Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Parse, navigate, and query materials science ontology structures — browse class hierarchies, inspect individual classes and their properties, look up object and data property definitions with domain/range, search for ontology terms by keyword, and parse or summarize raw OWL/XML files. Currently supports CMSO and ASMO; the broader OCDO ecosystem (CDCO, PODO, PLDO, LDO) is planned. Use when exploring what classes or properties an ontology provides, finding the right CMSO term for a crystal structure or simulation concept, understanding parent-child class relationships, or onboarding to an unfamiliar materials ontology, even if the user only says "what ontology terms describe my FCC copper simulation" or "show me the CMSO class hierarchy."
Provides comprehensive guidance for Spring Data JPA including repositories, entity management, query methods, and database operations. Use when the user asks about Spring Data JPA, needs to work with JPA repositories, implement data access layers, or configure JPA in Spring
The language-neutral foundation for Airflow language SDKs — implement task logic in a non-Python language while the DAG stays in Python. Use when the user wants to run an Airflow task in another language (Java, Kotlin, Go, or other JVM/native languages), asks how the Python `@task.stub` pairs with native task code, how task/DAG IDs must match across the two sides, how data passes via XCom as JSON, or which language SDKs exist. This skill owns the shared Python-stub pattern and conceptual model; for a specific language's native API, build, and runtime, use that language's skill (e.g. authoring-java-sdk-tasks, authoring-go-sdk-tasks)
Expert knowledge for Azure Resource Graph development, including troubleshooting, best practices, decision-making, limits and quotas, configuration, integrations, and coding patterns. Use when querying via CLI/PowerShell/REST, using GET/LIST vs. Query, handling paging and quotas, or creating Power BI ARG reports, and for other Azure Resource Graph development tasks. Not for Azure Monitor (use azure-monitor), Azure Policy (use azure-policy), Cost Management (use azure-cost-management), or Azure Resource Manager (use azure-resource-manager).
Configure Airflow to run language SDK tasks (Java, Go, and future native SDKs): register a coordinator, map a queue to it, ensure the runtime/artifact is available on workers, and tune coordinator options. Use when the user wants Airflow to route a queue to a native-language coordinator; asks about `[sdk]` `coordinators`/`queue_to_coordinator` settings, `AIRFLOW__SDK__COORDINATORS`, `jars_root`, `executables_root` or other coordinator `kwargs`, `task_startup_timeout`; or asks why native tasks aren't being picked up. Covers the shared routing mechanism and per-coordinator options (e.g. JavaCoordinator, ExecutableCoordinator).
Manage DingTalk product capabilities, including AI tables, calendar, contacts, documents, bots, tasks, email, meeting notes, AI apps, approvals, logs, and DingTalk Drive. Use when users need to work with spreadsheet data, manage schedules and meetings, look up contacts, send message notifications, handle approval workflows, view meeting-note summaries, create apps, systems, admin backends, or business tools, view daily or weekly reports, or manage DingTalk Drive files.
Parse and explain HL7 v2.5 IHE PAM (Patient Administration Management) messages. Identify the message type, extract segments (MSH, EVN, PID, PV1, PV2), validate the structure, and provide detailed explanations of ADT messages for patient administration workflows. Use when the user provides or asks to analyze an HL7 v2.5 IHE PAM / ADT message, identify a message type, or extract or explain MSH/EVN/PID/PV1/PV2 segments. For the proprietary HPK format, use hpk-parser instead
Implement and extend PostHog Data warehouse import sources. Use when adding a new source under products/warehouse_sources/backend/temporal/data_imports/sources, adding datasets or endpoints to an existing source, or adding incremental sync, resumable imports, webhook ingestion, pagination, credential validation, and source tests.
Two scopes: (1) Drives Kimi.app via computer-use for Work/Agent data-plugin queries (天眼查、同花顺 iFinD、SEC、IMF etc.) and Chat's built-in 深度研究 report; (2) kimi-cu macOS desktop-automation playbook — AX set_value / keyboard / click channel selection, foregrounding, AX-tree recovery, form & dialog recipes. Use for "用 Kimi 查" / "操作 Kimi 客户端" / "Kimi 插件" / "Kimi 深度研究" / driving any macOS desktop app (Electron/webview/native) via kimi-cu. Not for kimi.com browser automation (use kimi-webbridge) or direct iFinD API access.
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
Generate comprehensive workload analysis visualizations for PostHog customer accounts. Use when user requests account analysis, workload breakdown, SDK analysis, spend allocation, or expansion opportunity assessment. Triggers include "analyze [account]", "workload analysis for [account]", "SDK breakdown for [account]", "show me how [account] uses PostHog", or any request to understand customer usage patterns across products and platforms.
Writes Airflow task logic in Go using the Airflow Go SDK. Use when the user wants to implement Airflow tasks in Go, asks about `BundleProvider`/`RegisterDags`, the `bundlev1` Registry/Dag interfaces, registering Go tasks (`AddTask`/`AddTaskWithName`), dependency injection by parameter type (`context.Context`, `sdk.TIRunContext`, `*slog.Logger`, `sdk.Client`), or reading connections/variables/XComs from Go. This skill covers the Go-specific native API; the shared Python-stub pattern and conceptual model live in authoring-language-sdk-tasks. For building/packing/shipping the bundle see deploying-go-sdk-bundles; for coordinator config see configuring-airflow-language-sdks.
