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 soak-testgit 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/soak-test)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/soak-test"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/soak-test/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/soak-test"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/soak-test.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 | $0.00054 | $0.02449 |
| Opus 5.5 | $0.00022 | $0.00980 |
| Sonnet 5.5 | $0.00011 | $0.00490 |
| Haiku 4.5 | $0.00005 | $0.00245 |
Grade A, and why
soak-test 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 1mo 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
94% identical to soak-test — 95 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Port provenance: adapted from the pinned upstream source at
984023ddac0d5e27624f2baacde6105e45de375funder MIT; see the repository parity ledger for the exact path and blob.
Soak Test
A soak test (also called an endurance test) is an extended play session run with specific observation goals. Unlike a smoke check (broad critical path, ~10 min) or a single-feature playtest (~30 min), a soak test runs for 30 minutes to several hours to surface:
- Memory leaks — gradual heap growth that only appears after scene transitions
- Performance drift — frame time degradation that worsens over time
- State accumulation bugs — issues that only appear after N repetitions of a mechanic (inventory full, score overflow, AI state corruption)
- Fun fatigue — mechanics that feel good in a first session but grow repetitive over extended play
- Content exhaustion — the point where players run out of novel content
This skill generates the observation protocol and analysis harness — the human does the actual playing.
Output: production/qa/soak-test-[date]-[duration].md
When to run:
- Polish phase — before
$ai-game-studio:gate-check release - After fixing a memory or stability issue (regression soak)
- When extended play has not been formally tracked
1. Parse Arguments
Duration (default: 1h):
30m— short soak; suitable for testing a single mechanic or scene1h— standard soak; covers most common leak categories2h— extended soak; recommended for first full Polish soak4h— deep soak; required for games with long session design (RPGs, sims)
Focus (default: all):
memory— focus on heap size, object count, leak patternsstability— focus on crash/freeze/hang detectionbalance— focus on fun fatigue, content exhaustion, difficulty perceptionall— all of the above
2. Load Context
Read:
.ai-game-studio/project.json— engine (for engine-specific memory monitoring guidance), performance budgets (memory ceiling, target FPS)design/gdd/game-concept.md— intended session length (for comparison against soak duration), core loop description- Most recent file in
production/playtests/— prior playtest findings (to avoid re-documenting known issues) - Most recent file in
production/qa/qa-plan-*.md— current sprint test coverage (to understand what has been formally tested vs. what the soak covers)
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.
- 1mo ago First seen · 287 lines · 54 tokens per session scan A 98afd04eab67
soak-test is a skill published in the GitHub repository frabcd/codex-ai-game-studio (17 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 2,449 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to soak-test, differing in 95 lines, and is treated as a copy.
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