Monitor competitor content across blogs, LinkedIn

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

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

Output

6. 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-feed-monitor for each competitor blog URL:

python3 skills/capabilities/blog-feed-monitor/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/capabilities/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-mention-tracker for each handle:

python3 skills/capabilities/twitter-mention-tracker/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 access — via Gooseworks proxy by default (no key needed); set APIFY_API_TOKEN to BYO Apify
• Upstream skills: blog-feed-monitor, linkedin-profile-post-scraper, twitter-mention-tracker

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?"