athina-ai/competitor-monitoring-system

Competitor Monitoring System

Set up ongoing competitive intelligence for a client. Monitor competitor content, ads, reviews, social presence, and product moves. Produce regular intelligence reports.

When to Use

• "Set up competitor monitoring for [client]"
• "Track what [competitors] are doing"
• "Monitor [competitor] content and ads"

Prerequisites

• List of competitors to track (typically 3-7)
• Client context with competitive positioning
• Competitor founder/executive LinkedIn profiles (for social monitoring)

Setup Steps

1. Define Competitor Watchlist

Create a competitor tracking file: clients/<client-name>/intelligence/competitor-watchlist.md

For each competitor, document:

• Company name and URL
• Key products/features
• Founder/exec LinkedIn profiles
• Known content channels (blog URL, YouTube, podcast)
• Review profiles (G2, Capterra URLs)
• Ad library pages (Meta, Google)

2. Initial Competitive Baseline

Run the full competitor-intel composite for each competitor to establish a baseline:

Skill: competitor-intel (chains reddit + twitter + linkedin + blog + review scrapers)

Plus:

• Skill: google-ad-scraper — Scrape their current Google ads
• Method: Use web_search against Meta Ad Library (facebook.com/ads/library) for Meta ad research
• Skill: review-site-scraper — Pull latest G2/Capterra/Trustpilot reviews

Output: clients/<client-name>/intelligence/competitor-baseline.md

3. Configure Monitoring Cadence

| What to Monitor | Frequency | Skill | What to Look For | |----------------|-----------|-------|-----------------| | Blog/content output | Weekly | blog-feed-monitor | New posts, topic shifts, SEO attacks | | Social media posts | Weekly | linkedin-profile-post-scraper + twitter-mention-tracker | Messaging changes, product announcements, engagement patterns | | Reddit/HN mentions | Weekly | reddit-post-finder + hacker-news-scraper | User sentiment, complaints, praise, feature requests | | Ad creative changes | Bi-weekly | google-ad-scraper + web_search (Meta Ad Library) | New campaigns, messaging shifts, spend changes | | Review sentiment | Monthly | review-site-scraper | New reviews, rating trends, common complaints |

4. Run Monitoring

Each monitoring cycle:

1. Run the relevant scrapers for the cycle type
2. Compare new data against the baseline/previous cycle
3. Flag significant changes:
   • New product features or pricing changes
   • New content targeting our client's keywords
   • Negative review trends (poaching opportunity)
   • New ad campaigns (messaging intelligence)
   • Founder/exec public statements about strategy

5. Produce Intelligence Report

After each cycle, produce a brief intelligence summary:

# Competitor Intelligence — [Client] — Week of [Date]

## Key Changes
- [Competitor A] published 3 new blog posts targeting "[keyword]"
- [Competitor B] launched new Meta ad campaign focused on [theme]
- [Competitor C] received 5 negative G2 reviews about [issue]

## Recommended Actions
- Publish response content for [Competitor A]'s keyword attack
- Create comparison page addressing [Competitor B]'s new messaging
- Target [Competitor C]'s unhappy customers with migration content

## Detailed Findings
[Per-competitor breakdown]

Output: clients/<client-name>/intelligence/competitor-reports/[date].md

Ongoing Cadence

• Weekly: Content + social monitoring, brief report
• Bi-weekly: Ad monitoring
• Monthly: Full review scrape + comprehensive report
• Quarterly: Re-run full competitor-intel baseline, update watchlist

Human Checkpoints

• After setup: Review competitor watchlist and monitoring plan
• After each report: Review recommended actions before executing