anti-render▌
lionad-morotar/anti-render-skill · updated Apr 8, 2026
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Generate \"ideal promise vs cruel reality\" visual comparisons by analyzing image content across any domain.
- ›Automatically detects image domain (architecture, portrait, product, food, travel, gaming, fitness, home, tech) and current state (deteriorated, ideal, or normal)
- ›Outputs three modes: idealized rendering, realistic everyday appearance, or side-by-side comparison with automatic layout (left-right or top-bottom based on aspect ratio)
- ›Applies five universal contrast dimensions ac
Anti-Render 理想vs现实视觉对比生成器
核心理念
通过并置(juxtaposition)手法,揭示任何领域中"承诺与交付之间巨大落差"的普遍困境。左侧呈现理想化的完美渲染,右侧揭示真实的日常面貌。
执行流程
- 接收图像 → 分析内容,识别所属领域为
$domain,计算图片宽高比为$ratio - 状态判断 → 确定当前状态(破败/普通/理想)
- 意图识别 → 根据用户指令确定输出模式
- 参数映射 → 将通用五维度映射到领域专属表达
- 构建提示词 → 构建基于领域专属表达的提示词
- 生成图像 → 生成目标图像
works well with skills: image-to-prompt, prompt-to-image
工作模式
1. 图像状态识别
用户上传图片后,分析其当前状态:
| 状态 | 特征 | 输出目标 |
|---|---|---|
| 破败 | 质量问题、使用痕迹、维护不良 | 生成理想化渲染图(即 step 2.1) |
| 理想 | 用户上传了营销图片、广告图片等精修后照片 | 生成轻微破败渲染图片(即 step 2.2) |
| 普通/正常 | 无明显破损、日常使用状态 | 生成理想化渲染和轻微破败的对比图(即 step 2.3) |
如状态模糊,无法判断意图,主动询问用户期望方向
2. 三种输出模式
2.1 理想化渲染 (Ideal)
- 目标:输出对应领域的宣传级别完美呈现
- 特征:高饱和度、完美光影、无瑕疵、精心构图
2.2 真实面貌 (Reality)
- 目标:输出对应领域日常的真实状态(非破败)
- 特征:自然光线(“死亡打光”)、真实质感、日常氛围、未经修饰
2.3 对比图 (Comparison)
- 目标:输出理想化渲染和真实面貌并置的对比图
- 排列:根据原图宽高比自动选择左右或上下排列
领域识别与适配
领域检测
基于图像内容关键词匹配:
建筑领域: 建筑外观、城市景观、室内空间、建筑效果图、楼盘、住宅、商业空间
人像领域: 人像写真、Cosplay、自拍、证件照、活动拍摄、肖像
产品领域: 电商产品、商品展示、包装设计、电子产品、服饰
食物领域: 美食摄影、菜品展示、烘焙、饮品、餐厅菜单
旅游领域: 风景照、景点打卡、酒店房间、度假胜地
游戏领域: 游戏截图、游戏宣传、UI界面、角色设计
健身领域: 健身照、运动场景、瑜伽、健身房
家居领域: 室内装修、家具展示、样板间、智能家居
科技领域: 产品发布会、概念设计、VR/AR、智能汽车
核心对比维度(通用框架)
所有领域共享以下五个核心对比维度:
1. 光影 (Lighting)
| 理想侧 | 现实侧 |
|---|---|
| 精心计算的完美光照 | 自然/现场实际光线 |
| 黄金时刻或柔和补光 | 硬光、顶光或平淡漫射光 |
| 明暗层次丰富、无死黑/过曝 | 曝光妥协、阴影浓重 |
| 方向性明确、立体感强 | 低对比度、缺乏层次 |
2. 材质 (Material)
| 理想侧 | 现实侧 |
|---|---|
| 完美无瑕的表面 | 真实使用痕迹 |
| 色彩饱和、质感强化 | 褪色、污渍、磨损 |
| 无灰尘、无水痕、无瑕疵 | 自然老化、环境痕迹 |
| CG般的精确反射/折射 | 混浊、不完美的反射 |
3. 色彩 (Color)
| 理想侧 | 现实侧 |
|---|---|
| 高饱和度、鲜艳夺目 | 低饱和度、略显平淡 |
| 色温精准、统一协调 | 色温偏移、白平衡未校正 |
| 后期精修的色彩增强 | 相机原生色彩还原 |
| 广告级别的视觉吸引力 | 日常感、朴素感 |
4. 氛围 (Atmosphere)
| 理想侧 | 现实侧 |
|---|---|
| 充满活力、生机勃勃 | 冷清、平凡或略显尴尬 |
| 精心布置的场景元素 | 杂乱的现场环境 |
| 梦幻、理想化的背景 | 真实、暴露现场的环境 |
| 情绪饱满、引人入胜 | 纪实感、冷峻客观 |
5. 构图/细节 (Composition)
| 理想侧 | 现实侧 |
|---|---|
| 完美的透视与比例 | 自然的镜头畸变 |
| 瑕疵移除、穿帮修复 | 保留所有现场细节 |
| 精心安排的元素布局 | 随机、不规则的真实分布 |
| 后期添加的特效/光效 | 无后期加持的原始状态 |
对比图技术规范
排列规则
如生成对比图,需根据原图宽高比 $ratio 判断新的排列规则:
- 原图横向(宽 > 高):对比图上下排列,水平分割线
- 原图纵向(高 > 宽):对比图左右排列,垂直分割线
分割线规范
- 位置:画面正中
- 宽度:2-5像素
- 颜色:纯白或极浅灰
- 边缘:锐利清晰,无羽化
内容规则
- 对比图左或上(根据排列规则):理想化渲染
- 对比图右或下(根据排列规则):普通现实主义(默认)或破败状态(用户明确要求强烈对比效果)
- 默认不要添加标题文字,保持画面纯净,让视觉对比本身说话
How to use anti-render on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add anti-render
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches anti-render from GitHub repository lionad-morotar/anti-render-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate anti-render. Access the skill through slash commands (e.g., /anti-render) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★46 reviews- ★★★★★Hiroshi Iyer· Dec 20, 2024
anti-render is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Naina Sharma· Dec 20, 2024
Keeps context tight: anti-render is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 16, 2024
Solid pick for teams standardizing on skills: anti-render is focused, and the summary matches what you get after install.
- ★★★★★Sophia Bhatia· Dec 8, 2024
Solid pick for teams standardizing on skills: anti-render is focused, and the summary matches what you get after install.
- ★★★★★Yuki Torres· Dec 4, 2024
We added anti-render from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hiroshi Jain· Nov 23, 2024
Keeps context tight: anti-render is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★William Anderson· Nov 11, 2024
anti-render fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Tariq Reddy· Nov 11, 2024
We added anti-render from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Noor Garcia· Oct 14, 2024
anti-render is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ishan Yang· Oct 2, 2024
We added anti-render from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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