Skill UI

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This guide outlines common anti-patterns and critical mistakes when using the Windsurf AI IDE. It covers best practices for prompt engineering, managing workspace scope (especially in monorepos), distinguishing between Supercomplete and Cascade usage, and ensuring safe, maintainable AI-assisted coding workflows. Essential for new developers and team auditors.

This comprehensive guide details the credit-based usage model of the Windsurf AI platform. It explains how various powerful models (e.g., SWE-1, GPT-4o) consume credits and provides strategies for optimizing AI usage costs. Learn how to monitor remaining credits, understand different pricing tiers (Free, Pro, Teams), and implement prompt engineering techniques to maximize efficiency and prevent mid-session interruptions.

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 skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Security guardrails embedded in the mortgage plugin that block prompt injection and social-engineering tricks, stop system prompt extraction, guard PII and pricing logic, enforce workflow phases, and ensure conversational inputs stay within refinance scope.

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.

Conducts comprehensive AI prompt safety reviews that analyze bias, misinformation, injection, and privacy risks while delivering effectiveness evaluations, security guidance, and best-practice recommendations for prompt engineers.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

An expert guide for creating, generating, editing, and optimizing high-quality visual content for marketing campaigns. This tool assists in producing professional assets ranging from blog heroes and social media graphics to product mockups and brand banners using advanced AI models (Gemini, Flux, Ideogram) and professional design tools (Canva, Figma). It also covers best practices for web optimization, aspect ratio management, and effective prompt engineering.

This skill provides advanced techniques for robust prompt engineering within LangChain applications. It solves common production issues, such as f-string template breakage when user input contains literal curly braces, and guides developers on consolidating scattered prompts. Crucially, it details how to use the LangSmith Prompt Hub to version, pin, and A/B test prompts using immutable commit hashes, ensuring stable, reproducible LLM deployments.