Competitor monitoring system

Skill gooseworks-ai/goose-skills/skills/competitive-intel/playbooks/competitor-monitoring-system

Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping

Install

npx -y skills add gooseworks-ai/goose-skills --skill competitor-monitoring-system

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What its author says it does

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Set up and run ongoing competitive intelligence monitoring for a client. Tracks competitor content, ads, reviews, social, and product moves.

SKILL.md

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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:

Run the relevant scrapers for the cycle type
Compare new data against the baseline/previous cycle
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