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/sifxprime/kodelyth-eccWrote 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/sifxprime/kodelyth-ecc/gan-design)<a href="https://agentmods.dev/commands/sifxprime/kodelyth-ecc/gan-design"><img src="https://agentmods.dev/badge/commands/sifxprime/kodelyth-ecc/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/sifxprime/kodelyth-ecc/gan-design"><img src="https://agentmods.dev/badge/commands/sifxprime/kodelyth-ecc/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 | $0.00013 | $0.00420 |
| Opus 5.5 | $0.00005 | $0.00168 |
| Sonnet 5.5 | $0.00003 | $0.00084 |
| Haiku 4.5 | $0.00001 | $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.
This is a copy
92% identical to gan-design — 5 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.
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 Anthropic 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 · 41 lines · 13 tokens per session scan A 33f64150b1cb
gan-design is a command published in the GitHub repository sifxprime/kodelyth-ecc (11 stars, last pushed 14d ago), licensed MIT. It adds 13 tokens to every session and 420 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to gan-design, differing in 5 lines, and is treated as a copy.
Other commands, from other repositories
design
A pair of commands for technical design: one provides a document structure, and the other checks the design against a checklist.
gan-design
Run a generator/evaluator design loop for frontend or visual work with bounded iterations and scoring.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.