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astro-cta-injectorastro cta 注射器

Agent Skill

用于辅助前端页面、组件、样式和交互逻辑的开发与维护。它适合让 Agent 生成或审查 React、Next.js、Vue、Tailwind、CSS 等相关代码,整理组件结构,或定位布局和性能问题。使用时需要结合项目现有设计系统、路由和构建方式,避免只生成孤立片段;涉及页面改动时,应配合本地预览和构建检查确认视觉效果。

总安装

1,073

周安装

43

GitHub Stars

174

下载量

347
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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:astro-cta-injector(astro cta 注射器)
来源仓库:https://github.com/nicepkg/ai-workflow
仓库路径:skills/astro-cta-injector
安装命令:
npx skills add https://github.com/nicepkg/ai-workflow --skill astro-cta-injector
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/nicepkg/ai-workflow --skill astro-cta-injector

简介

用于向 Astro 站点内容智能注入 Call-to-Action 区块,支持 newsletter、产品推广等类型。

  • 适用于批量处理博客文章或着陆页,按内容相关性评分并预览插入位置与样式。
  • 使用时指定目标文件或目录,选择放置策略与 CTA 类型,输出带预览的可执行结果。
  • 安装方式为 GitHub,支持 Codex、Claude、Cursor 和 Gemini CLI,需确保 Astro 项目结构完整。
  • astro-cta-injector 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Astro CTA Injector Skill

Purpose

This skill injects Call-to-Action (CTA) blocks into Astro site content. It supports:

  • Multiple CTA types (newsletter, product, custom)
  • Intelligent placement strategies
  • Content-based relevance scoring
  • Batch processing with preview

When to Use This Skill

  • User asks to "add CTAs to blog posts"
  • User wants to "inject newsletter signup" into content
  • User mentions "add product promotion" to posts
  • User needs to batch-add any type of content block to posts
  • User wants to "add calls to action" to their Astro site

Prerequisites

  • Astro site with content in .astro or .md files
  • Python 3.10+
  • BeautifulSoup4 for HTML parsing

Configuration

Create a config.json in the skill directory:

{
  "content_path": "./src/content/blog",
  "file_patterns": ["*.astro", "*.md"],
  "cta_types": {
    "newsletter": {
      "template": "newsletter.html",
      "default_placement": "after-paragraph-50%",
      "keywords": ["tip", "guide", "learn", "strategy"]
    },
    "product": {
      "template": "product.html",
      "default_placement": "end",
      "keywords": ["productivity", "task", "habit", "goal"]
    }
  },
  "output": {
    "state_file": "./state/cta_injection_progress.json",
    "backup_dir": "./backups",
    "report_file": "./reports/cta_injection_report.md"
  },
  "dry_run": true
}

Placement Strategies

StrategyDescriptionBest For
endAfter all contentNon-intrusive CTAs
after-paragraph-50%After 50% of paragraphsMid-content engagement
after-paragraph-60%After 60% of paragraphsLater engagement
after-headingAfter first H2Early engagement
before-conclusionBefore last paragraphStrong finish

CTA Templates

Templates are HTML files in the templates/ directory:

<!-- templates/newsletter.html -->
<aside class="cta-newsletter" data-cta-type="newsletter">
  <h3>{{title}}</h3>
  <p>{{description}}</p>
  <form action="{{form_url}}" method="post">
    <input type="email" placeholder="Your email" required />
    <button type="submit">Subscribe</button>
  </form>
</aside>

Variables:

  • {{title}} - CTA headline
  • {{description}} - CTA body text
  • {{form_url}} - Form submission URL
  • {{product_url}} - Product link
  • {{image_url}} - Image source

Workflow

Step 1: Score Posts for Relevance

python scripts/score_posts.py --content-path ./src/content/blog --cta-type newsletter

Step 2: Preview Injections

python scripts/preview_injection.py --input scored_posts.json --cta-type newsletter

Step 3: Apply Injections

python scripts/inject_ctas.py --input scored_posts.json --cta-type newsletter

Input Format

Scored posts JSON:

{
  "posts": [
    {
      "file_path": "./src/content/blog/my-post.astro",
      "title": "My Blog Post",
      "relevance_score": 8.5,
      "cta_type": "newsletter",
      "placement": "after-paragraph-50%",
      "cta_data": {
        "title": "Get More Tips Like This",
        "description": "Subscribe to my weekly newsletter"
      }
    }
  ]
}

Scoring Algorithm

Posts are scored for CTA relevance based on:

  1. Keyword density - How many relevant keywords appear
  2. Content length - Longer posts = better candidates
  3. Topic match - Title and content topic alignment
  4. Existing CTAs - Skip posts that already have CTAs

Scores range from 0-10. Default threshold: 5.0

Safety Features

  • Dry-run mode by default
  • Backup creation before any modifications
  • Duplicate detection - Won't inject if CTA already exists
  • Rollback capability - Restore from backups
  • Preview diffs - See exactly what will change

Example Usage

User: "Add a newsletter signup CTA to all my productivity-related blog posts"

Claude will:

  1. Scan content directory for posts
  2. Score posts for "newsletter" relevance using productivity keywords
  3. Generate CTA HTML from template
  4. Show preview of changes
  5. Ask for confirmation
  6. Inject CTAs into matching posts
  7. Report results

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.64%
按下载量换算99

OpenCode

23.26%
按下载量换算81

Cursor

17.38%
按下载量换算60

trae

12.66%
按下载量换算44

Gemini CLI

7.5%
按下载量换算26

Codex

3.64%
按下载量换算13

安全审计

Gen Agent Trust Hub

未通过

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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