# Behavior Trees, Utility AI

Builds behavior-tree and utility-AI decision systems for game agents.

- Repository: https://github.com/gamedev-skills/awesome-gamedev-agent-skills (6445 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-10-28
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add gamedev-skills/awesome-gamedev-agent-skills --skill ai-behavior-trees-utility-ai -g -a claude-code -y`
Codex: `npx skills add gamedev-skills/awesome-gamedev-agent-skills --skill ai-behavior-trees-utility-ai -g -a codex -y`
Cursor: `npx skills add gamedev-skills/awesome-gamedev-agent-skills --skill ai-behavior-trees-utility-ai -g -a cursor -y`
Gemini CLI: `npx skills add gamedev-skills/awesome-gamedev-agent-skills --skill ai-behavior-trees-utility-ai -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add gamedev-skills/awesome-gamedev-agent-skills`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Behavior Trees & Utility AI

Two complementary ways to structure NPC decision-making, plus how to combine them. A
**behavior tree (BT)** expresses *structured, prioritized, reactive* logic as a tree that is
"ticked" each step. **Utility AI** answers *"how much do I want each option right now?"* by
scoring actions with normalized curves and picking the best. Ship believable agents by using a
BT for structure and Utility AI where graded trade-offs matter.

This skill is the **implementation** companion to `game-ai` (which helps you *choose* between
FSM / BT / steering / pathfinding). Read `game-ai` to pick a model; read this to build the
runtime.

## When to use

- Use to build a **reusable BT runtime**: a `Blackboard`, `Node` base, action/condition leaves,
  `Sequence`/`Selector`/`Parallel` composites, and decorators (Inverter, Cooldown, Repeat).
- Use to build a **Utility AI** decider: response curves, considerations, and an evaluator that
  scores and selects actions (max, softmax, or weighted-random for variety).
- Use to build **hybrid AI**, a BT whose leaf delegates the "which attack / which target"
  choice to a utility evaluator.

**When *not* to use:** to *choose* between FSM, BT, steering, or pathfinding, and for A*/navmesh
routing, use `game-ai`. For Unreal's asset-based `BehaviorTree`/`Blackboard`, `BTTask`/`BTService`
and `AIController`, use `unreal-behavior-trees`. For the navmesh agent that *moves* the NPC, use
`unity-navmesh` or the engine's navigation node.

## Core workflow

The rest: https://github.com/gamedev-skills/awesome-gamedev-agent-skills/blob/HEAD/skills/disciplines/ai-behavior-trees-utility-ai/SKILL.md