{"slug":"bgrenat-gamedev-game-ai","name":"gamedev-game-ai","description":"Imported awesome-gamedev-agent-skills guidance for game-ai workflows in game development projects. Use when designing NPC behavior with decision layers (FSM, behavior trees, utility AI), steering, and pathfinding.","long_description":"---\nname: gamedev-game-ai\ndescription: \"Imported awesome-gamedev-agent-skills guidance for game-ai workflows in game development projects. Use when designing NPC behavior with decision layers (FSM, behavior trees, utility AI), steering, and pathfinding.\"\n---\n# Game AI: decisions, steering, and pathfinding\n\nBuild believable NPC behavior from three separable layers: **decide** (what to\ndo), **steer** (how to move there), and **path** (how to route around the map).\nKeep them decoupled — a behavior tree picks a target, the pathfinder produces\nwaypoints, steering follows them. This skill teaches the engine-neutral\nalgorithms; bind them to your engine via the related skills below.\n\n## When to use\n\n- Use when implementing enemy/NPC logic: patrols, chase/flee, guard states,\n  group movement, or \"find a path to the player\".\n- Use to choose between an **FSM** (few clear states), a **behavior tree** (many\n  reactive behaviors with priorities), or **steering** (smooth local movement).\n- Use when integrating pathfinding: A* on a grid/graph, or driving an engine\n  navmesh agent.\n\n**When *not* to use:** for the engine's concrete navmesh/agent API and baking,\nuse `unity-navmesh`, `unreal-behavior-trees`, or Godot's `NavigationAgent2D/3D`\n(see that engine skill). For movement/collision feel, use `physics-tuning`. For\nspawning waves along lanes, see the `tower-defense` genre skill.\n\n## Core workflow\n\n1. **Pick the decision model by complexity.** 2–5 states with obvious\n   transitions → FSM. Many behaviors, priorities, interruption, reuse → behavior\n   tree. Continuous \"how strongly do I want each option\" → utility scoring.\n2. **Separate decision from motion.** The decision layer outputs an *intent*\n   (target position, action). Steering or pathfinding turns intent into motion.\n3. **Path on the right graph.** Grid tiles, waypoint graph, or a baked navmesh.\n   Fewer nodes = faster A*. Prefer the engine's navmesh for 3D; A* on a grid for\n   tile games.\n4. **Steer along the path**, not straight to the goal — follow the next waypoint,\n   advancing when close, so agents round corners.\n5. **Recompute paths sparingly.** Pathfind on a timer or when the goal moves a\n   tile, not every frame. Cache the path; only the waypoint index advances.\n6. **Verify by observation.** Watch the agent: does it reach the goal, get stuck\n   on corners, oscillate between states? Draw the path and current state on\n   screen while tuning.\n\n## Patterns\n\n### 1. Finite state machine (one state object, explicit transitions)\n\n```gdscript\n# Each state is a small object with enter/update/exit. The machine owns \"current\".\nclass_name State\nfunc enter(agent): pass\nfunc update(agent, dt) -> State: return null   # return a new state to transition\nfunc exit(agent): pass\n\n# --- Chase state: returns Patrol when the player escapes sight range ---\nclass Chase extends State:\n    func update(agent, dt) -> State:\n        if not agent.can_see(agent.target):\n            return Patrol.new()                 # transition by returning next state\n        agent.move_toward(agent.target.position, dt)\n        return null                             # null = stay in this state\n\n# --- Driver: call once per frame ---\nfunc tick(dt):\n    var next = current.update(self, dt)\n    if next != null:\n        current.exit(self); next.enter(self); current = next\n```\n\nKeep transition logic *inside* states (or in a table), never as a growing pile\nof `if` flags. One state owns one behavior; that is what keeps an FSM readable.\n\n### 2. Behavior tree tick (composite nodes return a status)\n\n```gdscript\n# A node's tick() returns SUCCESS, FAILURE, or RUNNING (still working this frame).\nenum Status { SUCCESS, FAILURE, RUNNING }\n\n# Sequence: run children in order; stop at the first non-SUCCESS (logical AND).\nfunc sequence_tick(children, agent, dt) -> int:\n    for child in children:\n        var s = child.tick(agent, dt)\n        if s != Status.SUCCESS:\n            return s                 # FAILURE or RUNNING short-circuits the sequence\n    return Status.SUCCESS\n\n# Selector: try children until one succeeds or is RUNNING (logical OR / fallback).