Skill UI

A comprehensive guide to product analytics, covering event tracking, funnels, cohort retention, north star metrics, OKRs, and dashboards. Examples include PostHog implementation and Python code for retention analysis. Ideal for product managers and data analysts seeking data-driven decisions.

This skill guides the construction of robust, production-grade backtesting systems for financial trading strategies. It helps users systematically validate performance, avoid common biases (like look-ahead bias), and implement advanced techniques such as walk-forward analysis, ensuring reliable performance estimates before live deployment.

This skill provides specialized guidance and structured patterns for conducting scientific research and analysis tasks. It helps users formulate hypotheses, design experiments, analyze data, and interpret results within a scientific context. Use this when the task involves rigorous scientific methodology, literature review, or technical analysis requiring expert-level reasoning. It is not a substitute for professional validation or safety checks.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation. This skill covers exploratory data analysis, hypothesis development, experiment design, feature architecture, analytics instrumentation, and implementation with integrated tracking and monitoring.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Guides the full workflow from exploratory data analysis to production deployment, covering experimentation, forecasting, NLP, and business intelligence for actionable insights.

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

Guides investigation of data incidents like freshness delays, volume anomalies, schema changes using Monte Carlo MCP tools. Combines observability metadata with direct data querying to find root cause.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

Guides agents through bounded parameter optimization for configurable systems with measurable objectives: define search space, budget, and baseline, run trials one at a time, log evidence, and recommend a configuration for HPO, inference tuning, simulations, or RL/control experiments.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

This skill analyzes skin health data to identify problem patterns and assess overall status. It supports correlation analysis with nutrition, chronic diseases, and medication data. Users should consult the detailed guide before execution to ensure safety and accuracy.