Skill UI

This skill guides the development of EarLLM One, a complex Android application. The app captures real-time audio from Bluetooth earbuds, transcribes the voice using STT, sends the text input to a Large Language Model (LLM) via a robust pipeline, and plays the response back using TTS. It requires deep knowledge of Kotlin, Android development, Bluetooth profiles (A2DP/HFP), and advanced audio routing.

This skill guides users on setting up automated code quality gates directly at the Git level. It covers best practices using frameworks like Husky, lint-staged, and pre-commit. Users can enforce linting, formatting, type-checking, and standardized commit message conventions (e.g., Conventional Commits) before code is committed or pushed, significantly improving code hygiene and development workflow consistency.

This skill simulates a highly advanced 'Board of Experts,' consulting multiple specialized personas (e.g., Musk, Buffett, Jobs) simultaneously. It gathers distinct, multi-perspective analyses on any complex topic—from engineering feasibility to market strategy—and synthesizes them into a single, comprehensive, and actionable decision. Ideal for complex strategic planning, product development, and high-stakes investment decisions.

This comprehensive index covers 37 advanced engineering skills designed for complex system architecture, platform operations, and reliability. Skills include agent design, RAG implementation, CI/CD, observability, database modeling, and security auditing, supporting the entire development lifecycle for powerful enterprise applications.

This comprehensive index provides access to 32 production-ready, specialized engineering skills. It covers the entire development lifecycle, including system architecture, frontend, backend, DevOps, security operations, AI/ML, and data engineering. It is designed to guide users in selecting and loading only the specific module needed, ensuring focused expertise for complex tasks.

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.

This skill is intended for Perl testing functionalities. Currently, the Japanese translation and content are under development, serving as a placeholder. Users should refer to the original English skill file for the core functionality and detailed implementation.

This skill provides sophisticated project estimation by adapting to human-only, hybrid, or agent-first development modes. It uses research-backed PERT statistics and confidence bands (P50, P75, P90) to forecast development timelines for tasks, backlogs, and capacity planning. It continuously calibrates estimates based on actual completion data, ensuring accuracy in AI-assisted and hybrid development environments.

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

This skill manages complex coding tasks from initial implementation through testing, review incorporation, deployment, and final runtime verification. It ensures the task meets explicit Definition of Done (DoD) criteria iteratively, minimizing the need for manual prompting across multiple development stages.

This skill enforces a mandatory, structured gate for starting new implementation tasks. It mandates that all work must be tracked via a GitHub issue, requiring explicit, testable acceptance criteria before any execution can begin. It defines a strict workflow (Problem, Goal, Scope, Acceptance Criteria, Status) to manage task readiness, ensuring that development only proceeds when criteria are fully defined and verifiable.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.