Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.
--- name: infinite-memory version: 1.0.0 description: High-precision memory with 100% recall accuracy for long contexts. emoji: 🦞 metadata: clawdbot: requires: bins: -
Recall memories from MemOS Cloud before responding. Use this skill when you need context about user's previous conversations, preferences, or decisions.
Persistent memory system for AI agents to remember facts, learn from experiences, recall memories, and track entities across sessions.
Structured AI agent memory with categorized storage, MD5 duplicate detection, consolidation, keyword recall, and export in Markdown or JSON formats.
Store and recall user-specific facts across conversations with a structured knowledge graph. Add, relate, and search information about people, organizations, events, and preferences to maintain consis
Persistent memory system for AI agents — daily logs, long-term memory, identity files, and heartbeat-driven recall. Solves context amnesia across sessions.
Adds intelligent long-term memory to agents for auto-capturing, recalling, and managing user facts and preferences across sessions.
--- name: ctxly version: 1.0.0 description: Cloud memory for AI agents. Store, search, and recall context across sessions. homepage: https://ctxly.app metadata: {"emoji": "🧠", "category": "memory",
Resume multi-session projects with full context retention. Use when returning to previous work, needing conversation history, recalling past decisions, or br...
Three-layer persistent memory system (Markdown + ChromaDB vectors + NetworkX knowledge graph) for long-term agent recall across sessions. One-command setup w...
Manage and update a layered memory system using daily files as source, indexing by domain/module/entity, extracting critical facts, and maintaining recall ef...
Track and recall your daily activities including git commits, web browsing, shell commands, and VS Code edits. Use this skill whenever the user asks about th...
Local semantic memory with Qdrant and Transformers.js. Store, search, and recall conversation context using vector embeddings (fully local, no API keys).
Hybrid memory strategy combining OpenClaw's built-in vector memory with Graphiti temporal knowledge graph. Use when you need to recall past context, answer temporal questions ("when did X happen?"), o
The best way to access Telegram message history. Use this skill whenever the user asks about their Telegram messages, chats, DMs, or groups — search, summarize, extract action items, recall conversa
MongoDB-backed long-term semantic memory for recalling, storing, searching, and managing facts, decisions, and user preferences across sessions.
Cloud-persistent memory for AI agents. Stateless plugins + TiDB Serverless = cross-session recall, multi-agent sharing, and hybrid vector + keyword search. W...
Cloud memory for AI agents. Store, search, and recall context across sessions.
Intelligent agent memory with semantic recall, automatic consolidation, contradiction detection, and bi-temporal knowledge graph. 80% on LOCOMO benchmark using 96% fewer tokens than full-context appro
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
Provides OpenClaw agents with local, scene-aware, persistent structured memory for task deduplication and long-term workflow recall.
Personal knowledge base powered by Ensue for capturing and retrieving understanding. Use when user wants to save knowledge, recall what they know, manage their toolbox, or build on past learnings. Tri
Provides a highly visual and interactive dashboard for OpenClaw users to easily understand and recall the functionalities of installed skills, featuring a vi...