Skill UI

Maintain a review-first engineering wiki with provenance, citation-aware queries, explicit capture and promotion, and deterministic checks. Helps teams capture durable lessons and verify knowledge before turning it into rules.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

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 guide provides expert patterns for implementing secure, full-stack authentication in Next.js using Supabase. It covers essential architectural components, including setting up separate browser and server clients, utilizing middleware for route protection and session refreshing, handling OAuth callbacks, and implementing auth operations safely via Server Actions and Server Components. Essential knowledge for building secure, modern web applications.

This senior product manager agent provides expertise across six knowledge domains including strategy, discovery, planning, execution, analytics, and communication. It applies over 30 proven frameworks like RICE and MoSCoW, utilizes 12 templates for PRDs and roadmaps, and calculates 32 essential SaaS metrics with exact formulas. Ideal for structured product analysis, prioritization, and decision-making without ad hoc brainstorming.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

This skill enables AI agents to search, import, and analyze video content using Memories.ai's Large Visual Memory Model. It provides persistent video intelligence, allowing videos to be indexed once and queried repeatedly. Supports social media import from TikTok, YouTube, and Instagram, meeting summarization, knowledge base building, and cross-video Q&A via Memory Augmented Generation. Ideal for analyzing video URLs, researching social media trends, and summarizing recordings.

This skill verifies code implementations strictly adhere to documentation specifications, especially for blockchain smart contracts. It compares code against whitepapers and design documents to identify gaps, undocumented behaviors, or compliance issues. Ideal for auditing protocol implementations, ensuring deterministic alignment between intended logic and actual code execution without relying on prior knowledge.

Zero-knowledge SSH ops CLI for server health checks, docker/systemd control, log tailing, Postgres introspection, and declarative deploys without exposing credentials to the agent.

This skill transforms the current workspace into a comprehensive, stateful teaching environment. It guides the user through deep, multi-session learning by structuring content into core components: a defined mission, reference materials, curated resources, structured lessons, and continuous learning records. It employs educational best practices (spaced repetition, retrieval practice) to ensure long-term knowledge retention.

Tree Ring Memory is a local-first framework for managing AI agent memory lifecycles. It enables recall, evidence recording, auditing, and forgetting without dumping transcripts. Built with Rust and SQLite, it supports scoped memory, privacy, and durable project knowledge consolidation. Ideal for developers managing AI agent projects securely.