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competitor-content-tracker竞争对手内容跟踪器

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

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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

2

许可证

unknown

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:competitor-content-tracker(竞争对手内容跟踪器)
来源仓库:https://github.com/nikiandr/goose-skills
仓库路径:skills/competitor-content-tracker
安装命令:
npx skills add https://github.com/nikiandr/goose-skills --skill competitor-content-tracker
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/nikiandr/goose-skills --skill competitor-content-tracker

简介

聚合三大传播渠道(博客/LinkedIn/Twitter)的内容活动态势感知。

  • 生成每周简报突出新发内容、爆款话题与自身内容差距量化指标。
  • 支持自定义追踪名单与高管账号扩展监测覆盖面至决策层发声。
  • 必须提供至少一个有效博客URL作为初始锚点展开全网扫描作业。
  • competitor-content-tracker 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Competitor Content Tracker

Monitor competitor content activity across three channels — blog, LinkedIn, Twitter/X — and produce a consolidated digest highlighting what's new, what's getting traction, and where you have a content gap.

When to Use

  • "Track what [competitor] is publishing"
  • "Show me what my competitors posted this week"
  • "What topics are competitors winning on?"
  • "I want a weekly competitor content digest"

Phase 0: Intake

Competitors to Track

  1. List of competitor company names + blog URLs (e.g., https://clay.com/blog)
  2. LinkedIn profile URLs of competitor founders/CMOs to track (optional but high-value)
  3. Twitter/X handles of the competitors or their founders (optional)

Scope

  1. How far back? (default: 7 days for weekly digest, 30 days for first run)
  2. Any topics/keywords you care most about? (used to surface relevant posts first)

Output

  1. Format preference: full digest (everything) or highlights only (top 3-5 per competitor)?

Save config to clients/<client-name>/configs/competitor-content-tracker.json.

{
  "competitors": [
    {
      "name": "Clay",
      "blog_url": "https://clay.com/blog",
      "linkedin_profiles": ["https://www.linkedin.com/in/kareem-amin/"],
      "twitter_handles": ["@clay_hq", "@kareemamin"]
    }
  ],
  "days_back": 7,
  "keywords": ["GTM", "outbound", "AI agents", "growth"],
  "output_mode": "highlights"
}

Phase 1: Scrape Blog Content

Run blog-scraper for each competitor blog URL:

python3 skills/blog-scraper/scripts/scrape_blogs.py \
  --urls "<competitor_blog_url>" \
  --days <days_back> \
  --keywords "<keywords>" \
  --output summary

Collect: post title, publish date, URL, excerpt.

Phase 2: Scrape LinkedIn Posts

Run linkedin-profile-post-scraper for each tracked founder/executive LinkedIn URL:

python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
  --profiles "<linkedin_url_1>,<linkedin_url_2>" \
  --days <days_back> \
  --max-posts 20 \
  --output summary

Collect: post text preview, date, reactions, comments, post URL.

Phase 3: Scrape Twitter/X

Run twitter-scraper for each handle:

python3 skills/twitter-scraper/scripts/search_twitter.py \
  --query "from:<handle>" \
  --since <YYYY-MM-DD> \
  --until <YYYY-MM-DD> \
  --max-tweets 20 \
  --output summary

Collect: tweet text, date, likes, retweets, URL.

Phase 4: Analyze & Synthesize

After collecting raw data, synthesize across all channels:

For each competitor, identify:

  • New blog posts — titles, dates, topics
  • Top LinkedIn post — by engagement (reactions + comments), topic, key message
  • Top tweet — by likes, topic
  • Recurring themes — what topics did they post about most this period?
  • Content format patterns — are they doing listicles, opinion pieces, case studies?

Cross-competitor analysis:

  • Shared trending topics — what are multiple competitors writing about?
  • Coverage gaps — topics they're covering that you're not
  • Topics you own — where you're publishing and they're not
  • Engagement benchmarks — average likes/reactions across competitors (context for your own performance)

Phase 5: Output Format

Produce a structured markdown digest:

# Competitor Content Digest — Week of [DATE]

## Summary
- [N] new blog posts tracked across [N] competitors
- Top trending topic: [topic]
- Biggest content gap for you: [topic]

---

## [Competitor Name]

### Blog
- [Post Title] — [Date] — [URL]
  > [One-sentence summary]

### LinkedIn (top post)
> "[Post preview...]"
— [Author], [Date] | [Reactions] reactions, [Comments] comments
[URL]

### Twitter/X (top tweet)
> "[Tweet text]"
— [@handle], [Date] | [Likes] likes
[URL]

### Themes this week: [tag1], [tag2], [tag3]

---

## Content Gap Analysis

| Topic | Competitors covering | You covering |
|-------|---------------------|--------------|
| [topic] | Clay, Apollo | ❌ No |
| [topic] | Nobody | ✅ Yes |

## Recommended Actions
1. [Specific content opportunity to act on this week]
2. [Topic to consider writing a response/alternative take on]

Save digest to clients/<client-name>/intelligence/competitor-content-[YYYY-MM-DD].md.

Scheduling

This skill is designed to run weekly (Mondays recommended). Set up a cron job:

# Every Monday at 8am
0 8 * * 1 python3 run_skill.py competitor-content-tracker --client <client-name>

Cost

ComponentCost
Blog scraping (RSS mode)Free
LinkedIn post scraping~$0.05-0.20/profile (Apify)
Twitter scraping~$0.01-0.05 per run
Total per weekly run~$0.10-0.50 depending on scope

Tools Required

  • Apify API tokenAPIFY_API_TOKEN env var
  • Upstream skills: blog-scraper, linkedin-profile-post-scraper, twitter-scraper

Trigger Phrases

  • "Run competitor content tracker for [client]"
  • "What did my competitors publish this week?"
  • "Give me a competitor content digest"
  • "What's [competitor] writing about?"

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

能力概览

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能力 3

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能力 4

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。

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