Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Builds reactive real-time backends on the iii engine. Use when building event-driven apps where state changes automatically trigger side effects, clients receive live updates via streams or websockets, or you need a real-time database layer with pub/sub and CRUD endpoints.
Keboola platform context — automatically load this whenever working on any Keboola Python component task (developing, testing, debugging, reviewing, schema design). Contains platform-level knowledge about how the Keboola Connection platform executes components, manages configuration, handles state, and runs jobs. This is not about code patterns — it is about platform behaviour that affects how code should be written and tested. Always pull this before making decisions about config structure, row handling, state, parallelism, or test data layout.
Expert-level Looker BI, LookML, explores, dimensions, measures, dashboards, and data modeling. Use when the user mentions LookML, BI, analytics, dashboards, or data modeling, or when the task involves LookML Basics, Advanced Dimensions and Measures, Persistent Derived Tables, or Explores and Joins.
Expert-level MongoDB database design, aggregation pipelines, indexing, replication, and production operations. Use when the user mentions NoSQL, database, aggregation, or performance, or when the task involves CRUD Operations, Query Operators, Aggregation Pipeline, or Indexing.
Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. Given a goal like “improve/fine-tune ASR for my domain or language,” it scopes the task, picks the cheapest sufficient path (word boosting → n-gram LM → fine-tuning), delegates each stage to the right sub-skill (data generation, training, evaluation, deployment), and answers cost, time, and data questions along the way.
Map materials science terms, crystal structures, and sample descriptions to standardized ontology classes and properties — resolve natural-language concepts to ontology entries with confidence scores, translate Bravais lattice types, space groups, and lattice constants into ontology-compliant annotations, and produce full sample metadata from structured descriptions. Supports any ontology in ontology_registry.json (CMSO, ASMO, etc.). Use when annotating simulation inputs with FAIR metadata, translating "BCC iron" or "FCC copper" into formal ontology terms, preparing machine-readable sample descriptions, or bridging between lab vocabulary and ontology vocabulary, even if the user only says "what CMSO terms describe my material" or "annotate this sample for me."
Exécute des requêtes SQL en lecture seule sur plusieurs bases de données PostgreSQL. À utiliser pour : (1) interroger des bases PostgreSQL, (2) explorer les schémas/tables, (3) exécuter des requêtes SELECT pour l'analyse de données, (4) vérifier le contenu des bases. Supporte plusieurs connexions avec descriptions pour une sélection automatique intelligente. Bloque toutes les opérations d'écriture (INSERT, UPDATE, DELETE, DROP, etc.) par sécurité.
Set up the sense CLI on a markdown tree and make the decisions that shape its index: run sense init, choose sqlite, duckdb, or turso, define presets and search signals, configure embeddings, and design queryable note conventions. Use when creating or restructuring a markdown knowledge base, editing sense.config.json, choosing a store or embedding model, or deciding how agents should write notes for later retrieval.
Analyzes profitability per customer, product, or transaction to determine business model viability and scalability. Covers CAC, LTV, contribution margin, cohort analysis, and growth-readiness assessment. Use when evaluating business model viability, validating startup metrics (CAC, LTV, payback period), making pricing decisions, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, or break-even analysis.
Provides an access guide for the Huawei Cloud Graph Database GES service. Covers Cypher queries, GQL queries, schema/label management, summary info queries, graph data editing, and more. Use this skill when users want to operate the Huawei Cloud graph database GES service via terminal.
When the user wants to build data enrichment workflows, score leads against ICP, set up Clay waterfalls, or improve contact data quality. Also use when the user mentions 'enrichment,' 'data enrichment,' 'Clay,' 'waterfall enrichment,' 'ICP scoring,' 'lead scoring,' 'intent data,' 'contact verification,' 'Apollo,' 'ZoomInfo,' or 'data quality.' This skill covers lead enrichment waterfalls, ICP scoring frameworks, and contact verification systems
DataWorks metadata Skill for Alibaba Cloud — browse Data Map metadata and perform non-destructive writes via Aliyun CLI. READ scope: list/get catalogs, databases, tables, columns, partitions; query data lineage (upstream/downstream impact); list/get datasets & versions; list/get metadata collections (Category/Album) and entities inside them; preview dataset version content. WRITE scope (non-destructive only): update table & column business metadata; register lineage relationships; create/update datasets and versions; create/update metadata collections and add entities to them. This Skill exposes NO delete or remove APIs — every `delete-*` and `remove-*` operation is intentionally out of scope. For deletions, use the DataWorks console. Triggers: "dataworks metadata", "data map", "data lineage", "meta collection", "dataset", "catalog", "table info", "column info", "partition", "impact analysis", "register lineage", "create dataset", "update business metadata".