\nfunc selector_tick(children, agent, dt) -> int:\n    for child in children:\n        var s = child.tick(agent, dt)\n        if s != Status.FAILURE:\n            return s                 # SUCCESS or RUNNING stops the search\n    return Status.FAILURE\n```\n\nA guard AI reads top-down: `Selector[ Sequence[CanSeePlayer?, Chase], Patrol ]`\n— chase if visible, otherwise patrol. See `references/behavior-trees.md` for\nleaf nodes, decorators (Inverter, Cooldown), and a blackboard.\n\n### 3. Steering: seek and arrive (smooth, frame-rate independent)\n\n```gdscript\n# Seek: accelerate toward a target at full speed. Steering = desired - current.\nfunc seek(pos, vel, target, max_speed, max_force) -> Vector2:\n    var desired = (target - pos).normalized() * max_speed\n    return (desired - vel).limit_length(max_force)   # a force, not a teleport\n\n# Arrive: like seek, but ramp speed down inside slow_radius so it stops cleanly.\nfunc arrive(pos, vel, target, max_speed, max_force, slow_radius) -> Vector2:\n    var offset = target - pos\n    var dist = offset.length()\n    if dist < 0.001: return -vel                      # already there: kill drift\n    var ramped = max_speed * min(dist / slow_radius, 1.0)\n    var desired = offset / dist * ramped\n    return (desired - vel).limit_length(max_force)\n\n# Per frame: vel += steering * dt; pos += vel * dt   (always scale by dt)\n```\n\n### 4. A* heuristic must not overestimate (or paths stop being shortest)\n\n```python\n# Match the heuristic to the movement. An ADMISSIBLE heuristic (never larger\n# than the true remaining cost) keeps A* optimal.\ndef heuristic(a, b):\n    dx, dy = abs(a.x - b.x), abs(a.y - b.y)\n    # return dx + dy             # Manhattan: 4-direction grids (no diagonals)\n    return (dx + dy) + (1.414 - 2) * min(dx, dy)   # octile: 8-direction grids\n# f(n) = g(n) + h(n): g = cost from start, h = heuristic to goal.\n# Overestimating h is faster but no longer guarantees the shortest path.\n```\n\nThe full A* loop (priority queue, `came_from` reconstruction, grid + waypoint\ngraphs) is in `references/pathfinding.md`.\n\n## Pitfalls\n\n- **Pathfinding every frame** tanks the frame rate. Recompute on a timer or only\n  when the target moves to a new tile; follow the cached waypoints in between.\n- **Steering straight to the goal** instead of to the next waypoint makes agents\n  hug walls and corners. Follow the path; advance the waypoint when within radius.\n- **Inadmissible A\\* heuristic** (e.g. Euclidean distance scaled up, or Manhattan\n  on a diagonal grid) returns fast but *non-shortest* paths. Pick the heuristic\n  that matches your allowed moves.\n- **Behavior tree leaves that never return RUNNING** for multi-frame actions\n  (walking, playing an animation) cause the tree to restart the action every\n  tick. Return RUNNING until the action completes.\n- **FSM transition spaghetti**: scattering `if state == ...` checks everywhere\n  recreates the mess an FSM exists to prevent. Keep transitions in the state.\n- **No line-of-sight or stuck check** → agents grind into walls forever. Add a\n  timeout that forces a repath or a state change.\n\n## References\n\n- `references/pathfinding.md` — complete A* (priority queue, reconstruction),\n  grid vs waypoint graphs, when to defer to an engine navmesh.\n- `references/behavior-trees.md` — node taxonomy, leaf/decorator implementations,\n  blackboard, and FSM-vs-BT selection.\n\n## Related skills\n\n- `unity-navmesh`, `unreal-behavior-trees` — concrete engine AI/navigation APIs.\n- `physics-tuning` — movement, collision response, and agent radius.\n- `procedural-gen` — generating the graph/level the AI navigates.\n- `tower-defense`, `fps-shooter` — genres that compose this skill.\n","tagline":"Imported awesome-gamedev-agent-skills guidance for game-ai workflows in game development projects. Use when designing NPC behavior with decision layers (FSM, behavior trees, utility AI), steering, and pathfinding.","category":"coding-agents","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"bgrenat","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"bgrenat/godot-game-dev-studio","creatorName":"bgrenat","creatorUrl":"https://github.com/bgrenat","sourceUrl":"https://github.com/bgrenat/godot-game-dev-studio/tree/main/skills/gamedev-game-ai","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/bgrenat-gamedev-game-ai#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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