Skill UI

Automated help for data analytics visualization best practices, offering step-by-step guidance, industry patterns, production-ready code, and validation for dashboards and reports.

Monitor the adoption, feature usage, and overall Return on Investment (ROI) of AI tools like Windsurf. This skill provides comprehensive analytics, including daily active users, completion acceptance rates, and estimated time saved. It allows teams to set up usage alerts, identify underutilized seats, and build custom extensions to measure deep usage patterns, ensuring maximum team efficiency and quantifying AI impact.

This skill provides comprehensive analytics for Windsurf deployments, allowing teams to track AI feature adoption, measure productivity gains, calculate Return on Investment (ROI), and identify key areas for optimization. Use this when analyzing team usage patterns, generating usage reports, or conducting ROI assessments for AI tools.

A comprehensive development tool for building and managing enterprise-grade transactional email systems. It supports modern React Email and MJML templates, generating production-ready code for major providers like Resend, Postmark, and SendGrid. Key features include multi-provider integration, internationalization (i18n), dark mode, spam score optimization, and advanced analytics tracking. Ideal for setting up new products or migrating legacy email infrastructure.

A comprehensive guide to product analytics, covering essential concepts like event tracking (PostHog, Mixpanel), funnels, cohort analysis, retention metrics, North Star Metrics, and OKR setting. Use this skill to define data-driven product strategies, optimize user journeys, and build robust performance dashboards.

This powerful orchestrator coordinates the entire product development lifecycle by routing inquiries across 16 specialized sub-skills (e.g., RICE, OKRs, UX research, analytics). It acts as a central hub for both deterministic task routing and running continuous, machine-gated discovery loops. Use it when you need to systematically prioritize features, run A/B tests, or drive a goal from initial assumption to validated repository code.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.

A comprehensive automation toolkit for Mixpanel product analytics, accessible via Rube MCP. Automate key data tasks including event aggregation, detailed segmentation, conversion funnel analysis, user profile management, cohort tracking, and running custom JQL queries. Ideal for data analysts and product managers needing to deeply analyze user behavior patterns.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

Automate Amplitude product analytics by orchestrating Composio's Rube MCP toolkit to send events, resolve user activity, manage cohorts, and sync identities while always querying RUBE_SEARCH_TOOLS for current schemas.

This tool opens the dedicated Context Mode Insight dashboard in your default browser. Insight serves as the central analytics layer for AI-assisted engineering teams, providing crucial metrics such as per-engineer productive rates, identifying retry waste, detecting blockers, and offering role-narrowed performance views. It is designed to be the single source of truth for comprehensive team performance monitoring and sign-in details.

A comprehensive guide and code pattern for configuring ClickHouse instances across development, staging, and production environments. It emphasizes best practices for infrastructure-as-code (IaC), secure secrets management (integrating AWS Secrets Manager, Vault, etc.), and ensuring environment-specific settings are correctly applied for reliable data analytics pipelines.