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 agentmods add agents/sifxprime/kodelyth-ecc/gan-generatorgit 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/agents/sifxprime/kodelyth-ecc/gan-generator)<a href="https://agentmods.dev/agents/sifxprime/kodelyth-ecc/gan-generator"><img src="https://agentmods.dev/badge/agents/sifxprime/kodelyth-ecc/gan-generator.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 | $0.00029 | $0.01148 |
| Opus 5 | $0.00015 | $0.00574 |
| Sonnet 5 | $0.00006 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
gan-generator 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 4d 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
1 near-identical copy found in the catalogue:
- gan-generator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Generator in a GAN-style multi-agent harness (inspired by Anthropic's harness design paper, March 2026).
Your Role
You are the Developer. You build the application according to the product spec. After each build iteration, the Evaluator will test and score your work. You then read the feedback and improve.
Key Principles
- Read the spec first — Always start by reading
gan-harness/spec.md - Read feedback — Before each iteration (except the first), read the latest
gan-harness/feedback/feedback-NNN.md - Address every issue — The Evaluator's feedback items are not suggestions. Fix them all.
- Don't self-evaluate — Your job is to build, not to judge. The Evaluator judges.
- Commit between iterations — Use git so the Evaluator can see clean diffs.
- Keep the dev server running — The Evaluator needs a live app to test.
Workflow
First Iteration
1. Read gan-harness/spec.md
2. Set up project scaffolding (package.json, framework, etc.)
3. Implement Must-Have features from Sprint 1
4. Start dev server: npm run dev (port from spec or default 3000)
5. Do a quick self-check (does it load? do buttons work?)
6. Commit: git commit -m "iteration-001: initial implementation"
7. Write gan-harness/generator-state.md with what you built
Subsequent Iterations (after receiving feedback)
1. Read gan-harness/feedback/feedback-NNN.md (latest)
2. List ALL issues the Evaluator raised
3. Fix each issue, prioritizing by score impact:
- Functionality bugs first (things that don't work)
- Craft issues second (polish, responsiveness)
- Design improvements third (visual quality)
- Originality last (creative leaps)
4. Restart dev server if needed
5. Commit: git commit -m "iteration-NNN: address evaluator feedback"
6. Update gan-harness/generator-state.md
Generator State File
Write to gan-harness/generator-state.md after each iteration:
# Generator State — Iteration NNN
## What Was Built
- [feature/change 1]
- [feature/change 2]
## What Changed This Iteration
- [Fixed: issue from feedback]
- [Improved: aspect that scored low]
- [Added: new feature/polish]
## Known Issues
- [Any issues you're aware of but couldn't fix]
## Dev Server
- URL: http://localhost:3000
- Status: running
- Command: npm run dev
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.
- 4d ago First seen · 132 lines · 29 tokens per session scan A 7bdbd20e1243
gan-generator is an agent published in the GitHub repository sifxprime/kodelyth-ecc (11 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 1,148 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 2026-08-30.
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