Expert knowledge for Azure Database for MySQL 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 MySQL Flexible Server tiers, HA/DR, automated deployments, backups/replication, or secure app connectivity, and for other Azure Database for MySQL development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), or Azure Database for PostgreSQL (use azure-database-postgresql)
Busabase is the open-source database and workspace for AI agents. Use this skill to read and write a Busabase workspace — records, docs, files, skills and apps — through the CLI, API or MCP. Writes are change requests: they land at once with write access and wait for review otherwise.
A-share sector research: computes board-wide Top N gainers, cross-checks week/month announcement windows across two sources, and grades market-sentiment evidence (L1–L3), via parallel Agent Team execution with fresh-context adversarial verification. Use for 「XX 行业 Top N/Top 10 标的」, 「这些标的最近一周/一个月发过哪些公告」, 「判断 XX 行业/板块现在的市场情绪」, or 「用 Agent Team 做行业投研」/对抗验证行业研究结论。
AI-powered financial analysis suite. Generates executive CFO briefings from QuickBooks exports (P&L, Balance Sheet, General Ledger, Cash Flow, etc.) with anomaly detection, burn rate, runway analysis, and scenario modeling. Also estimates codebase development costs with organizational overhead and AI ROI analysis. Triggers on: 'CFO briefing', 'financial analysis', 'cost briefing', 'expense review', 'runway analysis', 'burn rate', 'cost estimate', 'how much would this cost to build', 'development cost', 'Claude ROI'.
Local data SDK for JoinQuant, providing A-share market quotes, historical K-line data, financial data, indicator data, and more from the same source as the JoinQuant website. Use when users mention jqdatasdk or JoinQuant data, need A-share data, or have a JoinQuant account. Unlike baostock (free) and tushare (the most comprehensive data), jqdatasdk requires a registered JoinQuant account to obtain a Token and suits users already using the JoinQuant platform. Use this skill to retrieve local data; use the joinquant-strategy skill for strategy development on the JoinQuant website.
Validate material sample annotations against ontology constraints—check that class and property names exist in the ontology, verify domain and range consistency for object property relationships, assess annotation completeness (required, recommended, and optional properties), and flag unknown or misspelled terms. Use when verifying that CMSO or other ontology annotations are correct before publishing, checking whether all required properties are present for a class like Crystal Structure or Unit Cell, auditing relationship triples between instances, or catching annotation errors early in a FAIR data workflow, even if the user only says “is my annotation correct” or “what am I missing.”
This skill should be used when the user asks to optimize slow T-SQL queries, fix SQL Server performance regressions, improve SARGability, rewrite joins, prove whether joins can be removed, compare temp-table data types, select rewrite templates, diagnose parameter sniffing, evaluate query hints, inspect statistics/cardinality estimates, reduce spills or memory grants, use Query Store, or interpret plan evidence. PROACTIVELY activate for slow queries, scans vs seeks, implicit conversions, bad joins, tempdb spills, query rewrites, partition predicate safety, and before recommending indexes or hints. Provides: evidence-first workflow, rewrite templates, parameter-sniffing playbook, and gating rules before recommending indexes or hints.
A Tushare data research skill for natural-language requests in Chinese. It turns requests such as "how has this stock been doing lately", "help me check the earnings trend", "which sector is strongest recently", "what is northbound capital buying", "export a set of market data for me" into executable workflows for data retrieval, cleaning, comparison, filtering, export and brief analysis. Suitable for research on A-shares, indices, ETFs/funds, financials, valuations, capital flows, announcements and news, sector concepts and macroeconomic data.
Use for any task involving Ultralytics Platform, the ultralytics Python package, yolo CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction, tracking, export, deployment, or the detect / segment / semantic / depth / classify / pose / OBB vision tasks.
