Frontend

Generates custom design system rules for the user's codebase. Use when user says "create design system rules", "generate rules for my project", "set up design rules", "customize design system guidelines", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.

Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

Connects Figma design components to code components using Code Connect. Use when user says "code connect", "connect this component to code", "connect Figma to code", "map this component", "link component to code", "create code connect mapping", "add code connect", "connect design to code", or wants to establish mappings between Figma designs and code implementations. Requires Figma MCP server connection.

Translates Figma designs into production-ready code with 1:1 visual fidelity. Use when implementing UI from Figma files, when user mentions "implement design", "generate code", "implement component", "build Figma design", provides Figma URLs, or asks to build components matching Figma specs. Requires Figma MCP server connection.

Use this skill to build features or debug anything that uses a webapp frontend.

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Applies the Diataxis framework to create or improve technical documentation. Use when being asked to write high quality tutorials, how-to guides, reference docs, or explanations, when reviewing documentation quality, or when deciding what type of documentation to create. Helps identify documentation types using the action/cognition and acquisition/application dimensions.

Acquire expert Rust developer specialisation in rust systems programming, memory safety, and zero-cost abstractions. Masters ownership patterns, async programming, and performance optimisation for mission-critical applications.

Provides guidelines for applying Mantel's brand styles to diagrams and frontend components. Use when asked to create visuals that need to align with Mantel's branding.

Guide for testing and verifying code changes in this TUI application. Use this skill after making ANY code changes to main.go or main.md to verify they work correctly.

Provides guidance on software architecture patterns and design decisions. Use when designing systems, choosing patterns, structuring projects, or when asked about architectural approaches.

Guides performance analysis and optimization for any application. Use when diagnosing slowness, optimizing code, improving load times, or when asked about performance.

Instructs AI agents on documentation standards for design docs, folder READMEs, source code interfaces, and test cases

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

Design thinking and decision-making for web UI. Use when designing components, layouts, color schemes, typography, or creating aesthetic interfaces. Teaches principles, not fixed values.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Modern React patterns and principles. Hooks, composition, performance, TypeScript best practices.

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents. Use for creating new applications, scaffolding projects, or building features from scratch.

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.