Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Connect an arbitrary REST API to the PostHog data warehouse as a Custom source by authoring a JSON manifest, with no per-source code. Use when the user points at an API that has no built-in PostHog connector — "import data from this REST API", "sync my internal API", "connect this API from its docs", "build a custom data warehouse source" — and gives a docs URL or a natural-language description of the endpoints. Walks through drafting the RESTAPIConfig manifest (auth — bearer, API key, HTTP basic, or OAuth2 client credentials / refresh token — pagination, record path, incremental cursor, parent/child fan-out), validating it, test-reading live rows to verify the field mappings, and creating the source. If the API already has a native PostHog connector, use setting-up-a-data-warehouse-source instead — this skill checks the connector registry first and only handles APIs with no native connector.
Expert knowledge for Azure AI Anomaly Detector development, including troubleshooting, best practices, limits and quotas, configuration, and deployment. Use when tuning Docker-based Anomaly Detector, ACI or IoT Edge deployments, univariate/multivariate APIs, service limits, or other Azure AI Anomaly Detector development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure Monitor (use azure-monitor), or Azure Machine Learning (use azure-machine-learning)
Expert knowledge for Azure AI Custom Vision development, including best practices, decision-making, limits and quotas, security, integrations, coding patterns, and deployment. Use for exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, Smart Labeler, and other Azure AI Custom Vision development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), or Azure AI Foundry Local (use microsoft-foundry-local).
Expert knowledge for Azure Digital Twins development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use when modeling DTDL graphs, integrating IoT Hub/ADx, configuring endpoints/routes, securing access, tuning query units, and for other Azure Digital Twins development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT Central (use azure-iot-central), or Azure IoT Operations (use azure-iot-operations).
Expert knowledge for Azure Stream Analytics 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 ASA jobs with Event Hubs/Kafka, Cosmos/SQL outputs, autoscaling, VNets/private endpoints, or edge/IoT workloads, and for other Azure Stream Analytics-related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), or Azure Event Hubs (use azure-event-hubs).
Query and analyze real-time trending topics and searches on Google Trends for a specified time range and country or region. Trigger this skill when users mention Google Trends, hot topics, real-time trending searches, popular trends, current hot topics, recent trends, viral topics, trends over a time period, regional trend analysis, real-time hot topics, trending topics, popular trends, recent trending searches, or trend discovery. Trigger it even if users do not explicitly say “Google Trends” when they want to discover recent or emerging topics and search terms in a specific market.
Use for Roblox persistent data and cross-server state design: choosing between DataStoreService, OrderedDataStore, MemoryStoreService, and MessagingService; designing save and load flows, schema shape, versioning, metadata, retries, quotas, observability, and concurrency-safe coordination across servers.
Manage the full lifecycle of Alibaba Cloud PAI DSW (Data Science Workshop) instances — create, update, query, list, start, stop, and look up ECS specs. Triggers: PAI DSW, DSW instance, create instance, start instance, stop instance, update instance, query instance, instance list, ECS spec, CreateInstance, UpdateInstance, GetInstance, ListInstances, StartInstance, StopInstance, ListEcsSpecs
Scaffold, build, package, and install a custom visualization into Splunk using the dashboard-studio-extension framework. Use when the user wants to create a new custom viz, add a visualization to an existing project, or migrate a legacy custom viz.
Optimize your Omni Analytics model for Blobby, the Omni Agent—configure ai_context, ai_fields, synonyms, sample_queries, and AI-specific topic extensions. Use this skill whenever someone wants to improve AI accuracy in Omni, make Blobby smarter, add AI context or example questions, curate which fields the AI sees, personalize AI context by user attribute, scope context to a model tier or agent, diagnose context-window pruning or truncation, control which topics AI can reach, troubleshoot why Blobby gives wrong answers, or any variant of "make the AI better", "Blobby isn't answering correctly", "optimize for AI", or "teach the AI about our data".
Expert guidance for SQLite-per-tenant Rails apps using the Basecamp activerecord-tenanted gem. Use when adding multi-tenancy, tenant databases, GlobalRecord, ApplicationRecord.with_tenant, Current.tenant, Current.account, shard swapping, db/global_migrate, or per-tenant SQLite. Do not use for Nebula-style acts_as_tenant row scoping or Fizzy same-database path tenancy.
ADF activity nesting rules, limits, and validation gotchas. PROACTIVELY activate for: (1) ADF activity nesting rules (ForEach inside If, Switch, Until), (2) ForEach limitations (no nested ForEach, item limits), (3) pipeline validation errors and ARM-template build failures, (4) linked service authentication issues, (5) Set Variable restrictions in ForEach, (6) Data Flow constraints (transformation limits, source/sink rules), (7) parameter and variable scope, (8) max activities per pipeline, (9) pipeline concurrency limits. Provides: nesting rule matrix, activity limit reference, validation error catalog, and refactoring patterns for unsupported nesting.
Write, edit, and adapt PostHog Signals scouts: scheduled agents that scan a project and report findings. Use to change a canonical scout's scope, thresholds, schedule, or dry-run settings; add a scout for an uncovered surface; record structured output from scheduled LLM scoring; or give feedback through notes. Covers SKILL.md structure, report requirements, follow-up report checks, structured output, duplicate detection, scratchpad memory, scout notes, per-team skills-store and canonical repository edits, and private prompt trials with saved rubrics. Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>", "score/judge/measure X with a scout", "test/compare scout prompts", "generate scout rubrics", "structured output from a scout", "scout output to Slack", "leave a note for / give feedback to a scout".