Assesses what a page's heatmap is telling you and recommends concrete changes. Pulls click / rageclick / scroll-depth data for a URL, names the hot elements by cross-referencing autocapture events on the same page, and can create a saved heatmap the user opens in PostHog, then summarizes the behavior and proposes improvements. TRIGGER when: user asks what a heatmap shows, why people aren't clicking something, where users rage-click, how far they scroll, what to change on a page based on heatmap/click data, or to 'analyze/assess/review the heatmap' for a URL. DO NOT TRIGGER when: the user only wants to create a saved heatmap screenshot with no analysis (use heatmaps-saved-create directly), or is asking about session replay in general (use investigating-replay).
Expert Keboola Python component developer for ongoing development work — implementing features, extending extractors/writers/applications, adding incremental loads, designing configuration schemas, separating API clients, applying self-documenting workflow patterns, and maintaining code quality with Ruff. Use this skill whenever the user is working on component logic, implementation, or architecture — whether building something new or extending existing code. Do NOT use for initial project scaffolding (that's get-started) or UI-only schema work (that's build-component-ui).
Export PostHog events, persons, sessions, or the results of a HogQL query on demand and download the resulting files. Use when the user asks to download/export raw PostHog data, export HogQL query results, create a one-off file export, fetch a Parquet or JSONLines export, or use the file_download_batch_exports API. Covers starting the export with MCP, polling completion, and downloading via the existing REST redirect endpoint.
Create, update, and manage Omni Analytics documents and dashboards programmatically using the Omni CLI — including document lifecycle, drafts, tiles, visualizations, filters, controls, and layouts. Use this skill whenever someone wants to build a dashboard, create a workbook or document, add tiles or charts, configure dashboard filters or controls, set up a KPI view, lay out or rearrange tiles and pages, edit a dashboard as a draft and publish it, change dashboard settings, rename/move/duplicate/delete a dashboard, modify dashboard-level model customizations like workbook-specific joins or fields, or build and edit an Omni app (custom HTML content in place of a dashboard) — including casual phrasings like “make a dashboard for…”, “add a chart to…”, or “clean up this dashboard's layout” that don't name the skill. Load it before any document API call, even when a local build script exists. For running a query or pulling metrics use omni-query; for adding a field to the shared model use omni-model-builder.
Lovrabet development workflow CLI — manage datasets, database connections (dblink), Data List Pages, Custom Pages, SQL queries, Backend Function scripts, notification settings, enterprise knowledge bases, menu fact reading and syncing, developer roles and collaborators, app files, OCR text and structured-field extraction for ticket-related business documents, code generation, and platform issue reporting with the rabetbase command. Triggers: datasets, dataset relation-audit, dataset delete, dataset restore, deprecated datasets, restore datasets, dataset rename, dataset field-update, dataset extend-update, dataset business-groups, dataset business-group-update, businessGroup, field object update, doType, options, Data List Page, data list page, Custom Page, custom page, page create, page custom-list, page custom-detail, page custom-update, page custom-publish, page generate-start, page generate-status, page relation-audit, page sync, page pull, page push, page restore, restore deleted page, dblink, database connection, schema analysis, db list, db detail, db create, db update, db delete, db test, db tables, db diff, db diff-refresh-start, db diff-refresh-status, db analyze-batch-plan, db analyze-start, analyze-cancel, analyze-status, traceId, custom SQL, sql.execute, bff.execute, BF notifications, Backend Function notifications, notification config-list, EMAIL configCode, notification settings, notification.send, enterprise knowledge base, company knowledge, public knowledge, kb search, get_dataset_detail, validate_sql_content, save_or_update_custom_sql, @lovrabet/sdk, Lovrabet development, rabetbase, filter, codegen, init, appcode, app list, app members-list, tenant members-list, app user list, tenant user list, workspace, workspace init, workspace use, workspace add, workspace remove, multi-app, default app, menu list, menu visibility-update, show menu, hide menu, visible, menu external-link-create, menu external-link-update, menu rename, rename menu, create external-link menu, update external-link URL, menu anomaly audit, manual menu deletion list, menu sync, menu asset-update, update menu assets, menu move, move menu, developer roles, developer collaborators, role, role list, role user-add, role user-remove, user-resolve, join as developer, ADMIN, DEV, OWNER, CUSTOM, project create, project upgrade, schema, jq, compress, file upload, file query-url, ocr recognize, file upload, OCR recognition, ticket text extraction, ticket field extraction, invoice recognition, receipt recognition, license recognition, permanent link, issue report, platform issue, platform issue reporting, user-account dingding-sandbox-bind, DingTalk sandbox account binding.
