gan-generator

gan-generator is a skill for Claude Code, Codex from RaNDoM6913/claude-code-superkit. It costs 40 tokens per session (1,837 once invoked), scanned A, original, MIT.

An implementation skill for the second step of a GAN workflow. It builds a feature from a planner's specification and writes Playwright browser tests alongside the code.

In plain words
What is it for?
It implements the planned happy, empty, error, and authentication cases, verifies persistence after reloads, and reports unresolved test failures.
Why use it?
It keeps the implementation tied to explicit scenarios and prevents incomplete coverage or tests being disabled just to make the run pass.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/random6913/claude-code-superkit/gan-generator
Any agent
npx skills add RaNDoM6913/claude-code-superkit --skill gan-generator
Clone the repo
git clone --depth 1 https://github.com/RaNDoM6913/claude-code-superkit

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for gan-generator

README.md
[![agentmods](https://agentmods.dev/badge/skills/random6913/claude-code-superkit/gan-generator.svg)](https://agentmods.dev/skills/random6913/claude-code-superkit/gan-generator)
Your own site
<a href="https://agentmods.dev/skills/random6913/claude-code-superkit/gan-generator"><img src="https://agentmods.dev/badge/skills/random6913/claude-code-superkit/gan-generator.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,837 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00040 $0.01837
Opus 5 $0.00020 $0.00919
Sonnet 5 $0.00008 $0.00367
Haiku 4.5 $0.00004 $0.00184

Measured yesterday against content hash c1d6d1fc7b06, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

packages/gan/skills/gan-generator/SKILL.md · 170 lines

How it starts

The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GAN Generator

Step 2 of 3 in the GAN harness. Takes a plan from gan-planner and writes the production code + Playwright tests that satisfy it. Every scenario gets an implementation AND its own test. Codex CLI has no subagents: run this as the second of three skills in sequence — the planner's markdown plan is your input, and your hand-off note feeds the evaluator step.

Hard Rules

  • NEVER test.skip / xit / disable a failing test to reach green — fix the code or the test.
  • NEVER mock the project's own backend in e2e tests; mock only external services and injected failures (e.g., route → 500).
  • The happy-path persistence assertion (page.reload() + re-assert) is non-negotiable.
  • Implement ONLY what the plan scopes — no extra features, abstractions, or "improvements".
  • Max 3 fix attempts per failing test; after the 3rd, stop and hand off Local test result: FAILED with details — an honest FAILED beats a disabled test or an endless loop.
  • Every plan scenario gets both an implementation and its own test() block.

Phase 0 — Load Plan

The input is a markdown plan from gan-planner. Read it carefully:

  1. Identify all scenarios (happy + edge + error + auth)
  2. Identify the file list (what to modify, what to create)
  3. Identify the test file location
  4. Identify the anti-slop checklist (the plan's ## Rubric section is for gan-evaluator — pass it through untouched)

If anything in the plan is ambiguous → STOP and ask for clarification. Do NOT guess.

Workflow

Step 1: Implement the code

For each file in the plan's "Files to be modified" list:

  • Read the existing file (if it exists)
  • Apply the smallest change that fulfills the scenarios (minimal-change-engineer discipline: no drive-by refactors, no new abstractions for a single use)
  • Do NOT add extra abstractions, features, or "improvements" not in the plan
  • Keep imports tidy

Step 2: Write Playwright tests

For each scenario, write a single test() block:

import { test, expect } from '@playwright/test';

test.describe('<feature>', () => {
  test('happy path — user creates a post', async ({ page }) => {
    // Given
    await loginAs(page, '[email protected]');
    await page.goto('/posts');

    // When
    await page.getByRole('button', { name: 'New post' }).click();
    await page.getByLabel('Title').fill('Hello world');
    await page.getByLabel('Body').fill('First post content');
    await page.getByRole('button', { name: 'Publish' }).click();

    // Then
    await expect(page.getByText('Hello world')).toBeVisible({ timeout: 2000 });
    await expect(page).toHaveURL(/\/posts\/\w+/);

    // Persistence assertion
    await page.reload();
    await expect(page.getByText('Hello world')).toBeVisible();
  });

  test('empty state — no posts shown to first-time user', async ({ page }) => {
    await loginAs(page, '[email protected]');
    await page.goto('/posts');
    await expect(page.getByText(/no posts yet/i)).toBeVisible();
  });

  test('error state — graceful failure on server error', async ({ page }) => {
    await page.route('**/api/posts', r => r.fulfill({ status: 500 }));
    await loginAs(page, '[email protected]');
    await page.goto('/posts');
    await expect(page.getByText(/something went wrong/i)).toBeVisible();
  });

  test('auth-required — redirect to login when unauthenticated', async ({ page }) => {
    await page.goto('/posts');
    await expect(page).toHaveURL(/\/login/);
  });
});

Read the full file on GitHub · 170 lines

Changes

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.

  1. yesterday First seen · 170 lines · 40 tokens per session scan A c1d6d1fc7b06

Subscribe to this mod's changes

gan-generator is a skill published in the GitHub repository RaNDoM6913/claude-code-superkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 1,837 once invoked, about $0.0002 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 2026-09-03.

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