activation, etc.), or add/remove/replace insights on a dashboard they already have. Covers deciding create vs update, reusing existing insights vs creating new ones, and using PostHog's vetted dashboard templates as reference for what a strong dashboard on a topic looks like.
Start here for Yandex DataLens when the task has not yet settled on a tool, or when the question is about DataLens itself rather than about carrying something out in it: what DataLens is and what it can do; which installation is in play — Yandex Cloud, on-premise, or the internal Yandex Team one — and how they differ in hosts, auth, and capabilities; what a collection, workbook, connection, dataset, chart, or dashboard is and how they relate; and whether to reach for the MCP server, the Python SDK, the HTTP API, or the web UI. Use it to orient and to route to the skill that does the work. NOT for doing the work once the approach is chosen — that is `datalens-sdk` for Python and `datalens-html-pages` for standalone HTML reports — and not for interpreting what business metric values mean.
Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when the user wants to optimize Python Kafka client configuration for WarpStream.
Comprehensive parser and explainer for the HPK format (proprietary healthcare message format). Supports over 100 message types covering patient administration (ID, MV, CV), supply chain (PR, FO, MA, CO, LI, RO, FA), inventory (SO, IM), organizational structure (ST, UT), and financial operations (RD, DD). Uses @erp-pas/hpk-dictionary as the source of truth. Validates structure, extracts fields, explains business context, maps to HL7 v2.5/IHE PAM, and helps troubleshoot integration issues. Use when the user provides or asks to analyze an HPK-format message, identify an HPK message type, extract fields, or explain HPK business context. For standard HL7 v2.5 / IHE PAM messages, use hl7-pam-parser instead
Use when the user asks to design, review, add, drop, consolidate, or tune SQL Server indexes. Proactively activate for clustered vs. nonclustered design, covering indexes and INCLUDE columns, filtered indexes, columnstore indexes, missing-index DMV interpretation, duplicate or unused indexes, index maintenance, fragmentation, fill factor, compression, partition-aligned indexes, partition elimination proof, huge-table index constraints, online/resumable rebuilds, and index changes for slow T-SQL queries. Provides an index-design decision tree, workload-aware tradeoff checklist, DMV interpretation guidance, and maintenance/rebuild patterns.
Search and analyze TikTok product data, including sales, influencer sales, pricing, and commission rates, across 16 TikTok Shop sites. Trigger this skill when users mention TikTok product search, TikTok Shop product analysis, TikTok sales data, influencer sales, TikTok product selection, TikTok commission rates, TikTok product rankings, EchoTik data queries, TikTok product search, TikTok sales, influencer sales, TikTok commission, TikTok product selection, short-video e-commerce, or TikTok data. Even if users do not explicitly mention “EchoTik” or “TikTok,” trigger this skill whenever their needs involve searching for products on TikTok Shop or analyzing TikTok product performance metrics.
Use FastMoss data to query best-selling product rankings in the global TikTok e-commerce market, with analysis by day, week, month, and category. Trigger for TikTok bestseller rankings, sales rankings, GMV rankings, category bestsellers, weekly product reports, or FastMoss. Also trigger when users want TikTok best-seller rankings or sales rankings by time period, even if “FastMoss” is not mentioned.
Query historical time-series data for Amazon products, including price trends, BSR (Best Sellers Rank) trends, rating changes, seller counts, and monthly sales. Supports any ASIN across multiple Amazon marketplaces. Trigger for price history or tracking, BSR history or trends, historical pricing, price fluctuations, Keepa data, ranking history, price-drop alerts, historical Lightning Deal prices, Buy Box price trends, coupon prices, FBA/FBM price comparisons, changes in seller counts, rating trends, sales history, or related requests. Also trigger for Amazon product-level historical trends in price, rank, or sales, even if “Keepa” or “time-series data” is not mentioned.
