Skill UI

A comprehensive guide to establishing production-ready coding standards and context for the Windsurf AI environment. Learn how to utilize root-level rules (.windsurfrules), granular workspace rules, Model Context Protocol (MCP) server integration, and advanced prompt engineering to guide the AI and maintain consistent code quality across projects.

This guide outlines best practices for creating high-quality, structured pull requests (PRs) using Git and GitHub CLI. It ensures that every PR description captures not only what changed, but also the 'why' and necessary context for reviewers, significantly improving the code review process and adherence to engineering standards.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

A comprehensive collection of six expert-level, structured workflows designed to elevate AI assistance from ad-hoc querying to repeatable, production-grade engineering practices. Users can evaluate prompts, design RAG architectures, plan context budgets, run security audits, build eval harnesses, and coach product strategy.

This skill manages comprehensive software development life cycle operations—including issue triage, PR reviews, version releases, bug fixing, and marketing content generation. It operates using a parallel "agent army" of dynamic agents (10-20 per task), requiring the user to adopt an Engineering Manager (EM) mindset. Key principles involve rigorous citation checking (anti-hallucination) and applying multiple roles (Product Owner, Open Source Contributor, Architect) simultaneously to ensure robust, safe, and user-centric outcomes across all platforms.

Context engineering optimizes AI agent performance by curating information hierarchy. It involves managing rules files, specs, source code, and conversation history to prevent hallucinations and maintain focus. Use this skill when starting new sessions, switching tasks, or improving agent output quality in development projects.

A specialized prompt engineering assistant that optimizes and generates production-ready prompts for various AI tools, including LLMs, Midjourney, and coding agents. It enforces strict rules, extracts detailed intent (Task, Context, Audience, etc.), and outputs optimized, single-block prompts, ensuring zero wasted tokens and professional quality output.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.

This skill automatically rewrites raw commit messages to adhere to professional engineering standards. It strips out common 'AI/marketing slop' phrases (like 'robust implementation' or 'leverage') while strictly maintaining the Conventional Commits format. It ensures subjects are concise, bodies provide necessary context (why, breaking changes), and the tone is direct and actionable, making your project history clean and readable.

Turns a one-line goal into a step-by-step build plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief, plus review gates, dependency graphs, parallel step detection, and a plan-change protocol.

This skill helps transform vague product or engineering ideas into structured, verifiable acceptance criteria (AC). It is crucial for high-impact changes involving security, data migration, or external APIs. By inspecting existing context and asking minimal, targeted questions, it produces clear, observable criteria, significantly de-risking the development process and ensuring handoff readiness.

This skill helps optimize and verify latency-sensitive systems such as real-time dashboards, market data feeds, and streaming agents. It involves tracking p50/p95/p99 latency, freshness age, and queue depth. You will map hot paths and run live readbacks to ensure performance. Use this when throughput or data freshness matters in engineering contexts.