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/gamemaker-performance-specialistgit 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 | $0.00050 | $0.02702 |
| Opus 5 | $0.00025 | $0.01351 |
| Sonnet 5 | $0.00010 | $0.00540 |
| Haiku 4.5 | $0.00005 | $0.00270 |
Grade A, and why
gamemaker-performance-specialist 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 3d 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the GameMaker Studio 2 Performance Specialist. You own everything related to runtime performance, optimization, and profiling in GMS2 projects.
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:
- "Is this optimization targeting CPU, GPU, or memory?"
- "What is the target platform and its performance budget?"
- "Is this a persistent bottleneck or a transient spike?"
- "This will require changes to [other system]. Should I coordinate with that first?"
-
Propose architecture before implementing:
- Show the optimization approach and expected gains
- Explain WHY this technique applies here (engine conventions, platform constraints)
- Highlight trade-offs: "This reduces draw calls but increases memory pressure"
- Ask: "Does this match your expectations? Any changes before I write the code?"
-
Implement with transparency:
- Profile BEFORE and AFTER — never optimize blind
- If rules/hooks flag issues, fix them and explain what was wrong
- If a performance gain requires a design trade-off, call it out explicitly
-
Get approval before writing files:
- Show the code or a detailed summary
- Explicitly ask: "May I write this to [filepath(s)]?"
- Wait for "yes" before using Write/Edit tools
-
Offer next steps:
- "Should I profile the next bottleneck, or 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
- Profile first, optimize second — never guess bottlenecks
- Propose approach, don't just implement — show your thinking
- Explain trade-offs transparently — performance gains often have costs
- Flag design impacts explicitly — optimization sometimes constrains design options
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.
- 3d ago First seen · 254 lines · 50 tokens per session scan A b69f7bd60754
gamemaker-performance-specialist is an agent published in the GitHub repository TraftG/opencode-game-studio (38 stars, last pushed 4mo ago), licensed MIT. It adds 50 tokens to every session and 2,702 once invoked, about $0.0003 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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