Use SellerSprite data to find and analyze competitors on Amazon across 12 marketplaces, including product metrics such as sales, BSR, pricing, ratings, and growth trends. Trigger when users mention competitor lookup or analysis, ASIN reverse lookup, competitive product research, finding similar products, market competitor discovery, product benchmarking, competitor sales estimates, analyzing competing listings, competitor analysis, ASIN reverse lookup, competitor sales, competitor research, SellerSprite, market competitor discovery, or competitor trends. Also trigger even without explicitly mentioning “SellerSprite” or “competitor lookup” if the request involves discovering and analyzing Amazon competitors using ASINs, keywords, seller names, brands, or categories.
Find, browse, and organize content in Omni Analytics — dashboards, workbooks, folders, and labels — using the Omni CLI. Use this skill whenever someone wants to find an existing dashboard, search for content, list workbooks, browse folders, see what dashboards exist, find popular reports, download a dashboard as PDF or PNG, set an alert on a dashboard tile, favorite content, manage labels on documents, or any variant of "find the dashboard about", "what reports do we have", "show me our dashboards", "where is the sales report", "download this dashboard", or "alert me when this changes". This skill locates and organizes content; to inspect what a dashboard tile actually queries — its fields, filters, and sorts — use omni-query.
Embed Omni Analytics dashboards in external applications — URL signing, custom themes, iframe events, entity workspaces, and permission-aware content — using the @omni-co/embed SDK and Omni CLI. Use this skill whenever someone wants to embed a dashboard, sign an embed URL, customize the embedded theme, handle embed events, listen for clicks or drills in the iframe, send filters to an embedded dashboard, set up entity workspaces, look up embed users, build a permission-aware content list, white-label an embedded dashboard, or any variant of "embed this dashboard", "customize the iframe theme", "handle click events from the embed", "filter the embedded dashboard", "set up embedding", or "what dashboards can this user see".
Discover and inspect Omni Analytics models, topics, views, fields, dimensions, measures, and relationships using the Omni CLI. Use this skill whenever someone wants to understand what data is available in Omni, explore their semantic model, find specific fields or views, check how tables join together, see what topics exist, or asks any variant of "what can I query", "what fields are available", "show me the model", "what data do we have", or "how is this data modeled". Also use when you need to understand the Omni model structure before building or modifying anything.
A quantitative signal-scanning and position-sizing tool based on the original Turtle Trading system. Uses the longbridge CLI to retrieve market data for A-shares, Hong Kong stocks, US stocks, and Singapore stocks, and automatically calculates ATR (N value), breakout signals (System 1 / System 2), stop-loss prices, add-on points, and Unit position size. Use when users mention 海龟, turtle, 海龟交易, 海龟信号, turtle signal, turtle trading, ask about breakout signals, ATR, N value, Unit position, stop-loss prices, add-on points, S1/S2 signals, 20-day highs/lows, 55-day breakouts, or request scanning watchlists/indexes for trading signals using the turtle system. Also triggers when users say “扫描突破信号”, “帮我算Unit”, “海龟止损”, “海龟系统分析”, or any variation combining a stock name/code with “海龟”. **Suitable for:** - Scanning for breakout signals after each day's close (20-day/55-day high/low breakouts) - Calculating ATR, stop-loss prices, and add-on points for one or multiple stocks - Calculating suitable Unit position size based on account equity - Checking whether existing positions meet exit or add-on conditions - Scanning watchlists/index constituents for Turtle signals **Not suitable for:** - Fundamental analysis (the Turtle system is purely technical) - Predicting price direction - Automatic order placement (outputs signals only; users trade themselves) - Opening short positions in A-shares, Hong Kong stocks, or Singapore stocks
Windows and Git Bash path handling for SSDT, SqlPackage, and DACPAC files. PROACTIVELY activate for: (1) SqlPackage failing on Git Bash with path errors; (2) DACPAC file path conversion (MSYS, MINGW); (3) shell detection (PowerShell vs Git Bash) for SSDT scripts; (4) MSYS_NO_PATHCONV usage; (5) Windows-style backslash paths in publish profiles; (6) running dotnet build of .sqlproj from Git Bash; (7) cross-platform pipeline compatibility; (8) line-ending issues in SQL files. Provides SqlPackage path-conversion recipes, shell-detection helpers, dotnet build patterns from bash, and CRLF/LF normalization steps.
