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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ukanwat/aaabench/game-ai"><img src="https://agentmods.dev/badge/skills/ukanwat/aaabench/game-ai.svg" alt="Reviewed on agentmods" width="80" 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 5.5 | $0.00034 | $0.00803 |
| Sonnet 5.5 | $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 1mo 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 — 50 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.
- 1mo ago First seen · 180 lines · 85 tokens per session scan A 76e8412176fc
game-ai is a skill published in the GitHub repository ukanwat/aaabench (385 stars, last pushed 2mo ago), licensed MIT. It adds 85 tokens to every session and 2,008 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to game-ai, differing in 50 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
Domain skill - run screenshot-light PIE playtest episodes with structured entity observations, bounded semantic actions, transition polling, and in-memory traces for QA and external policy or RL runners.
unreal-level
Domain skill - Unreal Engine level and world management: inspect level state, load and save maps, read or edit world settings, and measure native collision distances in Editor or PIE worlds. Not for individual actor transform edits - use unreal-actors for that.
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
Domain skill - Unreal Engine actor management: list, spawn, transform, and delete level actors. Use when inspecting or editing actors in the current Unreal Editor level. Not for Content Browser asset import/export - use unreal-assets for that.
unreal-assets
Domain skill - Unreal Engine Content Browser asset management: list, import, export, inspect, and delete assets. Use for package and asset operations in the Unreal Editor. Not for actor placement or transforms - use unreal-actors for that.