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
Guides a user from "I want to watch recordings but don't know which ones" to a short, high-signal list of sessions worth watching. Use when the user asks which sessions or replays to watch, wants help finding interesting / useful recordings, says they don't know where to start in session replay, or wants to watch sessions about a goal (signup, pricing, onboarding, checkout, a feature, rageclicks, errors, mobile, a specific person) without naming exact filters. Turns a vague intent into a focused RecordingsQuery via `query-session-recordings-list`, then deep-links the best few and hands off to `investigating-replay`. Do NOT use when the user already has a recording/session ID (use investigating-replay) or wants the replay for a known error issue (use finding-replay-for-issue).
Use whenever code will build, return, fetch, or execute SQL that runs against a user's real Postgres database—even when the request reads like an ordinary feature or bug fix and never says "security," "injection," or "SafeSqlFragment." This covers: writing or editing any pg-meta function, query builder, or endpoint that builds/returns SQL for database objects (tables, views, functions, DB triggers, indexes, RLS policies); interpolating a schema/table/column/search/route-param value into SQL text; storing, fetching, or re-running SQL that round-trips from the database (a policy's definition, a function/view definition, a snippet's saved content); and any "Run"/"Apply"/"Execute" action that sends SQL to a project's database (SQL editor run-selection, policy editor apply, snippet runner). Load this BEFORE writing such code, not only when reviewing a finished diff. Skip only for changes that never touch SQL text or execution—styling, unrelated data hooks, non-SQL form validation, or UI layout work.
Expert knowledge for Azure Synapse Analytics development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use for Synapse SQL pools, Spark pools, Delta Lake, Synapse Link (Cosmos/SQL), ELT via PolyBase, and other Azure Synapse Analytics development tasks. Not for Azure Data Factory (use azure-data-factory), Azure Data Explorer (use azure-data-explorer), Azure Databricks (use azure-databricks), or Azure Stream Analytics (use azure-stream-analytics).
Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating the credits it will spend and checking the org's remaining budget before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep. TRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query, sampling_rate, or sampling_mode (or moving it to a pricier model) via vision-scanners-update. DO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against sessions on demand (vision-scanners-scan-session/-scan-sessions). For a one-off question about sessions you already have, use vision-scanners-inline-scan rather than creating a scanner — the skill's first section covers when that applies.
Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium (HTML/React artifact, inline SVG, matplotlib/plotly/d3/Recharts, PNG). Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / KPI row, or laying out a dashboard. Teaches a design-system-agnostic method - form heuristic, color formula with a runnable validator, mark specs, interaction rules. Default palette in references/palette.md.
Builds, packs, and deploys compiled Airflow Go SDK bundles so the ExecutableCoordinator can run them. Use when the user wants to compile a Go task bundle, asks about `go build`, `go tool airflow-go-pack`, the AFBNDL01 self-contained executable bundle, packing or inspecting a bundle, placing it under `executables_root`, cross-compiling a bundle for workers, `go-sdk` module versioning/tags/pseudo-versions, or getting the bundle onto an Airflow worker (Docker, Kubernetes, or Astro). For the task code see authoring-go-sdk-tasks; for the shared coordinator settings see configuring-airflow-language-sdks.
Analyzes and retrieves catalog information from Magento 2. Use when analyzing product catalogs, retrieving catalog data, assessing catalog health, or optimizing catalog performance. Masters catalog structure analysis, product data inspection, and catalog diagnostics.
Use the official MinerU (mineru.net) parsing API to convert a URL (HTML pages like WeChat articles, or direct PDF/Office/image links) into clean Markdown + structured outputs. Use when web_fetch/browser can’t access or extracts messy content, and you want higher-fidelity parsing (layout/table/formula/OCR)
Sizes a Qdrant deployment before it is provisioned. Use when someone asks 'how much RAM do I need', 'how many nodes', 'how big should my cluster be', 'sizing', 'capacity planning', 'will N vectors fit', 'what instance type should I pick', or gives a vector count and dimensions and asks what to provision. Also use when an existing estimate needs checking before hardware or a cluster tier is bought
Use when analyzing Xiaohongshu audience demographics, understanding follower characteristics and behavior patterns, creating user personas to guide content strategy, identifying audience segments for targeting, or using audience insights to optimize content and engagement
Expert knowledge for SQL Server on Azure Virtual Machines development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use when planning SQL VM vs Managed Instance, Always On/FCI HADR, Azure storage and backup, Key Vault/MI authentication, KQL telemetry, or other SQL Server on Azure Virtual Machines development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Virtual Machines (use azure-virtual-machines), or SAP HANA on Azure Large Instances (use azure-sap).
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series—sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements—without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.
This skill should be used when users need help analyzing content analytics data, creating reports, identifying trends, calculating ROI, or providing content optimization recommendations. It activates when users ask analytics questions, request reports, need performance analysis, ROI calculations, trend identification, or content optimization recommendations.
Implement a vulnerability aging dashboard and SLA tracking system that measures time-to-remediation against severity-based deadlines (e.g. 14 days critical, 30 days high, 60 days medium, 90 days low), with automated escalations and compliance metrics reporting. Use when designing SLA policies, building aging/remediation dashboards, or proving compliance with remediation timelines.
Use when building, modifying, deploying, or debugging Keboola Apps (Streamlit or Python/JS). Covers the full lifecycle — choosing app type, the Python/JS prod-app-and-drafts model (never create a second app), configuring keboola-config/, storage access (RO workspace, RW Query Service, input mapping), authentication, DuckDB caching for performance, default Keboola styling, dashboard patterns, optional Kai chat integration, and the three client paths (MCP-only, Claude Code with filesystem, kbagent CLI).
Scan a project to identify Kafka applications, extract schemas from data models, tag PII fields, generate Terraform for Confluent Schema Registry registration, and produce a migration report with rollout ordering. Use this skill when a user asks to analyze a folder or repo for Kafka usage, extract schemas, audit producer/consumer configurations, or generate Terraform for Schema Registry.
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.