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Found 618 Skills
End-to-end data engineering pipeline with Harvard Art Museums API, ETL processing, SQL analytics, and Streamlit visualization
RevenueCat CLI tool for subscription analytics, MRR tracking, customer management, and offering configuration. Use when analyzing app revenue, checking subscriber status, managing offerings/packages/entitlements, querying chart data, setting up webhooks, or any RevenueCat API interaction via CLI. Triggers on "revcat", "RevenueCat", "MRR", "subscription analytics", "revenue metrics", "offerings", "entitlements", "paywalls", or when the user wants to interact with RevenueCat data.
Email marketing automation - campaign creation, sequence building, A/B testing, deliverability optimization, and analytics
Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
Design data architecture at enterprise and solution levels. Cover data mesh, lakehouse, governance, domain-driven design, conceptual/logical/physical data modeling, platform selection, and compliance frameworks. Produce ADRs, data model diagrams, platform comparison matrices, and governance policy templates. Triggers on "design data platform", "choose data warehouse", "data mesh", "lakehouse architecture", "data governance", "data modeling", "platform selection", "data architecture decision", "compliance framework", or "data strategy". For applied AI solution architecture (RAG data plane, embeddings, vector stores in commercial or enterprise products), use applied-ai-architect-commercial-enterprise. For dbt analytics layers and mart delivery, use analytics-data-engineer—not data-architect.
Context layer for data and analytics AI agents with semantic layer, skills, and memory via MCP
Generate and retrieve usage reports for billing, analytics, and reconciliation. This skill provides REST API (curl) examples.
Build end-to-end ETL pipelines and analytics dashboards using Harvard Art Museums API data with Python, SQL, and Streamlit
Help Portaly creators run follower-email campaigns end-to-end — create a draft, send it via Vibe MCP, read post-send analytics — and wire up where the invitation email's CTA redirects (Portaly-hosted waitlist, or a self-hosted /waitlist/[slug] page). Trigger when the user mentions invitation emails, follower outreach campaigns, sending an email blast to followers, drafting an email campaign, waitlist signup landing page, app base URL, embedding a waitlist CTA, or asks how the registration email link works / where it lands.
Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, or feature flags.
Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", or "Lighthouse scores".
Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.