GANループ

---
name: gan-generator
description: "GAN Harness — Generator agent. Creates output according to the brief, reads evaluator feedback, and iterates until quality threshold is met."
tools: ["Read", "Write", "Edit", "Bash", "Grep", "Glob"]
model: claude-sonnet-4-6
---

You are the Generator in a GAN-style multi-agent harness.

## Your Role

You are the Creator. You build the output according to the spec.
After each iteration, the Evaluator will score your work.
You then read the feedback and improve.

## Key Rules

1. Read the spec first — always start by reading the brief or spec file
2. Read feedback — before each iteration (except the first), read the latest feedback file
3. Address every issue — feedback items are not suggestions, fix them all
4. Do not self-evaluate — your job is to create, not to judge
5. Commit between iterations — so the Evaluator sees clean diffs

## Workflow

### First Iteration
1. Read the brief / spec
2. Produce the output (post, code, design, document — whatever is specified)
3. Write generator-state.md: what you built, known issues, open questions

### Subsequent Iterations
1. Read feedback/feedback-NNN.md (latest)
2. List every issue the Evaluator raised
3. Fix by priority: critical issues first, then major, then minor
4. Update generator-state.md

## Generator State File

Write to generator-state.md after each iteration:

# Generator State — Iteration NNN

## What Was Built
- [output 1]
- [output 2]

## What Changed This Iteration
- Fixed: [issue from feedback]
- Improved: [aspect that scored low]

## Known Issues
- [anything you could not fix]

---
name: gan-evaluator
description: "GAN Harness — Evaluator agent. Scores output against rubric, provides actionable feedback to the Generator. Be ruthlessly strict."
tools: ["Read", "Write", "Grep", "Glob"]
model: claude-sonnet-4-6
---

You are the Evaluator in a GAN-style multi-agent harness.

## Your Role

You are the Critic. You score the Generator's output 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.
> A passing score must mean the output is genuinely good — not "good for an AI."

Your natural tendency is to be generous. Fight it:
- Do NOT say "overall good effort" — this is 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 vague claims, AI slop patterns, and missing specifics
- DO compare against what a professional human would ship

## Evaluation Workflow

### Step 1: Read the Rubric
Read the criteria file for this task type.
Read the spec / brief for what was asked.
Read generator-state.md for what was built.

### Step 2: Score

Score each criterion on a 1-10 scale using the rubric file.

Calibration:
- 1-3: Broken or embarrassing
- 4-5: Functional but clearly AI-generated
- 6: Decent but unremarkable
- 7: Good — solid work
- 8: Very good — professional quality
- 9: Excellent — polished, senior quality
- 10: Exceptional — ships as-is

### Step 3: Write Feedback

Write to feedback/feedback-NNN.md:

# Evaluation — Iteration NNN

## Scores

| Criterion | Score | Weight | Weighted |
|-----------|-------|--------|----------|
| [criterion] | X/10 | 0.X | X.X |
| TOTAL | | | X.X/10 |

## Verdict: PASS / FAIL (threshold: 7.0)

## Critical Issues (must fix)
1. [Issue]: [exact quote] → [how to fix]

## Major Issues (should fix)
1. [Issue]: [exact quote] → [how to fix]

## Minor Issues (nice to fix)
1. [Issue]: [exact quote] → [how to fix]

## What Improved Since Last Iteration
- [improvement]

## Feedback Quality Rules

1. Every issue must have a concrete "how to fix" — not just "this is bad"
2. Reference specific elements — not "the hook needs work" but quote the exact text
3. Quantify when possible — "3 out of 5 items have no concrete numbers"
4. Acknowledge genuine improvements — calibrates the loop

{
  "default_threshold": 7.0,
  "max_iterations": 3,
  "escalation": "accept-with-notes",

  "profiles": {
    "my-task": {
      "generator": {
        "agent": "gan-generator",
        "skills": ["relevant-skill-here"]
      },
      "evaluator": {
        "agent": "gan-evaluator",
        "criteria_file": "evaluator/my-task-criteria.md"
      },
      "scoring": {
        "dimensions": [
          {"name": "hook_power",     "weight": 0.25},
          {"name": "value_density",  "weight": 0.25},
          {"name": "brand_voice",    "weight": 0.20},
          {"name": "clarity",        "weight": 0.20},
          {"name": "cta",            "weight": 0.10}
        ],
        "threshold": 7.0,
        "max_iterations": 3
      },
      "sprint_contract": [
        "Each item here is a binary gate check that must pass before shipping",
        "Example: hook lands before 210-char cutoff",
        "Example: no forbidden words",
        "Example: all claims verified against source of truth"
      ]
    }
  }
}

### Hook Power (weight: 0.20)

What you're measuring: Would this stop a busy professional from scrolling?
Must land before the 210-character cutoff.

Score 9-10 (Exceptional):
  "I was quoted $15,000 for an MVP. I built it for $197 in a weekend."
  Why: Specific dollar amounts, unexpected contrast, lands in 67 characters.
  Personal stake. Reader wants to know how.

Score 6-7 (Acceptable):
  "Building a SaaS doesn't have to take months. Here's a faster way."
  Why: Clear promise but no specificity, no numbers, no personal stake.

Score 1-4 (Reject):
  "In today's rapidly evolving tech landscape, AI is changing everything."
  Why: Generic, forbidden phrase, says nothing unique. Reader scrolls past.

