Skill UI

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

This tool acts as a centralized orchestrator for the entire Go development lifecycle. It analyzes the current task context—whether designing an API, debugging a panic, or building a gRPC service—and intelligently loads multiple, highly relevant skills simultaneously. It ensures developers have comprehensive, context-aware support for complex workflows, handling everything from architecture and error management to performance tuning and security auditing.

A comprehensive skill for generating new design screens from text prompts or images, and iteratively editing existing designs. It incorporates advanced prompt enhancement, design tokens, and professional UI/UX terminology to ensure professional, structured, and context-aware design output for web and mobile platforms.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

A comprehensive guide for implementing spatial, mixed-reality user interfaces. Inspired by Apple Vision Pro and AR aesthetics, this pattern uses environmental transparency, blurred glass-like panels, and dynamic lighting effects to create deeply integrated, context-aware layouts for web and mobile applications.

This guide details how to implement Identity-Aware Proxy (IAP) using gcloud. It enforces per-request identity verification and context-aware access policies (e.g., device posture, location) across major GCP backends, including Compute Engine, App Engine, Cloud Run, and GKE. It is essential for migrating from traditional VPN access to a robust, zero-trust, identity-based security architecture for cloud applications.

This skill manages the end-to-end operational workflow for corporate email communication. It is focused on the mechanics of the mailbox itself, allowing users to triage inboxes, draft replies, send messages, and verify successful delivery. It ensures that all actions are context-aware, adhering to brand voice, and providing verifiable proof of the message's final status (e.g., Sent or Drafted).

This skill acts as an advanced orchestration protocol for reviewing GitHub pull requests. It is stack-agnostic, meaning it does not enforce its own rules. Instead, it dynamically discovers the project's existing standards—such as the test runner, architectural conventions, and requirement specifications—before running six specialized subagents in parallel. It provides a comprehensive, context-aware review and summary directly on GitHub.

Performs advanced semantic search across all stored brand knowledge, including guidelines, campaign learnings, and competitor insights. By combining vector databases, knowledge graphs, and local indices, it provides comprehensive, context-aware answers to complex natural language questions, ensuring users make decisions based on the brand's entire historical knowledge base.

This skill is a comprehensive strategic guide for authors targeting the ACM Conference on Recommender Systems (RecSys). It helps assess the fit of a manuscript, guides the re-framing of work from journal or arXiv styles into a specialized conference narrative, and outlines specific requirements for evidence, evaluation (including offline metrics, user context, and leakage-aware methods), and contribution framing necessary for top-tier CS venues.

Optimize multi-agent systems through coordinated profiling, workload distribution, and cost-aware orchestration. This skill helps improve agent performance, throughput, and reliability by identifying bottlenecks in workflows. It covers context window optimization, parallel execution strategies, and cost management for LLM usage. Suitable for engineering complex AI workflows requiring high efficiency and stability.

Guidance for designing system experience components per Apple Human Interface Guidelines: widgets, live activities, notifications, complications, home screen quick actions, top shelf, watch faces, app clips, and app shortcuts. Helps deliver glanceable, context-aware UI across platforms.