Skill UI

This skill provides a rigorous framework for tackling complex software development tasks. It guides users to break down large features, refactoring efforts, or bug fixes into small, actionable, and independently verifiable steps. By focusing on clear outcomes, dependencies, and specific verification criteria, it ensures that the implementation process is structured, manageable, and traceable from start to finish.

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.

This comprehensive guide details how to properly set up PostHog for multiple environments (development, staging, production). It emphasizes data isolation by utilizing separate projects, allowing granular control over critical analytics features such as session recording rates, autocapture, and feature flag rollout percentages. This ensures accurate and reliable data tracking across all deployment stages, preventing test data from polluting production analytics.

A comprehensive toolkit for the full software development lifecycle. It guides stack selection using a detailed decision engine, automatically scaffolds complex boilerplate projects across multiple popular stacks (Next.js, MERN, FastAPI, Django), and provides deep code quality analysis. Features include security vulnerability detection, complexity scoring, and dependency auditing, ensuring high-quality development from the start.

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 framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

This expert skill specializes in guiding comprehensive GitHub issue resolution. It covers systematic bug investigation, feature implementation, and collaborative development workflows. Expertise includes issue triage, root cause analysis, test-driven development, and professional pull request management, ensuring vague reports are transformed into production-ready code following modern CI/CD practices.

Guides developers through strict test-driven development: write failing tests first, iterate red-green-refactor, and keep tests passing before refactoring to ensure reliable features.

A comprehensive framework guiding feature planning and implementation through adaptive phases: Specify, Design, Tasks, and Execute. It enforces highly structured development practices by using EARS notation, atomic tasks, deterministic Conventional Commits, and mandatory code validation scripts. Features include requirement traceability, automatic verification, and a self-improving lessons layer, ensuring quality and compliance throughout the lifecycle.

This skill provides comprehensive guidance for the entire lifecycle of Conductor tracks—the logical work units used to organize features, bugs, and refactors. It assists users in creating, reviewing, updating specifications (spec.md), developing plans (plan.md), and managing the status and metadata of tasks from initial conception through final completion, ensuring structured and traceable development processes.

Configure the TwinMind AI platform across development, staging, and production environments. This setup ensures secure separation of accounts and manages API keys for robust meeting AI operations, supporting enterprise features like on-premise deployment and SSO integration.

This skill focuses on implementing advanced TypeScript features to build robust, type-safe applications. It covers designing type-first APIs, utilizing generics, implementing custom type guards, branded types, and managing complex state with discriminated unions. It is essential for full-stack development, monorepo setups, and projects requiring maximum type integrity (e.g., integrating with tRPC).