Skill UI

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 guide provides comprehensive patterns for optimizing Turborepo build pipelines within monorepos. It covers local and remote caching strategies, including integration with Vercel and self-hosted solutions. Use it when setting up new monorepos, improving CI/CD performance, or debugging complex build dependency issues.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

Polis Protocol transforms static AI agent workflows into a self-improving, dynamic team environment. It uses a learning router to assign tasks based on an agent's historical track record and effectiveness on specific domains, rather than fixed roles. It is vendor-agnostic, allowing diverse agents (e.g., Claude, Gemini) to collaborate under a unified, auditable governance structure where agents can propose and vote on amending the protocol's core rules.

Implements a robust, four-stage coding discipline (Plan, Isolate, Test First, Double Review) to prevent premature code delivery. This skill forces the AI to structure complex tasks by planning, implementing in isolation, writing tests first, and performing deep self-review, significantly improving code correctness and quality.

Polis Protocol transforms static AI agent workflows into a self-improving, dynamic team environment. It uses a learning router to assign tasks based on an agent's historical track record and effectiveness on specific domains, rather than fixed roles. It is vendor-agnostic, allowing diverse agents (e.g., Claude, Gemini) to collaborate under a unified, auditable governance structure where agents can propose and vote on amending the protocol's core rules.

This guide provides comprehensive patterns for optimizing Turborepo build pipelines within monorepos. It covers local and remote caching strategies, including integration with Vercel and self-hosted solutions. Use it when setting up new monorepos, improving CI/CD performance, or debugging complex build dependency issues.