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 skills add frabcd/codex-ai-game-studio --skill prompt-to-gamegit clone --depth 1 https://github.com/frabcd/codex-ai-game-studioWrote 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/skills/frabcd/codex-ai-game-studio/prompt-to-game)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/prompt-to-game"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/prompt-to-game/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/skills/frabcd/codex-ai-game-studio/prompt-to-game"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/prompt-to-game.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 | $0.00026 | $0.00445 |
| Opus 5 | $0.00013 | $0.00222 |
| Sonnet 5 | $0.00005 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
prompt-to-game 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 12d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt To Game
Outcome
Produce the smallest playable proof of the requested experience, not an unbounded production game.
Required inputs
- one-sentence game idea
- target platform
- time, team, engine, art, and licensing constraints
Ask for missing information only when it changes the route materially. Otherwise state conservative assumptions and proceed with read-only analysis.
Workflow
- Clarify the player fantasy, core verb, fail and success conditions, session length, and non-goals.
- Define a one-loop prototype acceptance test and asset budget.
- Run toolchain diagnosis and recommend a compatible engine and generation route.
- Create a reversible implementation plan; require approval before files or external tools change.
- Implement in thin vertical slices with a playable build after every slice.
- Capture controls, known limitations, screenshots, test evidence, and provenance.
Expected artifacts
- prototype brief
- approved plan
- playable build
- controls sheet
- evidence and provenance bundle
Workflow-specific gates
- Prefer placeholder assets until the core loop is demonstrably fun.
- Keep generated code and content reviewable in small, testable increments.
- Stop scope growth that does not improve the prototype acceptance test.
Production completion gate
Before recommending production use, complete and report all seven gates:
- Rights, consent, code/model/dataset/output license, and generation-provenance checks.
- Technical format, naming, scale, color, metadata, and target-import validation.
- Visual and temporal consistency review across representative views and states.
- Runtime memory, frame-time, draw-call, streaming, and asset-budget checks.
- Playability and interaction smoke tests in the target runtime.
- Screenshot, capture, diff, or artifact-regression evidence with reproducible settings.
- Human approval before replacing source assets or promoting generated output.
Unknown rights, missing consent, unsupported hardware, conflicting host adapters, or failed quality gates block production promotion. Preserve originals and make fallbacks explicit.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 59 lines · 26 tokens per session scan A 89036919cb0d
prompt-to-game is a skill published in the GitHub repository frabcd/codex-ai-game-studio (10 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 445 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 2026-08-31.
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