Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-feed-monitor, linkedin-profile-post-scraper, and twitter-mention-tracker. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.
npx gooseworks install --claude # Then in your agent: /gooseworks <prompt> --skill 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.
https://clay.com/blog)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"
}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 summaryCollect: post title, publish date, URL, excerpt.
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 summaryCollect: post text preview, date, reactions, comments, post URL.
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 summaryCollect: tweet text, date, likes, retweets, URL.
After collecting raw data, synthesize across all channels:
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.
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>| 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 |
APIFY_API_TOKEN to BYO Apifyblog-feed-monitor, linkedin-profile-post-scraper, twitter-mention-trackerDiagnose Meta Ads campaign performance using Meta's actual system mechanics — Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue — and produce structured, testable recommendations that avoid judging segments by average CPA instead of marginal efficiency.
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