Implement GOAP and 3-state FSM patterns for Unity agent AI.
npx skills add https://github.com/rpillai25/PitHero --skill nez-ai
Please help me install this Agent Skill. Skill: nez-ai Source: https://github.com/rpillai25/PitHero/tree/main/.claude/skills/nez-ai Command: npx skills add https://github.com/rpillai25/PitHero --skill nez-ai
Nez AI best practices and PitHero conventions for building robust AI systems. It consolidates GOAP planning, simple/complex state machines, and optional behavior trees to reduce integration friction and ensure consistent AI architecture across heroes and mercenaries.
Instantiate the HeroStateMachine and MercenaryStateMachine, register GOAP actions, and start the planning loop to enable AI-driven decisions in PitHero-style agents.
High-intent search queries and answers about installing and using this skill.
To implement GOAP and FSM for Unity AI agents, instantiate the HeroStateMachine or MercenaryStateMachine, register your GOAP actions, and start the planning loop to automate decision-making and action sequencing. This pattern consolidates goal-oriented planning with a 3-state finite state machine for heroes and mercenaries.
Pathfinding integration in PitHero-style agents connects GOAP actions with a GoTo state, handling tile-by-tile movement mechanics. This ensures AI plans execute correctly across the game map while maintaining consistent navigation architecture for heroes and mercenaries.
Yes, you can use IGoapContext and VirtualGoapContext for offline virtual testing of AI decision-making logic. This allows you to validate GOAP plans, world state integration, and goal state conditions without running the full Unity game environment.
The best way to register GoapConstants and actions for hero AI is to instantiate the HeroStateMachine, register specific GOAP actions through the planning loop, and integrate world and goal states. This satisfies GoapConstants usage requirements and enables automated action sequencing.
Yes, the AI pattern supports both simple and complex state machines for mercenaries through the MercenaryStateMachine. It consolidates GOAP planning with 3-state FSM architecture, ensuring consistent AI behavior design and reducing integration friction across different agent types.
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