Manage the full lifecycle of Alibaba Cloud EMR Serverless StarRocks instances — create, scale, configure, maintain and diagnose. Use this Skill when operations engineers, SREs, or architects need to manage StarRocks instances. Typical scenarios include: "create a StarRocks", "check instance status", "scale up CU", "modify configuration", "restart instance", "diagnose issues", etc. Not applicable for: writing SQL/DDL, data import/export, query tuning, materialized view configuration, or managing non-StarRocks products (EMR clusters, Spark, Milvus, ClickHouse, Doris, RDS, ECS).
Use when debugging schema migration crashes, concurrency thread-confinement errors, N+1 query performance, SwiftData to Core Data bridging, or testing migrations without data loss — systematic Core Data diagnostics with safety-first migration patterns
Expert knowledge for Azure Data Api Builder development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when exposing DB objects via REST/GraphQL, tuning paging/timeouts, securing auth/RLS, or deploying DAB to Azure, and other Azure Data Api Builder related development tasks. Not for Azure API Management (use azure-api-management), Azure App Service (use azure-app-service), Azure Functions (use azure-functions), Azure Logic Apps (use azure-logic-apps).
Expert knowledge for Azure AI Language development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using CLU, custom/health NER, CQA, sentiment/PII APIs, or Language/TA/health containers, and other Azure AI Language related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Document Intelligence (use azure-document-intelligence), Azure Speech in Foundry Tools (use azure-speech), Azure Translator (use azure-translator).
Expert knowledge for SAP HANA on Azure Large Instances development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying HANA LIs, Azure Monitor for SAP, HA/DR clusters, Terraform/SDAF automation, or Entra ID SSO, and other SAP HANA on Azure Large Instances related development tasks. Not for Azure Large Instances (use azure-large-instances), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).
Builds and deploys compiled Airflow Java SDK bundles so workers can run them. Use when the user wants to package a JVM task bundle into a JAR, asks about the `org.apache.airflow.sdk` Gradle plugin, `./gradlew bundle`, the Maven shade/BOM setup, fat vs thin JARs, the logging integration artifacts (JPL, SLF4J, Log4j 2, JUL), preview/snapshot builds, or getting the JAR onto an Airflow worker (Docker, Kubernetes, or Astro). For the task code see authoring-java-sdk-tasks; for the Airflow coordinator settings see configuring-airflow-language-sdks.
Expert knowledge for writing, optimizing, and migrating DuckDB transformations in Keboola. Covers SQL dialect, block orchestration, dynamic backends, Parquet format, case sensitivity, Snowflake-to-DuckDB migration, type casting patterns, and best practices. Use when the user is writing DuckDB SQL, migrating from Snowflake, debugging DuckDB transformations, or needs guidance on DuckDB-specific syntax and configuration in Keboola.
Starting point for exploring how a PostHog MCP server's tools are used — routes a broad question to the typed tool that answers it. Use when the user asks "how is my MCP doing?", "what should I look at?", "explore my tool calls", "who uses my MCP tools?", "what are agents doing with the MCP?", or pastes an MCP analytics URL without a specific question. Offers a menu of questions, each backed by a query tool, then hands off to the focused skill.
Discover, query, and analyze Israeli government open data from data.gov.il (CKAN API). Use when user asks about Israeli government data, "data.gov.il", government datasets, CBS statistics, or needs data about Israeli transportation, education, health, geography, economy, or environment. Supports dataset search, tabular data queries, and analysis guidance. Pair with the MCP servers listed below for direct tool access from your agent. Do NOT use for classified government data or data requiring security clearance.
Use this skill when working with Keboola project configurations, understanding JSON config files, editing transformations, or analyzing Keboola project structure. Triggers on questions about Keboola configs, transformations, orchestrations, extractors, writers, or .keboola directories.
