GAN-Style Harness Skill

Inspired by Anthropic's Harness Design for Long-Running Application Development (March 24, 2026)

A multi-agent harness that separates generation from evaluation, creating an adversarial feedback loop that drives quality far beyond what a single agent can achieve.

Core Insight

When asked to evaluate their own work, agents are pathological optimists — they praise mediocre output and talk themselves out of legitimate issues. But engineering a separate evaluator to be ruthlessly strict is far more tractable than teaching a generator to self-critique.

This is the same dynamic as GANs (Generative Adversarial Networks): the Generator produces, the Evaluator critiques, and that feedback drives the next iteration.

When to Use

• Building complete applications from a one-line prompt
• Frontend design tasks requiring high visual quality
• Full-stack projects that need working features, not just code
• Any task where "AI slop" aesthetics are unacceptable
• Projects where you want to invest $50-200 for production-quality output

When NOT to Use

• Quick single-file fixes (use standard claude -p)
• Tasks with tight budget constraints (<$10)
• Simple refactoring (use de-sloppify pattern instead)
• Tasks that are already well-specified with tests (use TDD workflow)

Architecture

                    ┌─────────────┐
                    │   PLANNER   │
                    │  (Sonnet)   │
                    └──────┬──────┘
                           │ Product Spec
                           │ (features, sprints, design direction)
                           ▼
              ┌────────────────────────┐
              │                        │
              │   GENERATOR-EVALUATOR  │
              │      FEEDBACK LOOP     │
              │                        │
              │  ┌──────────┐          │
              │  │GENERATOR │--build-->│──┐
              │  │ (Sonnet) │          │  │
              │  └────▲─────┘          │  │
              │       │                │  │ live app
              │    feedback             │  │
              │       │                │  │
              │  ┌────┴─────┐          │  │
              │  │EVALUATOR │<-test----│──┘
              │  │ (Sonnet) │          │
              │  │+Playwright│         │
              │  └──────────┘          │
              │                        │
              │   5-15 iterations      │
              └────────────────────────┘

The Three Agents

1. Planner Agent

Role: Product manager — expands a brief prompt into a full product specification.

Key behaviors:

• Takes a one-line prompt and produces a 16-feature, multi-sprint specification
• Defines user stories, technical requirements, and visual design direction
• Is deliberately ambitious — conservative planning leads to underwhelming results
• Produces evaluation criteria that the Evaluator will use later

Model: Sonnet by default; raise via GAN_PLANNER_MODEL=opus for deeper spec expansion

2. Generator Agent

Role: Developer — implements features according to the spec.

Key behaviors:

• Works in structured sprints (or continuous mode with newer models)
• Negotiates a "sprint contract" with the Evaluator before writing code
• Uses full-stack tooling: React, FastAPI/Express, databases, CSS
• Manages git for version control between iterations
• Reads Evaluator feedback and incorporates it in next iteration

Model: Sonnet by default; raise via GAN_GENERATOR_MODEL=opus for maximum coding capability

3. Evaluator Agent

Role: QA engineer — tests the live running application, not just code.

Key behaviors:

• Uses Playwright MCP to interact with the live application
• Clicks through features, fills forms, tests API endpoints
• Scores against four criteria (configurable):
  1. Design Quality — Does it feel like a coherent whole?
  2. Originality — Custom decisions vs. template/AI patterns?
  3. Craft — Typography, spacing, animations, micro-interactions?
  4. Functionality — Do all features actually work?
• Returns structured feedback with scores and specific issues
• Is engineered to be ruthlessly strict — never praises mediocre work

Model: Sonnet by default; raise via GAN_EVALUATOR_MODEL=opus for stronger judgment + tool use

Evaluation Criteria

The default four criteria, each scored 1-10:

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