Skill UI

Provides a comprehensive workflow for managing feature flags, running A/B/multivariate experiments, and analyzing user cohorts using PostHog. The guide covers implementing flag evaluation both client-side (browser) and server-side (Node.js), and demonstrates how to programmatically set up, track, and query experiment results via API calls (cURL/Bash).

Automates the collection of comprehensive diagnostic evidence for PostHog support tickets. This bundle gathers runtime environment details (Node, Python versions), SDK versions, API connectivity status to US/EU clouds, feature flag settings, event flow testing results, and redacts sensitive environment variables. It provides a structured, support-ready archive for rapid troubleshooting.

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 guide provides a comprehensive workflow for setting up a robust local development environment for PostHog integrations. It covers enabling debug mode for real-time event inspection, implementing mocking strategies for stable unit testing, and configuring integration tests using a dedicated development PostHog project. Use this when building or testing analytics tracking features with posthog-js or posthog-node to ensure fast, isolated development cycles.

A comprehensive guide for safely upgrading PostHog SDKs (for JavaScript and Node.js). This resource details critical breaking changes across major versions, such as the explicit requirement for `sendFeatureFlags` in v5.x and the new structured autocapture API for PostHog.js. It provides a structured, step-by-step procedure covering version auditing, code modification, testing, staging validation, and rollback plan.

This skill simulates an autonomous, overnight development cycle by leveraging Git hooks to enforce Test-Driven Development (TDD). It ensures that all code changes, whether building new features, refactoring, or fixing bugs, are rigorously tested and meet high quality standards before commitment. Use this when high code reliability and comprehensive testing are critical requirements.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

This guide outlines critical reliability patterns for integrating advanced AI coding assistants into professional development workflows. It emphasizes best practices such as mandatory Git checkpointing, utilizing dedicated feature branches, breaking large tasks into incremental steps, and implementing comprehensive validation gates (linting, testing). Use this when developing fault-tolerant, AI-assisted applications to prevent accidental code breaks and maintain project stability.

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

AWT provides AI coding tools with the ability to perform sophisticated End-to-End (E2E) web testing using a real browser. It accepts declarative YAML scenarios and executes them via Playwright. Key features include visual matching (OpenCV + OCR) instead of relying on fragile DOM selectors, automatic platform detection (React, Vue, etc.), and structured failure diagnosis for improved AI coding workflows.

A comprehensive guide on utilizing Firebase Remote Config within Flutter applications. Learn how to implement dynamic feature flags, execute A/B testing, manage remote parameter defaults, and fetch real-time configuration values. This skill allows developers to modify app behavior and user experiences without requiring an app store update, ensuring fast iteration and efficient product testing.

Master the art of creating, maintaining, and running robust end-to-end (E2E) tests for Flutter applications using the Patrol framework. This skill covers validating complex user journeys, testing interactions with native system features (like permissions and deep links), capturing UI regressions, and ensuring consistent cross-platform behavior across Android, iOS, and Web. Best practice guidelines for deterministic testing are included.