Skill UI

A comprehensive guide for building applications with Claude, ensuring responsible data handling. This skill covers critical patterns such as PII redaction (stripping sensitive data like emails, phone numbers, and card numbers), optimizing the 200K token context window through conversation trimming, and implementing best practices for data retention and privacy compliance when interacting with the Anthropic API.

A persistent memory kit designed for Claude AI agents. It maintains deep context across multiple sessions and projects using a two-layer architecture (hot cache and knowledge wiki). Features include safety hooks, multi-project context isolation, and a /close-day command for daily synthesis, ensuring agents remember complex information and continuously learn.

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

This advanced engine creates deeply personalized, relationship-aware daily gifts using a sophisticated five-stage creative pipeline. It first performs an 'Editorial Judgment' to determine if a gift is warranted, and then generates the content concept. Output can be rendered as interactive H5 web pages, AI-generated images, or videos, ensuring that every artifact is highly unique, contextually relevant, and avoids repetition. Ideal for automated journaling, marking milestones, or maintaining thoughtful daily engagement.

This skill acts as a rigorous, Socratic interviewer designed to stress-test a proposed plan against an established domain model and existing project documentation. It forces the user to sharpen terminology, resolve cross-cutting dependencies, and identify inconsistencies between stated requirements, code implementations, and documented decisions. It ensures the resulting architecture is precise, consistent, and well-documented by automatically updating the context and generating Architecture Decision Records (ADRs).

This skill acts as a specialized prompt engineer for AI design tools. It transforms vague, rough UI/UX ideas into polished, highly optimized prompts. It systematically enhances prompts by injecting missing details such as specific components, design system adherence (colors, typography), page structure, and platform context, ensuring the resulting command is professional and ready for superior AI generation.

FastMCP CLI provides a unified command-line interface to interact with Model Context Protocol (MCP) servers. It allows users to discover available tools and invoke them programmatically using `fastmcp list` and `fastmcp call`. The CLI supports various server targets (HTTP URLs, local files, configs), complex argument types, and ensures structured output, making it essential for building automated, multi-tool workflows and system integrations.

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

This skill serves as a comprehensive entry point for initiating new development work. It systematically analyzes project metadata (specs, roadmaps) to determine the optimal work decomposition path (new spec, existing extension, or simple fix). By conducting deep context loading and guiding structured dialogue, it refines vague ideas into clearly defined, actionable scope boundaries. Finally, it proposes multiple concrete technical approaches, complete with trade-offs and estimates, ensuring the resulting project scope is well-defined and ready for implementation.

This skill orchestrates the entire development lifecycle for approved software tasks. It operates in autonomous or manual modes, rigorously applying Test-Driven Development (TDD). It automatically gathers all necessary context (specs, requirements, design, and historical learnings), validates the repository state, and dispatches specialized sub-agents to implement tasks one by one, ensuring high code quality, adherence to design, and process traceability.