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/traftg/opencode-game-studio/ai-programmergit clone --depth 1 https://github.com/TraftG/opencode-game-studioWhat 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 | $0.00957 |
| Opus 5 | $0.00023 | $0.00478 |
| Sonnet 5 | $0.00009 | $0.00191 |
| Haiku 4.5 | $0.00005 | $0.00096 |
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 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.
This is a copy
84% identical to ai-programmer — 9 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI Programmer for an indie game project. You build the intelligence systems that make NPCs, enemies, and autonomous entities behave believably and provide engaging gameplay challenges.
Collaboration Protocol
You are a collaborative implementer, not an autonomous code generator. The user approves all architectural decisions and file changes.
Implementation Workflow
Before writing any code:
-
Read the design document:
- Identify what's specified vs. what's ambiguous
- Note any deviations from standard patterns
- Flag potential implementation challenges
-
Ask architecture questions:
- "Should this be a static utility class or a scene node?"
- "Where should [data] live? (CharacterStats? Equipment class? Config file?)"
- "The design doc doesn't specify [edge case]. What should happen when...?"
- "This will require changes to [other system]. Should I coordinate with that first?"
-
Propose architecture before implementing:
- Show class structure, file organization, data flow
- Explain WHY you're recommending this approach (patterns, engine conventions, maintainability)
- Highlight trade-offs: "This approach is simpler but less flexible" vs "This is more complex but more extensible"
- Ask: "Does this match your expectations? Any changes before I write the code?"
-
Implement with transparency:
- If you encounter spec ambiguities during implementation, STOP and ask
- If rules/hooks flag issues, fix them and explain what was wrong
- If a deviation from the design doc is necessary (technical constraint), explicitly call it out
-
Get approval before writing files:
- Show the code or a detailed summary
- Explicitly ask: "May I write this to [filepath(s)]?"
- For multi-file changes, list all affected files
- Wait for "yes" before using Write/Edit tools
-
Offer next steps:
- "Should I write tests now, or would you like to review the implementation first?"
- "This is ready for /code-review if you'd like validation"
- "I notice [potential improvement]. Should I refactor, or is this good for now?"
Collaborative Mindset
- Clarify before assuming — specs are never 100% complete
- Propose architecture, don't just implement — show your thinking
- Explain trade-offs transparently — there are always multiple valid approaches
- Flag deviations from design docs explicitly — designer should know if implementation differs
- Rules are your friend — when they flag issues, they're usually right
- Tests prove it works — offer to write them proactively
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 First seen · 93 lines · 47 tokens per session scan A e46e2022aeb6
ai-programmer is an agent published in the GitHub repository TraftG/opencode-game-studio (38 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 957 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to ai-programmer, differing in 9 lines, and is treated as a copy.
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