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Found 126 Skills
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
Scoped CLAUDE.md memory system that reduces context token spend. Creates directory-level context files, tracks savings via dashboard, and routes agents to the right sub-context.
Manage AI agent memory files (AGENTS.md/CLAUDE.md). Supports update and restructure modes. Use this when users need to sync, update, or restructure agent memory files. Triggered by keywords such as "记忆文件", "memory file", "AGENTS.md", "更新记忆", "重构记忆", "memory sync", "memory restructure".
Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
Agent skill for memory-coordinator - invoke with $agent-memory-coordinator
Portable .agent/ folder with memory, skills, and protocols that works across Claude Code, Cursor, Windsurf, and other AI coding harnesses
Self-improving agent architecture using ChromaDB for continuous learning, self-evaluation, and improvement storage. Agents maintain separate memory collections for learned patterns, performance metrics, and self-assessments without modifying their static .md configuration.
Use when managing project memory, initializing .agent-memory/, saving session learnings, or running memory maintenance. Handles cross-interface persistent memory for any project.
Choose and combine Eve storage primitives to give agents persistent memory — short-term workspace, medium-term attachments and threads, long-term org docs and filesystem. Use when designing how agents remember, retrieve, and share knowledge.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Sets up and drives TencentDB Agent Memory (MemoryCore + MemoryHub + MemoryProxy + MemoryPanel, from AnEntrypoint/agent-memory) -- a persistent, cross-session memory and knowledge system for AI agents. Chat Memory (L0 conversation -> L1 atom -> L2 scenario -> L3 persona), a versioned Skill library extracted from past work, a Wiki + CodeGraph knowledge map over docs and code, and a human-controlled review panel. Use when the user wants an agent team to share and accumulate memory/skills/knowledge across sessions and across multiple agent frameworks (not just this one Claude Code session), when they mention "memory hub", "team memory", "chat memory", "skill library", "wiki", "codegraph", or ask to install/configure/troubleshoot the memory-tencentdb plugin, or when onboarding a new agent into an existing team's accumulated experience. As of gm's tencentdb_backend addition, this system's format can ALSO be gm's own memorize/recall/memorize-fire/memorize-prune backend for an opted-in namespace (see gm.config.json's memory.tencentdb_backend) -- when that's enabled, the verb surface an agent already knows is unchanged; only the storage target moves. Use this skill for the standalone deployment (Docker Compose, panel UI, team review) or when gm's own backend is not what's being asked about.
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.