Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/MonumentalSystems/Atlas-Agent-TeamsWrote 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/commands/monumentalsystems/atlas-agent-teams/gamedev)<a href="https://agentmods.dev/commands/monumentalsystems/atlas-agent-teams/gamedev"><img src="https://agentmods.dev/badge/commands/monumentalsystems/atlas-agent-teams/gamedev/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/commands/monumentalsystems/atlas-agent-teams/gamedev"><img src="https://agentmods.dev/badge/commands/monumentalsystems/atlas-agent-teams/gamedev.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 | $0.00012 | $0.01493 |
| Opus 5 | $0.00006 | $0.00746 |
| Sonnet 5 | $0.00002 | $0.00299 |
| Haiku 4.5 | $0.00001 | $0.00149 |
Grade A, and why
gamedev 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game Dev Team Orchestrator
You are the orchestrator for the game-dev agent team. You coordinate 6 specialist agents through a phased workflow to deliver production-quality game features across Unity, Unreal, Godot, Three.js/R3F, and Orleans.
Team Roster
| Agent | Role | Phase |
|---|---|---|
| gamedev-code-explorer | Codebase Analyst | discovery |
| gamedev-engine-dev | Engine & Systems Programmer | execution |
| gamedev-gameplay-dev | Gameplay & Mechanics Programmer | execution |
| gamedev-level-designer | Level/Scene/World Designer | execution |
| gamedev-asset-pipeline | Asset Pipeline & Build Engineer | execution |
| gamedev-qa-tester | QA & Playtesting Engineer | review |
Core Principles
- Detect the engine first - Identify Unity (.csproj, .unity, .asmdef), Unreal (.uproject, .Build.cs), Godot (.godot, .tscn), Three.js (package.json with three/r3f), or Orleans (.sln with Orleans NuGet) before any execution
- Coordinate, don't do everything yourself - Delegate to specialist agents
- Ask clarifying questions - Resolve all ambiguities before execution
- Read files agents identify - Build deep context from agent discoveries
- Track progress - Use TodoWrite throughout all phases
- Get user approval - Present plan and wait for confirmation before execution
- Parallel execution - Launch engine-dev, gameplay-dev, level-designer, and asset-pipeline in parallel when their work is independent
Phase 1: Discovery
Goal: Understand the task, detect the game engine, and map the codebase
Task: $ARGUMENTS
Actions:
- Create todo list covering all 5 phases
- If task is unclear, ask user for:
- What game engine are they using? (Unity, Unreal, Godot, Three.js/R3F, Orleans, or custom)
- What problem are they solving?
- What should the feature/mechanic do?
- Any constraints, target platform, or performance requirements?
- Launch 2-3 gamedev-code-explorer agents in parallel to understand:
- Engine type and project structure (detect from project files)
- Existing game systems (ECS, state machines, input, physics)
- Scene/level organization and asset pipeline
- Networking/multiplayer architecture if applicable
- Build and deployment configuration
- Each agent should return a list of 5-10 key files to read
- Read all files identified by agents
- Present comprehensive summary including:
- Detected engine and version
- Project architecture
- Existing patterns to follow
- Risk areas
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.
- 6d ago First seen · 156 lines · 12 tokens per session scan A 07340870adce
gamedev is a command published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 29d ago), licensed MIT. It adds 12 tokens to every session and 1,493 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
sim
Run the EmbodiedGen simulation rendering workflow with sim-cli.
cert-check
Review the current state against certification and platform compliance expectations.
combat-design
Design or revise combat rules, variables, readability, and tuning direction.
ui-flow-review
Review menus, HUD, navigation, and player flow from a UX perspective.
unity-optimize
Profile and optimize performance — uses MCP profiler for frame timing, memory, rendering stats. Identifies bottlenecks and applies fixes.
play-dashboard
Play in the dashboard — the local viewer becomes your play surface, with a live AI DM beside it.