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.
git clone --depth 1 https://github.com/mturac/everything-openai-codexWrote 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/commands/mturac/everything-openai-codex/gan-design)<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/gan-design"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/gan-design/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/commands/mturac/everything-openai-codex/gan-design"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/gan-design.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 | $0.00018 | $0.00417 |
| Opus 5.5 | $0.00007 | $0.00167 |
| Sonnet 5.5 | $0.00004 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
gan-design 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- gan-design — 92% identical, 5 lines differ
- gan-design — 92% identical, 12 lines differ
What it actually says
Parse the following from $ARGUMENTS:
brief— the user's description of the design to create--max-iterations N— (optional, default 10) maximum design-evaluate cycles--pass-threshold N— (optional, default 7.5) weighted score to pass (higher default for design)
GAN-Style Design Harness
A two-agent loop (Generator + Evaluator) focused on frontend design quality. No planner — the brief IS the spec.
This is the same mode OpenAI used for their frontend design experiments, where they saw creative breakthroughs like the 3D Dutch art museum with CSS perspective and doorway navigation.
Setup
- Create
gan-harness/directory - Write the brief directly as
gan-harness/spec.md - Write a design-focused
gan-harness/eval-rubric.mdwith extra weight on Design Quality and Originality
Design-Specific Eval Rubric
### Design Quality (weight: 0.35)
### Originality (weight: 0.30)
### Craft (weight: 0.25)
### Functionality (weight: 0.10)
Note: Originality weight is higher (0.30 vs 0.20) to push for creative breakthroughs. Functionality weight is lower since design mode focuses on visual quality.
Loop
Same as /project:gan-build Phase 2, but:
- Skip the planner
- Use the design-focused rubric
- Generator prompt emphasizes visual quality over feature completeness
- Evaluator prompt emphasizes "would this win a design award?" over "do all features work?"
Key Difference from gan-build
The Generator is told: "Your PRIMARY goal is visual excellence. A stunning half-finished app beats a functional ugly one. Push for creative leaps — unusual layouts, custom animations, distinctive color work."
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 · 40 lines · 18 tokens per session scan A f3a0b9a340ec
gan-design is a command published in the GitHub repository mturac/everything-openai-codex (86 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 417 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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responsive-design-specialist
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