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Set up and run ongoing competitive intelligence monitoring for a client. Tracks competitor content, ads, reviews, social, and product moves.
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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npx skills add https://github.com/gooseworks-ai/goose-skills --skill competitor-monitoring-systemコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Scrape competitor ads from Meta, TikTok, Google, and LinkedIn ad libraries, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.
Turn public social videos, transcripts, posts, creator research, or the user's own source material into platform-specific LinkedIn posts, X posts and threads, short-form scripts, newsletters, blog outlines, carousel briefs, and content calendars. Use when the user wants to reuse a strong idea without copying the source.
Multi-provider waterfall lead enrichment. Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback. Cost-efficient (~$0.04-$0.10/lead) with confidence scoring and full error visibility.
SOC 職業分類に基づく
SKILL.md を表示中
| type | playbook |
| name | competitor-monitoring-system |
| description | Set up and run ongoing competitive intelligence monitoring for a client. Tracks competitor content, ads, reviews, social, and product moves. |
Set up ongoing competitive intelligence for a client. Monitor competitor content, ads, reviews, social presence, and product moves. Produce regular intelligence reports.
Create a competitor tracking file: clients/<client-name>/intelligence/competitor-watchlist.md
For each competitor, document:
Run the full competitor-intel composite for each competitor to establish a baseline:
Skill: competitor-intel (chains reddit + twitter + linkedin + blog + review scrapers)
Plus:
web_search against Meta Ad Library (facebook.com/ads/library) for Meta ad researchOutput: clients/<client-name>/intelligence/competitor-baseline.md
| 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 |
Each monitoring cycle:
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