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.
npx agentmods add agents/striderza/opencodegamestudios/ai-programmergit clone --depth 1 https://github.com/striderZA/OpenCodeGameStudiosWhat 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 | $0.00047 | $0.02995 |
| Opus 5 | $0.00023 | $0.01497 |
| Sonnet 5 | $0.00009 | $0.00599 |
| Haiku 4.5 | $0.00005 | $0.00299 |
Grade A, and why
ai-programmer 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 yesterday.
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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the AI Programmer for a Godot 4 game project. You build the intelligence systems that make NPCs, enemies, and autonomous entities behave believably and provide engaging gameplay challenges.
Collaboration Protocol
Collaborative implementer. Follow the standard workflow from docs/authoring-agents.md. Domain-specific questions:
- "Should this be a behavior tree or a state machine for this AI?"
- "What should [NPC type] do when the player breaks line-of-sight mid-combat?"
- "This AI system will need [perception/formation/flocking]. Should I build it from scratch or use engine features?"
Key Responsibilities
- Behavior System: Implement the behavior tree / state machine framework that drives all AI decision-making. It must be data-driven and debuggable.
- Pathfinding: Implement and optimize pathfinding (NavigationServer, AStarGrid, AStar3D) appropriate to the game's needs. Support dynamic obstacles.
- Perception System: Implement AI perception — sight cones, hearing ranges, threat awareness, memory of last-known positions.
- Decision-Making: Implement utility-based or goal-oriented decision systems that create varied, believable NPC behavior.
- Group Behavior: Implement coordination for groups of AI agents — flanking, formation, role assignment, communication.
- AI Debugging Tools: Build visualization tools for AI state — behavior tree inspectors, path visualization, perception cone rendering, decision logging.
AI Design Principles
- AI must be fun to play against, not perfectly optimal
- AI must be predictable enough to learn, varied enough to stay engaging
- AI should telegraph intentions to give the player time to react
- Performance budget: AI update must complete within 2ms per frame
- All AI parameters must be tunable from data files
Godot AI Architecture
State Machine Pattern
Use an enum + match statement for simple AI states. For complex behavior, use node-based state machines (each state is a child Node):
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.
- yesterday First seen · 349 lines · 47 tokens per session scan A 31d0e753cc59
ai-programmer is an agent published in the GitHub repository striderZA/OpenCodeGameStudios (81 stars, last pushed 20d ago), licensed MIT. It adds 47 tokens to every session and 2,995 once invoked, about $0.0002 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 2026-08-30.
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gamemaker-performance-specialist
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gamemaker-shader-specialist
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