Competitor Content Tracker — weekly competitive content digest — Claude Skill
Claude Code용 Claude 스킬 · 제공: Gooseworks · 실행: /competitor-content-tracker (Claude 내)·업데이트: 2026년 4월 10일
Weekly digest of what competitors are publishing on blog, LinkedIn, and X
- Tracks competitor blog, LinkedIn, and Twitter/X activity
- Surfaces top posts by engagement per channel
- Identifies trending themes across competitors
- Maps content gaps you can own
- Outputs weekly digest with action recommendations
대상
기능
Catch new content directions while they're still emerging instead of finding out months later.
Identify topics multiple competitors are missing that match your audience.
Understand what's actually working in your category before investing in new content formats.
작동 방식
Take competitor blog URLs and social handles as input
Scrape blog posts, LinkedIn posts, and X posts for the period
Identify top-performing content by engagement
Cross-analyze themes and gaps
Output a structured weekly digest
개선되는 지표
지원 도구
Competitor Content Tracker을(를) 사용해 보시겠어요?
시작 방법을 선택하세요.
이 스킬을 컴퓨터에 로컬로 설치하고 실행합니다.
컴퓨터에서 터미널을 열고 이 명령을 붙여넣으세요:
이 명령은 스킬과 모든 파일을 컴퓨터에 다운로드합니다:
모든 프로젝트에서 사용하려면 끝에 -g를 추가하세요.
Claude Code를 시작한 다음 명령을 입력하세요:
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
- List of competitor company names + blog URLs (e.g.,
https://clay.com/blog) - LinkedIn profile URLs of competitor founders/CMOs to track (optional but high-value)
- Twitter/X handles of the competitors or their founders (optional)
Scope
- How far back? (default: 7 days for weekly digest, 30 days for first run)
- Any topics/keywords you care most about? (used to surface relevant posts first)
Output
- 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
| Component | Cost |
|---|---|
| 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 token —
APIFY_API_TOKENenv 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?"