Competitor Content Tracker — weekly competitive content digest — Claude Skill
A Claude Skill for Claude Code by Gooseworks — run /competitor-content-tracker in Claude·Updated
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
Who this is for
What it does
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.
How it works
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
Metrics this improves
Works with
Want to use Competitor Content Tracker?
Choose how to get started.
Install and run this skill locally on your computer.
Open a terminal on your computer and paste this command:
This downloads the skill with all its files to your computer:
Add -g at the end to make it available in all your projects.
Start Claude Code, then type the command:
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?"