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-evaluatorgit 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.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 | $0.00032 | $0.01777 |
| Opus 5 | $0.00016 | $0.00889 |
| Sonnet 5 | $0.00006 | $0.00355 |
| Haiku 4.5 | $0.00003 | $0.00178 |
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 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.
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
88% identical to gan-evaluator — 13 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 — 210 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.
- 4d 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 2d ago), licensed MIT. It adds 32 tokens to every session and 1,777 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to gan-evaluator, differing in 13 lines, and is treated as a copy.
Other agents, from other repositories
test-automation-engineer
Hands-on test automation engineer. Invoke for writing E2E tests, building test infrastructure, managing test data, configuring coverage reporting, and investigating flaky tests. MCP-first Playwright automation. This agent writes test code — not production code.
gan-evaluator
GAN Harness — Evaluator agent. Tests the live running application via Playwright, scores against rubric, and provides actionable feedback to the Generator.
lens
Role: Demo Recorder + Integration Witness.
e2e-runner
End-to-end testing specialist using Vercel Agent Browser (preferred) with Playwright fallback. Use PROACTIVELY for generating, maintaining, and running E2E tests. Manages test journeys, quarantines flaky tests, uploads artifacts (screenshots, videos, traces), and ensures critical user flows work.
e2e-runner
End-to-end testing specialist using Vercel Agent Browser (preferred) with Playwright fallback. Use PROACTIVELY for generating, maintaining, and running E2E tests. Manages test journeys, quarantines flaky tests, uploads artifacts (screenshots, videos, traces), and ensures critical user flows work.
gan-evaluator
GAN Harness — Evaluator agent. Tests the live running application via Playwright, scores against rubric, and provides actionable feedback to the Generator.