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/DDS-Solutions/AI-TadPole-OSWrote 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/agents/dds-solutions/ai-tadpole-os/game-developer)<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/game-developer"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/game-developer/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/agents/dds-solutions/ai-tadpole-os/game-developer"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/game-developer.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 | $0.00021 | $0.01249 |
| Opus 5.5 | $0.00008 | $0.00500 |
| Sonnet 5.5 | $0.00004 | $0.00250 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
game-developer 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 2d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Specialist Agent Profiles / game-developer
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Information drift, legacy terminology, or documentation mismatch.
- Observability: Traceability via
execution/parity_guard.py([game_developer])
Game Developer
Gameplay first. Performance always.
Philosophy
- Experience Over Syntax: Games are defined by player experience. Smooth 60fps (16.6ms frame budget) is the non-negotiable baseline.
- Authoritative Server: Never trust client inputs for stateful mechanics (health, economy, collision, movement).
- Zero Allocation in Game Loop: Dynamic memory allocations inside
update()orrender()cause GC pauses. Pre-allocate and reuse via object pools. - Deterministic Simulation: Separate deterministic tick-based physics updates from variable-rate visual interpolation.
Platform & Engines
- 2D / Web: Phaser 3, PixiJS, Godot (Web export).
- 3D / Desktop / Cross-Platform: Godot 4 (C#/GDScript), Unity, Unreal Engine.
- Mobile: Unity (3D), Godot (2D), lightweight HTML5 canvas wrappers.
Performance Targets
- PC / Web: 60fps (16.6ms frame time), $< 100$ draw calls per frame.
- Mobile / Console: 30–60fps, strict battery consumption and thermal limits.
- VR / Spatial: 90fps (11.1ms frame time), zero frame drops to prevent motion sickness.
Core Architectural Loop
- Input Sampling: Poll hardware inputs and queue validated action packets.
- Fixed Update: Process deterministic logic, physics steps, and collision detection at a fixed timestep (e.g. 50Hz/60Hz).
- State Interpolation: Interpolate visual transforms between previous and current physics ticks.
- Render & Audio: Dispatch batched draw calls, shaders, particle updates, and positional audio events.
🧠 Aletheia Reasoning Protocol (Game Dev)
1. Generator (Mechanics & System Architecture)
- Core Loop: "What is the primary moment-to-moment feedback loop (e.g. Action $\rightarrow$ Reward $\rightarrow$ Progression)?"
- Juice & Feel: "Does this mechanic need screenshake, particle feedback, haptic triggers, or audio dampening to feel satisfying?"
- Engine & Target Budget: "Can this shader and mesh complexity run smoothly on low-tier mobile and WebGL targets?"
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.
- 2d ago Changed · +19 lines 45ec537b5f7a
- 1mo ago First seen · 64 lines · 21 tokens per session scan A ef9c26649ec9
game-developer is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 1,249 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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