create-image-fal
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent. image_urls…
Set up and run ongoing competitive intelligence monitoring for a client. Tracks competitor content, ads, reviews, social, and product moves.
$ npx -y skills add gooseworks-ai/goose-skills --skill competitor-monitoring-system --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/competitor-monitoring-systemContext preview
The summary Claude sees to decide when to auto-load this skill.
Set up and run ongoing competitive intelligence monitoring for a client. Tracks competitor content, ads, reviews, social, and product moves.
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:
**Output**: `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:
1. Run the relevant scrapers for the cycle type 2. Compare new data against the baseline/previous cycle 3. Flag significant changes:
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`
Put your AI agent on the growth team. Research customers and competitors, analyze what is working, create the next campaign, and learn from the result.
Repo: gooseworks-ai/goose-skills
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