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feat: add GAN-style generator-evaluator harness (#1029)

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Implements Anthropic's March 2026 harness design pattern — a multi-agent
architecture that separates generation from evaluation, creating an
adversarial feedback loop that produces production-quality applications.

Components:
- 3 agent definitions (planner, generator, evaluator)
- 1 skill with full documentation (skills/gan-style-harness/)
- 2 commands (gan-build for full apps, gan-design for frontend)
- 1 shell orchestrator (scripts/gan-harness.sh)
- Examples and configuration reference

Based on: https://www.anthropic.com/engineering/harness-design-long-running-apps

Co-authored-by: Hao Chen <haochen806@gmail.com>
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---
name: gan-style-harness
description: "GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper."
origin: ECC-community
tools: Read, Write, Edit, Bash, Grep, Glob, Task
---
# GAN-Style Harness Skill
> Inspired by [Anthropic's Harness Design for Long-Running Application Development](https://www.anthropic.com/engineering/harness-design-long-running-apps) (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 │
│ (Opus 4.6) │
└──────┬──────┘
│ Product Spec
│ (features, sprints, design direction)
▼
┌────────────────────────┐
│ │
│ GENERATOR-EVALUATOR │
│ FEEDBACK LOOP │
│ │
│ ┌──────────┐ │
│ │GENERATOR │──build──▶│──┐
│ │(Opus 4.6)│ │ │
│ └────▲─────┘ │ │
│ │ │ │ live app
│ feedback │ │
│ │ │ │
│ ┌────┴─────┐ │ │
│ │EVALUATOR │◀─test───│──┘
│ │(Opus 4.6)│ │
│ │+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:** Opus 4.6 (needs deep reasoning for 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:** Opus 4.6 (needs strong 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:** Opus 4.6 (needs strong judgment + tool use)
## Evaluation Criteria
The default four criteria, each scored 1-10:
```markdown
## Evaluation Rubric
### Design Quality (weight: 0.3)
- 1-3: Generic, template-like, "AI slop" aesthetics
- 4-6: Competent but unremarkable, follows conventions
- 7-8: Distinctive, cohesive visual identity
- 9-10: Could pass for a professional designer's work
### Originality (weight: 0.2)
- 1-3: Default colors, stock layouts, no personality
- 4-6: Some custom choices, mostly standard patterns
- 7-8: Clear creative vision, unique approach
- 9-10: Surprising, delightful, genuinely novel
### Craft (weight: 0.3)
- 1-3: Broken layouts, missing states, no animations
- 4-6: Works but feels rough, inconsistent spacing
- 7-8: Polished, smooth transitions, responsive
- 9-10: Pixel-perfect, delightful micro-interactions
### Functionality (weight: 0.2)
- 1-3: Core features broken or missing
- 4-6: Happy path works, edge cases fail
- 7-8: All features work, good error handling
- 9-10: Bulletproof, handles every edge case
```
### Scoring
- **Weighted score** = sum of (criterion_score * weight)
- **Pass threshold** = 7.0 (configurable)
- **Max iterations** = 15 (configurable, typically 5-15 sufficient)
## Usage
### Via Command
```bash
# Full three-agent harness
/project:gan-build "Build a project management app with Kanban boards, team collaboration, and dark mode"
# With custom config
/project:gan-build "Build a recipe sharing platform" --max-iterations 10 --pass-threshold 7.5
# Frontend design mode (generator + evaluator only, no planner)
/project:gan-design "Create a landing page for a crypto portfolio tracker"
```
### Via Shell Script
```bash
# Basic usage
./scripts/gan-harness.sh "Build a music streaming dashboard"
# With options
GAN_MAX_ITERATIONS=10 \
GAN_PASS_THRESHOLD=7.5 \
GAN_EVAL_CRITERIA="functionality,performance,security" \
./scripts/gan-harness.sh "Build a REST API for task management"
```
### Via Claude Code (Manual)
```bash
# Step 1: Plan
claude -p --model opus "You are a Product Planner. Read PLANNER_PROMPT.md. Expand this brief into a full product spec: 'Build a Kanban board app'. Write spec to spec.md"
# Step 2: Generate (iteration 1)
claude -p --model opus "You are a Generator. Read spec.md. Implement Sprint 1. Start the dev server on port 3000."
# Step 3: Evaluate (iteration 1)
claude -p --model opus --allowedTools "Read,Bash,mcp__playwright__*" "You are an Evaluator. Read EVALUATOR_PROMPT.md. Test the live app at http://localhost:3000. Score against the rubric. Write feedback to feedback-001.md"