Expert knowledge for Azure Data Box development including troubleshooting, best practices, limits & quotas, security, configuration, and integrations & coding patterns. Use when using Data Box/Disk/Heavy for offline data copy, SMB/NFS uploads, REST APIs, CMKs/Key Vault, or NTFS ACL preservation, and other Azure Data Box related development tasks. Not for Azure Import Export (use azure-import-export), Azure Stack Edge (use azure-stack-edge), Azure Virtual Machines (use azure-virtual-machines).
Expert knowledge for Azure Energy Data Services development, including troubleshooting, decision-making, architecture and design patterns, security, configuration, integrations and coding patterns, and deployment. Use when configuring ADME metrics/CORS, choosing tiers, using DDMS/RAFS/Seismic APIs, securing legal tags/ACLs, deploying GCZ on AKS, or handling other Azure Energy Data Services-related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), or Azure Databricks (use azure-databricks).
Expert-level data mesh architecture, domain-oriented ownership, data products, federated governance, and self-serve platforms. Use when the user mentions architecture, domain driven, data products, governance, or platform, or when the task involves Data Mesh Principles, Domain-Oriented Data Ownership, Data as a Product, or Self-Serve Data Infrastructure Platform.
ADF + Databricks 2025 integration patterns. PROACTIVELY activate for: (1) Databricks Job activity in ADF, (2) DatabricksJob (preview) vs DatabricksNotebook activity, (3) ServiceNow V2 connector, (4) ADF managed identity authentication for Databricks, (5) Databricks serverless linked services, (6) Snowflake V2 connector, (7) Databricks job parameters and outputs, (8) MFA enforcement and authentication updates, (9) Unity Catalog integration, (10) Delta Live Tables orchestration from ADF. Provides: Databricks linked service templates (PAT, MSI, serverless), DatabricksJob activity examples, parameter passing recipes, and authentication migration guidance.
Query and analyze Amazon ABA (Brand Analytics) search term data, covering weekly data from the past three years across 15 marketplaces. Trigger this skill when users mention ABA data, Amazon search term analysis, keyword research, search ranking trends, market opportunity analysis, seasonal keywords, high-click low-conversion analysis, blue ocean keyword discovery, competitor keyword analysis, ABA data, search term report, keyword mining, search ranking trends, blue ocean keywords, click share, conversion share, seasonal keywords, market opportunity analysis, or competitor keywords. Trigger it even if the user does not explicitly mention “ABA”, as long as their request involves Amazon search term data and ranking analysis.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps
Expert knowledge for Azure AI Metrics Advisor development, including decision making, limits and quotas, security, configuration, integrations and coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alerts/hooks, or calling Metrics Advisor REST/SDKs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Stream Analytics (use azure-stream-analytics).
Expert knowledge for Azure AI Video Indexer development, including troubleshooting, best practices, decision making, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use when configuring live camera analysis, custom AI insights, OpenAI summaries, Video Indexer APIs, or face redaction, and other Azure AI Video Indexer related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Search (use azure-cognitive-search).
Generates Tianyancha-style company and industry insight dashboards from researched enterprise data. Use when user asks for 天眼查, company lookup, enterprise analysis, hiring analysis, legal risk, financing, shareholders, intellectual property, investments, or industry company comparison.
Use when monitoring Xiaohongshu marketing campaign performance, tracking promotion effectiveness, analyzing advertising ROI, measuring influencer collaboration results, evaluating activity success rates, or optimizing marketing spend allocation
Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces. Use when someone wants to understand what's going wrong with an AI feature, find and categorize failure modes, triage errors, or investigate quality issues (wrong answers, ignored instructions, hallucinations, tool misuse) — "what's failing in my agent", "surface error patterns", "why are the responses bad", "find the common failure modes", "what should I fix next". Covers scoping to one use case, finding failing traces by whichever signal fits the context (code errors, metric outliers, trace-type slices, manual review, existing-eval spikes, clustering), and reading them into a ranked failure taxonomy.
Architect, build, and debug Kafka Streams apps (JVM-embedded stream processing). Use when the user mentions KStream, KTable, topology, TopologyTestDriver, StreamsBuilder, interactive queries, GlobalKTable, joins/windows/aggregations, or debugging issues (rebalancing, state stores, lag, deserialization errors). Also use when the user wants to optimize Kafka Streams for WarpStream or tune Kafka Streams client configuration for WarpStream. Do NOT trigger for Flink, connectors, CDC, or plain producer/consumer.
Retrieve Amazon product details by ASIN, including price, title, main image, listing date, material, weight, variant monthly sales, and monthly sales data for the past 12 months. Trigger this skill when users ask about Amazon product details, ASIN lookups, product pricing, sales rank history, monthly sales trends, product dimensions, FBA fees, product specifications, bulk ASIN lookups, Keepa product details, ASIN detail lookup, monthly sales data, pricing info, product specifications, FBA fees, or batch ASIN queries. Even if the user does not explicitly mention "Keepa," trigger this skill whenever their request involves retrieving structured product data for one or more Amazon ASINs.
PM Skills Marketplace — product-management skills and chained workflows across nine plugins: product discovery, product strategy, execution (PRDs, OKRs, roadmaps, sprints, retros, release notes), market research, data analytics (SQL, cohort analysis, A/B testing), go-to-market, marketing & growth, a PM toolkit, and an AI shipping kit. Encodes proven frameworks for discovery → strategy → execution → launch → growth.
Signals scout for warehouse imports. Watches external data sources, sync schemas, webhook push channels, and materialized views for failures, silent staleness, and row-volume cliffs, and suggests materialization candidates from recurring query-log hot spots.
Comprehensive guide for implementing Syncfusion WPF Charts (SfChart) in Windows Presentation Foundation applications. Use this when working with WPF charts, data visualization, or interactive charting features. This skill covers chart series types, axes configuration, data binding, appearance customization, financial charts, technical indicators, and interactive features like zooming and tooltips for WPF applications.
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.
