83건 · 관련도순
Evaluates task complexity and selects a small set of existing skills for work spanning multiple areas. Uses agent-memory-mcp to record and retrieve successful combinations, while keeping simple tasks within the agent’s ordinary capabilities.
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Install and configure the memory-tencentdb plugin for OpenClaw to retain and retrieve conversation information. Verify the setup through logs and searches, adjust memory settings, and troubleshoot missing recall or vector search results.
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Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Agent Memory Systems workflow skill. Use this skill when the user needs "Memory is the cornerstone of intelligent agents. Without it, every and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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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.
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Preserve project decisions and work progress across sessions, and share information between agents. Organize stored knowledge into separate spaces, retrieve relevant records, and manage expiration, compression, and cleanup of saved data.
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Give an agent rules for consulting long-term memory before work and saving decisions, corrections, and failures afterwards. It helps carry preferences and project conventions across sessions while preserving dated history and making conflicting memories visible.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
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antigravity-skill-orchestrator workflow skill. Use this skill when the user needs A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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antigravity-skill-orchestrator workflow skill. Use this skill when the user needs A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Agent Memory Systems workflow skill. Use this skill when the user needs "Memory is the cornerstone of intelligent agents. Without it, every and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Design memory systems for AI agents. TRIGGERS - Use when user needs help with ai-agent-memory related tasks.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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Expert en systèmes de mémoire pour agents IA (court terme, long terme, épisodique, sémantique, vector retrieval, RAG)
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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Design memory systems for AI agents. TRIGGERS - Use when user needs help with ai-agent-memory related tasks.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.
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Design agent memory that preserves facts, preferences, and conversation history across sessions. Plan storage, retrieval, and retention separately for each organization or user, with checks for unauthorized access, outdated information, conflicting records, and retrieval quality.
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