memory▌

Use this skill as the single memory system for this repository.

Persistent, searchable memory system for agents to store and retrieve architectural decisions, patterns, and project knowledge. \n \n Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write to record decisions and patterns, memory_read to retrieve specific entries, and memory_stats for usage analytics \n Runs as an MCP server that syncs with project documentation, enabling long-term knowledge retention across agent sessions \n Includes a standalone dashboard (por

Multi-layer memory architecture for agents: short-term context, long-term vector storage, and retrieval optimization. \n \n Covers seven memory types: short-term (context window), long-term (vector stores), working, episodic, semantic, and procedural memory, each suited to different information patterns \n Provides three core patterns: memory type selection, vector store choice, and chunking strategy to maximize retrieval accuracy \n Highlights critical retrieval challenges: contextual chunking,

Two-tier memory system that decodes workplace shorthand, acronyms, and internal language for contextual understanding. \n \n CLAUDE.md serves as a hot cache of ~30 frequent contacts, common terms, and active projects; memory/ directory stores the complete glossary, detailed profiles, and project context \n Tiered lookup flow checks CLAUDE.md first (covers 90% of daily decoding), then searches memory/glossary.md, then asks the user for unknown terms \n Supports progressive disclosure: quick parsi

Production-grade long-term memory system (v1.1.0-beta.8) for OpenClaw AI agents. Provides persistent, intelligent memory storage using LanceDB with hybrid vector + BM25 retrieval, LLM-powered Smart Extraction, Weibull decay lifecycle, and multi-scope isolation.

Streamline Claude Code memory files through 4 interactive optimization steps. \n \n Removes duplicate entries already present in CLAUDE.md or rules files, then migrates remaining entries to appropriate persistent config locations \n Compresses remaining memory entries using LLM-efficient formatting (prose to tables, verbose descriptions to imperatives) \n Validates final state by checking for broken references, contradictions, and orphaned files \n Typical reduction of 30–50% token count in memo

Skill by ara.so — Daily 2026 Skills collection.

Memory management CLI for AI agents. Organize, search, and maintain your memory files.

Agents on Eve Horizon have no built-in "memory" primitive, but the platform provides storage systems at every timescale. This skill teaches how to compose them into coherent memory for agents that learn, remember, and share.

You remember. Not everything—but enough to feel continuous.