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Found 10 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.
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.
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'.
Multi-agent shared blackboard: The current directory is .yomi/boards/<feature>.md, one board per feature, allowing parallel agents/subagents under the same feature to see each other's tasks, progress, and findings. Use when multi-agent collaboration requires peer visibility, sharing progress/findings, wanting to know what peers are doing before starting work, or when terms like "blackboard / board / peer progress" are mentioned. Task assignment/sign-off/state machine/acceptance are handled via task-tickets, not blackboard.
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.
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.
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.
Build a production behavior-tree runtime (Blackboard, action/condition leaves, sequence/selector/parallel composites, decorators) and a Utility AI system (response curves — linear, exponential, sigmoid, quadratic — considerations, and action evaluators), plus hybrid BT-drives-Utility agents. Use when implementing a reusable behavior-tree or utility-based decision system, or tuning enemy/NPC decisions beyond a simple FSM, or when the user mentions behavior tree, blackboard, decorator, selector, sequence, tick status, utility AI, response/scoring curve, or consideration. For choosing between FSM/BT/steering or for pathfinding, use game-ai; for Unreal's BehaviorTree/Blackboard assets, use unreal-behavior-trees.
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.
Use when using the LimboAI addon — behavior trees and hierarchical state machines (C++ GDExtension) with a visual editor, BTTask subclassing, and a blackboard