Goose-skills competitor-monitoring-system

install
source · Clone the upstream repo
git clone https://github.com/gooseworks-ai/goose-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/gooseworks-ai/goose-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/playbooks/competitor-monitoring-system" ~/.claude/skills/gooseworks-ai-goose-skills-competitor-monitoring-system && rm -rf "$T"
manifest: skills/playbooks/competitor-monitoring-system/SKILL.md
source content

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 MonitorFrequencySkillWhat to Look For
Blog/content outputWeeklyblog-feed-monitorNew posts, topic shifts, SEO attacks
Social media postsWeeklylinkedin-profile-post-scraper + twitter-mention-trackerMessaging changes, product announcements, engagement patterns
Reddit/HN mentionsWeeklyreddit-post-finder + hacker-news-scraperUser sentiment, complaints, praise, feature requests
Ad creative changesBi-weeklygoogle-ad-scraper + web_search (Meta Ad Library)New campaigns, messaging shifts, spend changes
Review sentimentMonthlyreview-site-scraperNew 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