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Found 7 Skills
Use this skill when implementing AI, AIController, behavior tree, blackboard, AI perception, NavMesh, EQS, navigation, pathfinding, State Tree, or Smart Objects in Unreal Engine. See references/behavior-tree-patterns.md for BT patterns and references/eqs-reference.md for EQS configuration. For AI ability use, see ue-gameplay-abilities.
Authors MSW `.behaviourtree` files end-to-end and maintains the project-specific authoring spec (`.behaviourDocs/bt-spec.md`). Scans every `.codeblock` whose paired `.mlua` extends `ActionNode`/`DecoratorNode` to build a compact catalog of custom action/decorator UUIDs, propertyKey names, and version-stamped MODNativeType strings. Then generates the full tree: RootNode → Nodes graph, Blackboard variables, nodeProperties wiring, and self-validates parent/child consistency. Triggers: 'create behaviourtree', 'new BT', 'add a behaviour tree', 'BT node graph', '비헤이비어 트리 만들어', '.behaviourtree 생성', 'SequenceNode SelectorNode', 'Blackboard variable', 'definitionId codeblock', 'startNodeId', 'build BT spec', 'refresh bt-spec', 'generate behaviourtree catalog', 'BT 스펙 생성', 'bt-spec.md 만들어', 'rescan BT nodes'.
Build NPC AI in Unreal Engine 5 with Behavior Trees and Blackboards: composites (Selector/Sequence), tasks, decorators, services, and running the tree from an AIController. Use when creating enemy/NPC AI, BT_/BB_ assets, custom BTTask or BTService nodes, or when the user mentions Behavior Tree, Blackboard, AIController, BTTask, decorator, or service.
Building modular, debuggable AI behaviors using behavior trees for game NPCs and agentsUse when "behavior tree, bt, npc ai, ai behavior, game ai, decision tree, blackboard, ai, behavior-trees, npc, game-ai, decision-making, agents" mentioned.
Inter-agent communication patterns including message passing, shared memory, blackboard systems, and event-driven architectures for LLM agentsUse when "agent communication, message passing, inter-agent, blackboard, agent events, multi-agent, communication, message-passing, events, coordination" mentioned.
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Design NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A* pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the user mentions state machine, behavior tree, blackboard, A*, navmesh, seek, or patrol/chase.