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Agent Skill · 前端设计

promptor(promptor 前端)

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

promptor 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 5,784。

总安装 606周安装 25GitHub Stars 7下载 198核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:promptor(promptor 前端)
来源仓库:https://github.com/thangphistudent/nano-banana-2-promptor-skill
仓库路径:skills/promptor
安装命令:
npx skills add https://github.com/thangphistudent/nano-banana-2-promptor-skill --skill promptor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/thangphistudent/nano-banana-2-promptor-skill --skill promptor

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

用于辅助前端界面设计与交互逻辑的开发支持。promptor 属于前端设计类 Skill,可作为该场景下的辅助能力补充。适合生成或审查 React、Vue 组件及样式代码片段。使用时需结合项目现有设计系统和构建流程进行集成。建议通过本地预览确认布局、响应式和文本表现效果。避免只输出孤立代码块,应与路由和状态管理协同考虑。

来源仓库内路径:skills/promptor

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/thangphistudent/nano-banana-2-promptor-skill -…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

promptor 怎么安装?

在终端执行 npx skills add https://github.com/thangphistudent/nano-banana-2-promptor-skill --skill pro…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

promptor 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

promptor 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

promptor 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

promptor 用的人多吗?

公开数据是累计安装 606 次,近一周安装 25 次,下载 198 次,GitHub 7 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

promptor 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
promptor

promptor 前端 · 本页

6067只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Nano Banana 2 Promptor

Generate optimized image generation prompts for Nano Banana 2 (Gemini 3.1) from natural conversation context. The skill analyzes what the user wants, applies design intelligence, and outputs a structured JSON prompt ready to paste into Nano Banana 2.

Why This Skill Exists

Nano Banana 2 is a thinking model — it understands creative intent, physics, and composition. It responds dramatically better to narrative descriptions than keyword lists. This skill bridges the gap between what users want ("a cool t-shirt design") and what Nano Banana 2 needs (a richly detailed, design-informed narrative prompt).

Core Pipeline

1. CONTEXT PARSER  → detect/infer dimensions from any input format
2. CATEGORY ROUTER → POD path or Commercial path
3. PROMPT COMPOSER → narrative prompt with design intelligence
4. JSON OUTPUT     → structured output ready for use

Step 1: Parse Context

Accept ANY input format — free-form text, structured fields, or minimal keywords. Extract these 8 dimensions:

DimensionWhat to detectInference when missing
subjectThe core product/object/conceptRequired — ask if truly ambiguous
categoryOne of the 10 categories belowInfer from subject + purpose
audienceTarget demographic/psychographicInfer from subject + style
purposeWhat the image is for (platform/use)Default: "general commercial use"
style_hintsAny style/aesthetic directionInfer via context (see Style Inference)
text_contentText to render in the imageNone unless specified
brand_voiceTone/personalityInfer from style + audience
constraintsWhat to avoid or must includeNone unless specified

10 Supported Categories

CategoryTriggers on
product_heroProduct shots, studio photography, hero images
lifestyleProducts in real-world settings, contextual scenes
social_mediaInstagram, Facebook, Pinterest, TikTok content
pod_tshirtT-shirt designs, apparel graphics
pod_mugMug designs, drinkware, wrap-around
pod_posterPosters, wall art, fine art prints
pod_accessoriesPhone cases, tote bags, stickers
marketing_bannerWeb banners, email headers, ad creatives
infographicData visuals, comparison charts, bento grids
logo_brandLogos, brand marks, icons

Style Inference

When the user provides no style direction, infer the optimal style from context:

For POD categories — read references/niche-modules.md and select the closest niche module based on subject + audience. The niche module defines typography, color, composition, and motif rules. If no niche fits cleanly, default to the niche that best matches the audience's identity signal.

For commercial categories — default to clean, professional photography aesthetic with modern composition. Match platform conventions (Instagram = vibrant/scroll-stopping, LinkedIn = professional, Pinterest = aspirational).

Step 2: Route by Category

POD Path (pod_tshirt, pod_mug, pod_poster, pod_accessories)

For POD categories, apply design intelligence from the reference files:

  1. Read references/niche-modules.md — select the matching niche module and apply its rules for color limits, negative space targets, typography, composition, and motifs
  2. Read references/design-principles.md — apply core design philosophy principles
  3. Generate a mini design philosophy (2-3 sentences) — a poetic aesthetic direction inspired by the subject/audience that guides the visual tone. Think of it as naming a tiny art movement. Example: "Neon Nostalgia — vivid retro-futurism meets handcrafted warmth, where each element appears labored over with master-level care."
  4. Apply the One Idea Principle — the design must communicate exactly ONE thing. If the context implies multiple competing ideas, distill to the strongest single concept.
  5. Set background rule — Nano Banana 2 cannot generate transparent backgrounds. Instead:

- If design is predominantly dark → use pure solid white background (#FFFFFF) - If design is light/colorful → use pure solid black background (#000000) - Always add: "clean isolated background with sharp edges, no gradients, no ambient shadows, no noise — optimized for AI background removal"

Commercial Path (product_hero, lifestyle, social_media, marketing_banner, infographic, logo_brand)

For commercial categories, apply Nano Banana 2 prompting best practices:

  1. Read references/prompt-patterns.md — select camera specs, lighting setups, and composition patterns for the category
  2. Set platform-specific aspect ratio:

- Instagram feed: 4:5 - Instagram story / TikTok: 9:16 - Facebook ad: 1:1 or 4:5 - Pinterest: 2:3 - Web banner: 16:9 or 21:9 - Email header: 3:1 - General: 4:3 or 16:9

  1. For product_hero — add camera hardware language (e.g., "Sony A7III, 85mm f/1.4, studio softbox lighting") as this dramatically improves photorealistic output
  2. For marketing_banner — reserve negative space for text overlay, describe composition zones explicitly
  3. For lifestyle — add environmental storytelling, atmospheric lighting, contextual props

Step 3: Compose the Prompt

Assemble a narrative-style prompt (not keyword lists). Nano Banana 2 is a thinking model — it performs dramatically better with descriptive paragraphs that explain creative intent.