# Step 4: Generate (iteration 2 — reads feedback)
claude -p --model opus "You are a Generator. Read spec.md and feedback-001.md. Address all issues. Improve the scores."
# Repeat steps 3-4 until pass threshold met
```
## Evolution Across Model Capabilities
The harness should simplify as models improve. Following Anthropic's evolution:
### Stage 1 — Weaker Models (Sonnet-class)
- Full sprint decomposition required
- Context resets between sprints (avoid context anxiety)
- 2-agent minimum: Initializer + Coding Agent
- Heavy scaffolding compensates for model limitations
### Stage 2 — Capable Models (Opus 4.5-class)
- Full 3-agent harness: Planner + Generator + Evaluator
- Sprint contracts before each implementation phase
- 10-sprint decomposition for complex apps
- Context resets still useful but less critical
### Stage 3 — Frontier Models (Opus 4.6-class)
- Simplified harness: single planning pass, continuous generation
- Evaluation reduced to single end-pass (model is smarter)
- No sprint structure needed
- Automatic compaction handles context growth
> **Key principle:** Every harness component encodes an assumption about what the model can't do alone. When models improve, re-test those assumptions. Strip away what's no longer needed.
## Configuration
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `GAN_MAX_ITERATIONS` | `15` | Maximum generator-evaluator cycles |
| `GAN_PASS_THRESHOLD` | `7.0` | Weighted score to pass (1-10) |
| `GAN_PLANNER_MODEL` | `opus` | Model for planning agent |
| `GAN_GENERATOR_MODEL` | `opus` | Model for generator agent |
| `GAN_EVALUATOR_MODEL` | `opus` | Model for evaluator agent |
| `GAN_EVAL_CRITERIA` | `design,originality,craft,functionality` | Comma-separated criteria |
| `GAN_DEV_SERVER_PORT` | `3000` | Port for the live app |
| `GAN_DEV_SERVER_CMD` | `npm run dev` | Command to start dev server |
| `GAN_PROJECT_DIR` | `.` | Project working directory |
| `GAN_SKIP_PLANNER` | `false` | Skip planner, use spec directly |
| `GAN_EVAL_MODE` | `playwright` | `playwright`, `screenshot`, or `code-only` |
### Evaluation Modes
| Mode | Tools | Best For |
|------|-------|----------|
| `playwright` | Browser MCP + live interaction | Full-stack apps with UI |
| `screenshot` | Screenshot + visual analysis | Static sites, design-only |
| `code-only` | Tests + linting + build | APIs, libraries, CLI tools |
## Anti-Patterns
1. **Evaluator too lenient** — If the evaluator passes everything on iteration 1, your rubric is too generous. Tighten scoring criteria and add explicit penalties for common AI patterns.
2. **Generator ignoring feedback** — Ensure feedback is passed as a file, not inline. The generator should read `feedback-NNN.md` at the start of each iteration.
3. **Infinite loops** — Always set `GAN_MAX_ITERATIONS`. If the generator can't improve past a score plateau after 3 iterations, stop and flag for human review.
4. **Evaluator testing superficially** — The evaluator must use Playwright to **interact** with the live app, not just screenshot it. Click buttons, fill forms, test error states.
5. **Evaluator praising its own fixes** — Never let the evaluator suggest fixes and then evaluate those fixes. The evaluator only critiques; the generator fixes.
6. **Context exhaustion** — For long sessions, use Claude Agent SDK's automatic compaction or reset context between major phases.
## Results: What to Expect
Based on Anthropic's published results:
| Metric | Solo Agent | GAN Harness | Improvement |
|--------|-----------|-------------|-------------|
| Time | 20 min | 4-6 hours | 12-18x longer |
| Cost | $9 | $125-200 | 14-22x more |
| Quality | Barely functional | Production-ready | Phase change |
| Core features | Broken | All working | N/A |
| Design | Generic AI slop | Distinctive, polished | N/A |
**The tradeoff is clear:** ~20x more time and cost for a qualitative leap in output quality. This is for projects where quality matters.
## References
- [Anthropic: Harness Design for Long-Running Apps](https://www.anthropic.com/engineering/harness-design-long-running-apps) — Original paper by Prithvi Rajasekaran
- [Epsilla: The GAN-Style Agent Loop](https://www.epsilla.com/blogs/anthropic-harness-engineering-multi-agent-gan-architecture) — Architecture deconstruction
- [Martin Fowler: Harness Engineering](https://martinfowler.com/articles/exploring-gen-ai/harness-engineering.html) — Broader industry context
- [OpenAI: Harness Engineering](https://openai.com/index/harness-engineering/) — OpenAI's parallel work