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Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Consistent agent behavior after restart — progressive identity loading, working context reconstruction from persistent artifacts, fresh-vs-continuation detection, calibration through centering and attunement, and identity verification for coherence. Addresses the cold-start problem where an agent must reconstruct who it is and what it was doing from evidence rather than memory. Use at the start of every new session, after a session interruption or crash, when agent behavior feels inconsistent with prior sessions, or when persistent memory and current context appear contradictory.
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Agent-agnostic long-term project memory through `.memory-bank/` + RULES (TDD/SOLID/Clean Architecture/FSD/Mobile) + dev-toolkit commands. Use when working in a project with a `.memory-bank/` directory or when the user explicitly asks for memory-bank workflow, code rules, or dev-toolkit commands (`/mb`, plan, spec, verify, done; «память проекта», «контекст проекта», «план», «продолжай по плану»).
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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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Bootstrap new 33GOD ecosystem agents with standardized configuration. Use when creating new agents, spawning workers, deploying Yi nodes, or when the user says "spin up an agent", "create a new agent", "deploy a new worker", or needs a new agent for a specific pipeline role. Handles workspace creation, config injection, skill installation, provider mirroring, channel binding, and 33GOD ecosystem onboarding (GOD Docs, Plane, Bloodbank, memory).
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Synapse A2A agent communication — sending messages, spawning agents, delegating tasks, sharing memory, and coordinating file edits. Use this skill when: running synapse send/reply/broadcast/interrupt, spawning agents with synapse spawn or synapse team start, managing the task board with synapse tasks, sharing knowledge with synapse memory, locking files with synapse file-safety, checking agent status with synapse list/status, or orchestrating any multi-agent workflow.
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Guides AI agents in using Tree Ring Memory for durable recall, project decisions, user preferences, warnings, future seeds, privacy-safe memory capture, and lifecycle-aware forgetting.
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Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.
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You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.
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Tidy an agent's always-loaded memory index (MEMORY.md) on macOS or Linux: trim long lines, move closed entries to an archive, fold domains into hub files, restore dropped pins and add pointers for orphan memory files. Use when the index is over budget or has orphans. Triggers: /memory-tidy, /mt, tidy memory.
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F Agent Memory: A Git-Native Persistent Memory Solution for AI Coding Agents and Project Knowledge Management OKF Agent Memory introduces a standardiz
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アーキテクチャ知識グラフ(memory MCP / memory.jsonl)の初期作成。「知識グラフを初期化して」「memory グラフを作って」「アーキテクチャグラフを登録して」といった依頼、またはプロジェクト初期構築完了後の仕上げとして使用する。コードベースを調査し、モジュール構成と依存関係を entities / relations として登録する。
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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool ...
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Agent Orchestrator v2 IA — Expert en orchestration multi-agents (tool routing, mémoire, planning, ReAct, orchestration de workflows IA)
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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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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.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。