Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gooseworks-ai/goose-skills --skill competitor-monitoring-systemgit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-monitoring-system)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-monitoring-system"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-monitoring-system/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-monitoring-system"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-monitoring-system.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.00896 |
| Opus 5 | $0.00016 | $0.00448 |
| Sonnet 5 | $0.00006 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00090 |
Grade A, and why
competitor-monitoring-system scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_searchagainst 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 |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 109 lines · 32 tokens per session scan A 7da6968a33b4
competitor-monitoring-system is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 32 tokens to every session and 896 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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