Skill UI

This guide provides minimal working examples demonstrating the three core PostHog functionalities: event capture, user identification, and feature flag evaluation. It covers both server-side (Node.js, Python) and client-side (JavaScript) SDK usage, as well as direct API calls, making it perfect for initial integration setup, testing, and learning basic product analytics patterns.

This comprehensive guide outlines the process of migrating analytics data from major platforms like Google Analytics, Mixpanel, Amplitude, or Segment to PostHog. It covers implementing a dual-write strategy, mapping historical event names and properties, and performing bulk historical data imports, ensuring a smooth transition and data continuity.

This comprehensive guide outlines advanced strategies for optimizing PostHog in production environments. Key techniques covered include implementing local feature flag evaluation to eliminate network latency, optimizing event batching for network efficiency, utilizing strategic event sampling, and writing efficient HogQL queries. Master these methods to dramatically improve data collection reliability and query speed.

This skill guides you through integrating PostHog's Customer Data Platform (CDP) features. It covers setting up webhooks to trigger real-time actions (e.g., syncing to CRM, sending notifications) when specific user events occur. Furthermore, it demonstrates how to query event data using the Events API and perform complex funnel analysis using HogQL, allowing for deep, custom insights into user behavior.

Guides and automates Prefect Flow Builder tasks for data pipelines, offering best-practice patterns, production-ready code, configuration templates, and validation advice whenever you mention the tool or request pipeline help.

This skill builds and executes automated pipelines to clean, validate, and transform data for machine learning, covering ETL workflows, error handling, and quality reporting so Claude can deliver analysis-ready datasets.

Automated assistance for pyspark transformer work within data pipelines, activating when you request transformer patterns or best practices and covering ETL, data transformation, orchestration, and streaming scenarios.

This skill simulates the role of a quantitative analyst, providing comprehensive tools for building complex financial models, backtesting trading strategies, and analyzing market data. It covers advanced topics like portfolio optimization (Markowitz, Black-Litterman), calculating risk metrics (VaR, Sharpe ratio), and performing statistical arbitrage. Ideal for quantitative finance professionals needing rigorous data analysis, robust backtesting, and risk-adjusted performance evaluation.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

Provides automated regression analysis guidance, best practices, code snippets, and validation for SQL, BI, visualization, and statistical workflows in data analytics projects.

Automates report template creation within data analytics by guiding SQL, visualization, statistics, and BI best practices, generating ready-to-use configurations and validating outputs.

Automated retention calculator guidance within data analytics workflows, assisting with SQL, visualization, BI, and statistical analysis to produce and validate production-ready outputs for retention insights.