--- id: daily-gan-style-harness name: "gan-style-harness" url: https://skills.yangsir.net/skill/daily-gan-style-harness author: affaan-m domain: ai-agent-orchestration-collaboration tags: ["gan", "multi-agent", "adversarial-training", "generative-ai", "image-generation"] install_count: 2700 rating: 4.40 (7 reviews) github: https://github.com/affaan-m/everything-claude-code --- # gan-style-harness > 多智能体GAN训练系统，分离生成与评估流程，实现对抗性迭代优化，适用于图像生成和风格迁移 **Stats**: 2,700 installs · 4.4/5 (7 reviews) ## Before / After 对比 ### GAN模型训练 **Before**: 手动配置生成器和判别器网络，调整超参数、监控训练过程、手动调整损失权重，一个模型需要1周调优 **After**: 自动化的多智能体对抗训练系统，生成器与评估器独立优化，自动平衡损失权重，2天达到收敛并生成高质量图像 | Metric | Before | After | Change | |---|---|---|---| | 训练周期 | 7天 | 2天 | -71% | ## Readme # gan-style-harness # 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): **Design Quality** — Does it feel like a coherent whole? - **Originality** — Custom decisions vs. template/AI patterns? - **Craft** — Typography, spacing, animations, micro-interactions? - **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: ``` ## 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 ``` # 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 ``` # 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) ``` # 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 - **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. - **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. - **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. - **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. - **Evaluator praising its own fixes** — Never let the evaluator suggest fixes and then evaluate those fixes. The evaluator only critiques; the generator fixes. - **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 Weekly Installs568Repository[affaan-m/everyt…ude-code](https://github.com/affaan-m/everything-claude-code)GitHub Stars152.8KFirst Seen13 days agoSecurity Audits[Gen Agent Trust HubPass](/affaan-m/everything-claude-code/gan-style-harness/security/agent-trust-hub)[SocketPass](/affaan-m/everything-claude-code/gan-style-harness/security/socket)[SnykPass](/affaan-m/everything-claude-code/gan-style-harness/security/snyk)Installed oncodex530opencode506gemini-cli501antigravity501cursor501cline500 --- *Source: https://skills.yangsir.net/skill/daily-gan-style-harness* *Markdown mirror: https://skills.yangsir.net/api/skill/daily-gan-style-harness/markdown*