Skill UI

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

This framework moves beyond simple storage solutions (like vector databases) to treat memory as a complex metabolic system. It provides tools and methodologies to audit, price, and govern an agent's memory, focusing especially on the forgetting policy. Key functions include cost profiling (construction vs. query energy), classifying stored records (fact/skill/log), and ensuring a mandatory forgetting policy to prevent state bloat and maintain performance.

This skill assists in designing tenant-scoped memory systems for AI agents, covering retrieval, retention, and storage strategies. It guides planning for RAG, conversation history, and vector stores while ensuring data isolation across sessions. Users learn to establish semantic and episodic records, manage permissions, and test retrieval quality before deployment.

This skill acts as a context agent ensuring continuity between AI coding sessions. It automatically saves session summaries, decisions, and pending tasks to local storage and MEMORY.md. Upon starting a new session, it loads a detailed briefing to restore context. Features include full-text search, archiving, and maintenance tools for managing long-term project memory within the Claude Code environment.

Provides tiered persistent memory patterns for LLM conversations, including short-term, long-term, entity memory, storage, retrieval, and consolidation.