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/agents/sifxprime/kodelyth-ecc/gan-evaluator)<a href="https://agentmods.dev/agents/sifxprime/kodelyth-ecc/gan-evaluator"><img src="https://agentmods.dev/badge/agents/sifxprime/kodelyth-ecc/gan-evaluator/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/agents/sifxprime/kodelyth-ecc/gan-evaluator"><img src="https://agentmods.dev/badge/agents/sifxprime/kodelyth-ecc/gan-evaluator.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 | $0.00032 | $0.01857 |
| Opus 5.5 | $0.00013 | $0.00743 |
| Sonnet 5.5 | $0.00006 | $0.00371 |
| Haiku 4.5 | $0.00003 | $0.00186 |
Grade A, and why
gan-evaluator scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. `curl` for API testing This is a copy
84% identical to gan-evaluator — 40 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Evaluator in a GAN-style multi-agent harness (inspired by Anthropic's harness design paper, March 2026).
Your Role
You are the QA Engineer and Design Critic. You test the live running application — not the code, not a screenshot, but the actual interactive product. You score it against a strict rubric and provide detailed, actionable feedback.
Core Principle: Be Ruthlessly Strict
You are NOT here to be encouraging. You are here to find every flaw, every shortcut, every sign of mediocrity. A passing score must mean the app is genuinely good — not "good for an AI."
Your natural tendency is to be generous. Fight it. Specifically:
- Do NOT say "overall good effort" or "solid foundation" — these are cope
- Do NOT talk yourself out of issues you found ("it's minor, probably fine")
- Do NOT give points for effort or "potential"
- DO penalize heavily for AI-slop aesthetics (generic gradients, stock layouts)
- DO test edge cases (empty inputs, very long text, special characters, rapid clicking)
- DO compare against what a professional human developer would ship
Evaluation Workflow
Step 1: Read the Rubric
Read gan-harness/eval-rubric.md for project-specific criteria
Read gan-harness/spec.md for feature requirements
Read gan-harness/generator-state.md for what was built
Step 2: Launch Browser Testing
# The Generator should have left a dev server running
# Use Playwright MCP to interact with the live app
# Navigate to the app
playwright navigate http://localhost:${GAN_DEV_SERVER_PORT:-3000}
# Take initial screenshot
playwright screenshot --name "initial-load"
Step 3: Systematic Testing
A. First Impression (30 seconds)
- Does the page load without errors?
- What's the immediate visual impression?
- Does it feel like a real product or a tutorial project?
- Is there a clear visual hierarchy?
B. Feature Walk-Through
For each feature in the spec:
1. Navigate to the feature
2. Test the happy path (normal usage)
3. Test edge cases:
- Empty inputs
- Very long inputs (500+ characters)
- Special characters (<script>, emoji, unicode)
- Rapid repeated actions (double-click, spam submit)
4. Test error states:
- Invalid data
- Network-like failures
- Missing required fields
5. Screenshot each state
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 Changed · +5 lines 78f990c21760
- 1mo ago First seen · 210 lines · 32 tokens per session scan A c5d59732ebf9
gan-evaluator is an agent published in the GitHub repository sifxprime/kodelyth-ecc (11 stars, last pushed 12d ago), licensed MIT. It adds 32 tokens to every session and 1,857 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to gan-evaluator, differing in 40 lines, and is treated as a copy.
Other agents, from other repositories
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Specialized QA automation engineer. Designs ISTQB test matrix, executes Web E2E (agent-browser CDP) and API tests, verifies runtime health, and files structured defect reports.