Complete Switchboard Oracle Protocol SDK for Solana - the permissionless oracle solution for price feeds, on-demand data, VRF randomness, and real-time streaming via Surge. Covers TypeScript SDK, Rust integration, Oracle Quotes, and all Switchboard tools.
Tushare is a professional financial data platform offering A-share market quotes, historical K-lines, financial data, macroeconomic data, index data, and more, with broad coverage and high data quality. Use when users mention Tushare, retrieving A-share data, querying financial statements, downloading macroeconomic data, or needing high-quality, multidimensional financial data. Unlike baostock (free but limited) and jqdatasdk (registration required with a points threshold), tushare offers the most comprehensive data but requires registration to obtain a Token. Free alternatives include baostock or akshare.
Guide for video analysis and frame-level event detection tasks using OpenCV and similar libraries. This skill should be used when detecting events in videos (jumps, movements, gestures), extracting frames, analyzing motion patterns, or implementing computer vision algorithms on video data. It provides verification strategies and helps avoid common pitfalls in video processing workflows.
Use this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer questions about the MaxFrame API, concepts, official examples, and supported pandas APIs; create data processing programs; read and write MaxCompute tables; debug remote or local jobs; and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs or examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.
Create and edit Omni Analytics semantic model definitions — views, topics, dimensions, measures, relationships, query views, composite topics, and aggregate tables (materialized_query) — using YAML through the Omni CLI. Use this skill whenever someone wants to add a field, create a new dimension or measure, define a topic, set up joins between tables, modify the data model, build a new view, add a calculated field, create a relationship, edit YAML, work on a branch, promote model changes, or any variant of "model this data", "add this metric", "create a view for", or "set up a join between". Also use for migrating modeling patterns since Omni's YAML is conceptually similar to other semantic layer definitions.
Use when creating SwiftData custom schema migrations with VersionedSchema and SchemaMigrationPlan — property type changes, relationship preservation (one-to-many, many-to-many), the willMigrate/didMigrate limitation, two-stage migration patterns, and testing migrations on real devices
Expert knowledge for Azure Event Hubs development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use when working with Kafka clients, Flink/Spark, geo-replication, Entra ID/RBAC authentication, Event Hubs Capture/Avro, or other Azure Event Hubs development tasks. Not for Azure Service Bus (use azure-service-bus), Azure Event Grid (use azure-event-grid), Azure Notification Hubs (use azure-notification-hubs), or Azure Web PubSub (use azure-web-pubsub).
Expert knowledge for Azure SQL Managed Instance 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 MI networking, migrations, HA/DR, backup and restore, Entra-integrated security, or other Azure SQL Managed Instance development tasks. Not for Azure SQL Database (use azure-sql-database), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), or Azure Virtual Machines (use azure-virtual-machines).
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop
Generate sample data for Domo — invoke when a user needs realistic sample datasets created and uploaded to a Domo instance. Primary signals: requests for sample, demo, test, or fake data for Domo; mentions of Salesforce, Google Analytics, QuickBooks, NetSuite, Google Ads, Facebook Ads, HubSpot, Marketo, or Health Portal sample data; questions about the datagen CLI or domo_data_generator. Covers generating datasets, uploading to Domo, creating datasets in Domo, rolling dates, entity pools, connector icons, catalog management, and adding dataset definitions. Skip for real connector setup, production data pipelines, data transformations (Magic ETL), or Domo App Platform.
Use when working with @faasjs/pg or PostgreSQL in FaasJS: Tables declaration merging, QueryBuilder, select aliases, parameterized sql expressions, raw SQL, numeric/decimal/bigint boundaries, transaction isolation and read-only modes, row locks, concurrency, migrations, SchemaBuilder, TableBuilder, faasjs-pg CLI, PgVitestPlugin, shared migration snapshots, per-file test database isolation, and database type tests.
Show a GitHub repo's star growth as CLI charts, by day and by hour, in any timezone. Use when the user asks to “check GitHub stars,” “stars by hour/day for <repo>,” “star growth,” “how many stars did <repo> get today,” or shares a repo name and wants its starring activity over time.
Explore and interpret football data, and judge players or teams from stats: rankings, 'who is the best', 'is he a poor finisher', comparisons, headlines and social posts about stats. Use for match events, team and player comparisons and tactical patterns, at the user's level.
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.