Magic ETL dataflows via community-domo-cli — list, get-definition, create, update, run, execution status; JSON DAG actions, transforms, joins. Use when automating dataflows with the community Domo CLI end-to-end. For REST/Java-CLI–first flows or mixed API patterns, use magic-etl instead.
Run queries against Omni Analytics' semantic layer using the Omni CLI, interpret results, and chain queries for multi-step analysis. Use this skill whenever someone wants to query data through Omni, run a report, get metrics, pull numbers, analyze data, ask "how many" / "what's the trend" / "show me the data", retrieve dashboard query results, extract data from an existing dashboard, or inspect the query definition behind a tile. Also use for table calculations and computed columns (running totals, percent-of-total, month-over-month / period-over-period change, moving averages, rankings), open-ended multi-step analysis via agentic AI jobs, and running raw SQL through the semantic layer — even when the user doesn't say "query" (e.g. "add a running total column", "what's our MoM growth", "analyze revenue trends"). For building or editing a dashboard or chart use omni-content-builder; for adding a field or measure to the model use omni-model-builder — this skill retrieves and computes over data.
Turn a data file (CSV/TSV/TXT/XLSX/XLS/JSON) into standalone interactive ECharts HTML charts - 32 chart types, 3 themes, fully offline with no CDN. A data profile (--profile) lets the agent read the data itself and decide what to plot; each chart ships with an embedded fact sheet (plot_stats plus data_preview). This skill produces charts and facts, not written reports or narrative documents. TRIGGER: user supplies or points to a tabular data file and wants a chart; asks to generate a chart/make a graph/visualize data.
Writes Airflow task logic in Java, Kotlin, or any JVM language using the Airflow Java SDK. Use when implementing Airflow tasks in Java/JVM or asking about `@Builder.Dag`/`@Builder.Task`/`@Builder.XCom`, the `Task`/`BundleBuilder` interfaces, reading connections/variables/XComs from Java, JSON-to-Java type mapping, or logging from Java tasks. Covers the Java-specific native API; the shared Python-stub pattern and conceptual model are in authoring-language-sdk-tasks. For building or shipping the bundle, see deploying-java-sdk-bundles; for coordinator config, see configuring-airflow-language-sdks.
Expert knowledge for Azure Data Manager for Agriculture development including limits & quotas, security, configuration, and integrations & coding patterns. Use when setting up BYOL creds/Private Link, ag data ingestion/IoT, AI/nutrient APIs, throttling, or Event Grid logs, and other Azure Data Manager for Agriculture related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks).
Expert knowledge for Azure Health Data Services development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use for FHIR/DICOM APIs, bulk import/export, de-identification, Synapse integration, or SMART on FHIR, and other Azure Health Data Services development tasks. Not for Azure Health Bot (use azure-health-bot), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), or Azure Data Explorer (use azure-data-explorer).
Multi-expert parallel analysis workflow that selects domain expert subagents to analyze datasets in parallel, synthesizes findings into interactive HTML reports with charts, and exports to PowerPoint on request. Requires Python packages: pandas, openpyxl, tabulate, chardet.
Data visualization engineer agent using D3.js, Chart.js, Matplotlib, and Plotly. Chooses visual encodings by perceptual accuracy, enforces WCAG AA contrast, colorblind-safe palettes, and keyboard and screen-reader support, and selects the right chart type for the data relationship. Upstream is a Claude Code subagent definition installed here as a skill.
Query Google Analytics 4 (GA4) reports via the Analytics Data API v1 and list properties via the Admin API. Use when the user mentions Google Analytics, GA4, website traffic / sessions / users, top pages or sources, conversions, or a realtime report.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Analyzes order data and order processing in Magento 2. Use when analyzing orders, retrieving order information, assessing order processing performance, or troubleshooting order issues. Specializes in order lifecycle analysis, payment data inspection, and fulfillment diagnostics.
Translate an Obsidian Bases .base file into sense SQL that returns the same rows. Use when asked to convert, import, or reproduce a .base file (or a Bases view, filter, or formula) with sense, or when a Markdown tree contains .base files whose views someone wants to query outside Obsidian.
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.