Prompt Formula

[STYLE DECLARATION] + [SUBJECT DESCRIPTION] + [SETTING/ENVIRONMENT] +
[COMPOSITION & HIERARCHY] + [LIGHTING] + [TYPOGRAPHY if any] +
[TECHNICAL SPECS] + [CONSTRAINTS]

Writing Rules

  • Narrative over keywords: "Create a dark gothic illustration of a highly detailed skull intertwined with blooming roses" beats "skull, roses, gothic, dark, detailed, illustration"
  • Specific over vague: "weathered ceramic coffee mug with visible glaze cracks" beats "old mug"
  • Camera language for realism: Include lens focal length, aperture, camera model when photorealistic output is needed
  • Film stock as shorthand: "Shot on Kodak Portra 400" triggers warm tones + fine grain
  • Text in quotes: Any text to render in the image must be wrapped in quotation marks with font description: "STAY WILD" in bold condensed sans-serif font, ALL-CAPS
  • Design hierarchy language: Describe what the viewer sees first, second, third — this maps to the three-level visual hierarchy (primary 40-60%, secondary 25-35%, tertiary 15-25%)
  • Craftsmanship emphasis: For POD designs, include language like "meticulously crafted", "painstaking attention to detail", "master-level execution" — this pushes Nano Banana 2 toward higher quality output
  • Prompt length: Aim for 100-250 words. Enough detail to guide the model, not so much that it gets confused.

Anti-Pattern Guards

Before finalizing the prompt, verify it does NOT contain:

  • Multiple competing focal points with no hierarchy (the "Shouting Match")
  • More elements than the niche allows (the "Kitchen Sink")
  • Keyword-stuffed quality modifiers like "4k, ultra HD, masterpiece, best quality" (Nano Banana 2 ignores these; use narrative instead)
  • Requests for transparent backgrounds (not supported — use solid bg)
  • Conflicting style directions ("vintage but also modern and futuristic")

Step 4: Output JSON

Always output in this exact structure:

{
  "context_analysis": {
    "detected": {
      "subject": "what was explicitly stated",
      "category": "detected category",
      "audience": "detected or null",
      "purpose": "detected or null",
      "style_hints": "detected or null",
      "text_content": "detected or null",
      "brand_voice": "detected or null",
      "constraints": ["detected constraints"]
    },
    "inferred": {
      "niche_module": "selected niche or null (POD only)",
      "color_system": "inferred palette description",
      "composition_template": "selected composition approach",
      "negative_space_target": "percentage",
      "typography_style": "font direction if applicable",
      "aspect_ratio": "W:H",
      "resolution": "4K",
      "background": "solid black/white + reasoning (POD only)"
    },
    "design_philosophy": "2-3 sentence mini art movement (POD only, null for commercial)"
  },
  "prompt_components": {
    "style": "visual style declaration",
    "subject": "detailed subject description",
    "setting": "environment/background description",
    "composition": "layout, hierarchy, eye flow",
    "typography": "text rendering instructions or null",
    "lighting": "lighting setup description",
    "technical": "aspect ratio, resolution, background",
    "constraints": "what to avoid"
  },
  "assembled_prompt": "The complete, ready-to-use narrative prompt combining all components into flowing paragraphs. This is what gets pasted directly into Nano Banana 2.",
  "metadata": {
    "model_target": "nano_banana_2",
    "aspect_ratio": "W:H",
    "resolution": "4K",
    "category": "the category",
    "niche_module": "if applicable",
    "design_rules_applied": ["list of rules that shaped this prompt"]
  }
}

Reference Files

These contain detailed domain knowledge. Read them as needed based on the category:

references/niche-modules.md

Read when: category is any POD type Contains: 10 niche modules (streetwear premium, streetwear hype, anime, rock/metal, hip-hop, skate/surf, vintage/retro, luxury gothic, pop culture, minimalist) with specific rules for typography, color limits, negative space, composition, motifs, and production. Also contains the niche selection decision tree.

references/prompt-patterns.md

Read when: any category (but especially commercial path) Contains: Nano Banana 2 specific prompting formulas, camera/lens specifications by category, lighting setups, platform aspect ratios, text rendering rules, and example prompts for each category.

references/design-principles.md

Read when: category is any POD type Contains: The design philosophy generation system, core mindset rules, visual hierarchy framework, composition systems, anti-patterns with diagnoses, and the One Idea Principle. Distilled from validated streetwear brand analysis.

Important Reminders

  • Nano Banana 2 is a THINKING model. Write prompts that explain intent, not just list attributes.
  • For POD: always use solid background (black or white) for easy AI removal. Never say "transparent background."
  • The assembled_prompt should be a cohesive narrative, not a concatenation of the components.
  • Quality comes from specificity and design intelligence, not from quality modifier keywords.
  • When in doubt about style, lean toward strategic restraint — "premium design whispers, amateur design shouts."

平台分布

Codex

33.3%
按下载量换算66

Claude

30.1%
按下载量换算60

Cursor

17.41%
按下载量换算34

Gemini CLI

9.99%
按下载量换算20

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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