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 quality-enhancegit 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/quality-enhance)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/quality-enhance"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/quality-enhance.svg" alt="Measured on agentmods" 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 | $0.00024 | $0.00473 |
| Opus 5 | $0.00012 | $0.00236 |
| Sonnet 5 | $0.00005 | $0.00095 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
quality-enhance 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 8d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Enhance
Outcome
Improve a specific measurable weakness without silently changing style, behavior, rights, or performance budgets.
Required inputs
- source asset or scene
- approved quality target
- locked properties, performance budget, and acceptable transformations
Ask for missing information only when it changes the route materially. Otherwise state conservative assumptions and proceed with read-only analysis.
Workflow
- Diagnose concrete defects and define measurable acceptance criteria before proposing a transformation.
- Copy originals to a recoverable location and inventory all dependent files and references.
- Prepare a plan showing the exact enhancement route, tools, license implications, files, previews, and rollback.
- Wait for explicit confirmation before running transformations.
- Produce variants beside originals and create controlled before-and-after comparisons.
- Validate format, visual fidelity, temporal behavior, runtime budget, playability, and project integration before requesting replacement approval.
Expected artifacts
- defect diagnosis
- approved enhancement plan
- preserved originals
- candidate variants
- before-and-after evidence
- rollback record
Workflow-specific gates
- Never overwrite or replace a source asset without a final human choice made after previewing evidence.
- Reject improvements that introduce identity drift, style drift, seams, artifacts, broken dependencies, or budget regressions.
- Keep the enhancement reproducible by recording tools, versions, settings, prompts, seeds, and manual edits.
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.
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.
- 8d ago First seen · 60 lines · 24 tokens per session scan A 85153ce49fef
quality-enhance is a skill published in the GitHub repository frabcd/codex-ai-game-studio (9 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 473 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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