In today's fast-paced world of software development, AI is completely
changing how we build products. Our innovative platform leverages
cutting-edge AI agents to help you build your SaaS faster and
more efficiently than ever before.

#AI #SaaS #startup #tech #innovation

DIMENSION SCORES:
- hook_power:           2/10 — forbidden phrase opener, zero specificity
- value_density:        2/10 — no concrete information in any sentence
- brand_alignment:      2/10 — "innovative", "cutting-edge" are banned words
- platform_optimization: 3/10 — 5 hashtags OK but no character discipline
- cta_effectiveness:    1/10 — no CTA present

OVERALL: 2.1/10

GATE CHECKS:
- No forbidden words: FAIL ("cutting-edge", "innovative", "fast-paced world")
- Single clear CTA: FAIL

VERDICT: reject

CRITICAL ISSUES:
1. Hook: "In today's fast-paced world" → forbidden phrase, replace with
   a specific personal story or data point. Example:
   "I rebuilt auth and payments 3 times in 2 years. Same 3-week slog. Every time."

I spent 3 weekends rebuilding the same auth and payments system.

With Build This Now, I describe the feature. 9 AI agents take over:
database architect designs the tables, backend developer writes the
API, designer builds the UI, tester catches the bugs.

One feature, 10-60 minutes. No more rebuilt boilerplate.

What are you still building by hand that you shouldn't be?

#BuildThisNow #indiehacker #buildinpublic #solofounder #SaaS

DIMENSION SCORES:
- hook_power:           7/10 — personal, relatable, but "I spent 3 weekends"
                               is passive. "3 weekends" needs more punch.
- value_density:        8/10 — concrete agent names, real time range
- brand_alignment:      8/10 — on-brand, hashtag count correct
- platform_optimization: 8/10 — good length, hashtags at end
- cta_effectiveness:    8/10 — question CTA invites comment

OVERALL: 7.7/10

GATE CHECKS: all pass

VERDICT: refine

MAJOR ISSUES:
1. Hook: "I spent 3 weekends" is weak. The data point "3 weekends" is good
   but "spent" is passive. Rewrite opener to front-load the pain with
   a harder verb. Example:
   "I rebuilt auth and payments 3 times in 2 years. Same 3-week slog. Every time."

I rebuilt auth and payments 3 times in 2 years. Same 3-week slog. Every time.

Then I described the feature in plain English and watched 9 AI agents
build it in 45 minutes: database, API, UI, tests. All wired. All clean.

Build This Now ships what used to take a week, in an afternoon.

What are you still rebuilding by hand?

#BuildThisNow #indiehacker #buildinpublic #solofounder #SaaS

DIMENSION SCORES:
- hook_power:           9/10 — "3 times in 2 years" is specific and damning
- value_density:        9/10 — every sentence adds new information
- brand_alignment:      9/10 — BTN voice, correct claims
- platform_optimization: 9/10 — clean format, right length
- cta_effectiveness:    8/10 — question CTA invites comment

OVERALL: 8.9/10

GATE CHECKS: all pass

VERDICT: accept

# Step 1: Generator runs first
claude -p --agent gan-generator \
  "Brief: write a LinkedIn post about [topic]. Save output to output/draft.md.
   Write generator-state.md with what you produced."

# Step 2: Evaluator scores it
claude -p --agent gan-evaluator \
  "Read output/draft.md and evaluator/linkedin-criteria.md.
   Score against the rubric. Write feedback to feedback/feedback-001.md.
   Be ruthlessly strict."

# Step 3: Generator iterates with feedback
claude -p --agent gan-generator \
  "Iteration 2. Read feedback/feedback-001.md FIRST.
   Address every issue. Update output/draft.md.
   Update generator-state.md."

# Repeat until VERDICT: accept

# Set env vars to configure the loop
GAN_MAX_ITERATIONS=5 GAN_PASS_THRESHOLD=7.5 \
  claude -p --agent loop-operator \
  "Run a GAN loop using gan-generator and gan-evaluator.
   Brief: [your task]. Criteria file: evaluator/my-criteria.md.
   Stop when score >= 7.5 or after 5 iterations."

{
  "max_iterations": 3,
  "escalation": "accept-with-notes"
}

# Install the plugin
/plugin marketplace add openai/codex-plugin-cc

# Use it in your loop
/codex:adversarial-review output/draft.md

{
  "profiles": {
    "adversarial": {
      "generator": {
        "agent": "gan-generator",
        "skills": ["linkedin-post"]
      },
      "evaluator": {
        "agent": "codex",
        "command": "/codex:adversarial-review",
        "criteria_file": "evaluator/linkedin-criteria.md"
      }
    }
  }
}

.claude/
  agents/
    gan-generator.md      ← generator agent definition
    gan-evaluator.md      ← evaluator agent definition
  subsystems/
    content/
      gan.json            ← loop config with profiles and thresholds
      evaluator/
        linkedin-criteria.md    ← rubric for LinkedIn posts
        carousel-text-criteria.md
        x-thread-criteria.md
        reddit-criteria.md
output/
  draft.md                ← generator output
  generator-state.md      ← what was built each iteration
  feedback/
    feedback-001.md       ← evaluator feedback per round
    feedback-002.md
    feedback-003.md