Borrowing it
Nothing to install: this file belongs to ukanwat/aaabench. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ukanwat/aaabench/main/.claude/skills/game-ai/SKILL.mdgit clone --depth 1 https://github.com/ukanwat/aaabenchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ukanwat/aaabench/game-ai)<a href="https://agentmods.dev/skills/ukanwat/aaabench/game-ai"><img src="https://agentmods.dev/badge/skills/ukanwat/aaabench/game-ai.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.02008 |
| Opus 5 | $0.00043 | $0.01004 |
| Sonnet 5 | $0.00017 | $0.00402 |
| Haiku 4.5 | $0.00009 | $0.00201 |
Grade A, and why
game-ai scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to game-ai — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game AI: decisions, steering, and pathfinding
Build believable NPC behavior from three separable layers: decide (what to do), steer (how to move there), and path (how to route around the map). Keep them decoupled — a behavior tree picks a target, the pathfinder produces waypoints, steering follows them. This skill teaches the engine-neutral algorithms; bind them to your engine via the related skills below.
When to use
- Use when implementing enemy/NPC logic: patrols, chase/flee, guard states, group movement, or "find a path to the player".
- Use to choose between an FSM (few clear states), a behavior tree (many reactive behaviors with priorities), or steering (smooth local movement).
- Use when integrating pathfinding: A* on a grid/graph, or driving an engine navmesh agent.
When not to use: for the engine's concrete navmesh/agent API and baking,
use unity-navmesh, unreal-behavior-trees, or Godot's NavigationAgent2D/3D
(see that engine skill). For movement/collision feel, use physics-tuning. For
spawning waves along lanes, see the tower-defense genre skill.
Core workflow
- Pick the decision model by complexity. 2–5 states with obvious transitions → FSM. Many behaviors, priorities, interruption, reuse → behavior tree. Continuous "how strongly do I want each option" → utility scoring.
- Separate decision from motion. The decision layer outputs an intent (target position, action). Steering or pathfinding turns intent into motion.
- Path on the right graph. Grid tiles, waypoint graph, or a baked navmesh. Fewer nodes = faster A*. Prefer the engine's navmesh for 3D; A* on a grid for tile games.
- Steer along the path, not straight to the goal — follow the next waypoint, advancing when close, so agents round corners.
- Recompute paths sparingly. Pathfind on a timer or when the goal moves a tile, not every frame. Cache the path; only the waypoint index advances.
- Verify by observation. Watch the agent: does it reach the goal, get stuck on corners, oscillate between states? Draw the path and current state on screen while tuning.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 180 lines · 85 tokens per session scan A 76e8412176fc
game-ai is a skill published in the GitHub repository ukanwat/aaabench (382 stars, last pushed 24d ago), licensed MIT. It adds 85 tokens to every session and 2,008 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to game-ai, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
unreal-engineer
!cat skills/shared/protocols/3d-spatial-foundations.md 2>/dev/null || echo "=== 3D Foundations not loaded ===".
unreal-playtest-agent
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unreal-fab-assets
Acquire free Fab marketplace content through Unreal Engine's official Fab integration, then verify the imported Content Browser assets. Use when the user asks to find, download, add, or import Fab or Unreal Marketplace assets. Not for arbitrary file imports - use unreal-assets instead.
unreal-pie
Domain skill - Unreal Engine Play-In-Editor (PIE) closed-loop verification. Provides PIE lifecycle control (enter/pause/resume/exit), controlled input injection (no OS dependency), viewport screenshot capture, output log snapshot, performance sampling, and Automation Test execution with async job polling. Use for…
unreal-actors
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unreal-automation
Domain skill - Unreal Engine native Automation Test and MCP health validation. Use to inspect typed plugin readiness, list UE Automation tests, queue native test runs, and run a safe self-check against the active MCP server.