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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/gooseworks-ai/goose-skillsnpx agentmods add skills/gooseworks-ai/goose-skills/kol-content-monitorWrote 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/kol-content-monitor)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/kol-content-monitor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/kol-content-monitor/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/kol-content-monitor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/kol-content-monitor.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 | $0.00092 | $0.01772 |
| Opus 5 | $0.00046 | $0.00886 |
| Sonnet 5 | $0.00018 | $0.00354 |
| Haiku 4.5 | $0.00009 | $0.00177 |
Grade A, and why
kol-content-monitor 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KOL Content Monitor
Track what Key Opinion Leaders in your space are writing about. Surface trending narratives early — before they peak — so your team can join the conversation at the right time with relevant content.
Core principle: For seed-stage teams, the fastest path to content distribution is riding a wave that's already breaking, not creating one from scratch.
When to Use
- "What are the top voices in [our space] posting about?"
- "What topics are trending on LinkedIn in [industry]?"
- "I want to know what content is resonating before I write anything"
- "Track [list of founders/experts] and tell me what they're saying"
- "Find trending narratives I can contribute to"
Phase 0: Intake
KOL List
- Names and LinkedIn URLs of KOLs to track (if known)
- If unknown: use
kol-discoveryskill first to build the list
- If unknown: use
- Twitter/X handles for the same KOLs (optional but recommended for full picture)
- Any specific topics/keywords you care about? (for filtering noisy feeds)
Scope
- How far back? (default: 7 days for weekly monitor, 30 days for first run)
- Minimum engagement threshold to include a post? (default: 20 reactions/likes)
Save config to the current working directory as kol-monitor.json (or user-specified path).
{
"kols": [
{
"name": "Lenny Rachitsky",
"linkedin": "https://www.linkedin.com/in/lennyrachitsky/",
"twitter": "@lennysan"
},
{
"name": "Kyle Poyar",
"linkedin": "https://www.linkedin.com/in/kylepoyar/",
"twitter": "@kylepoyar"
}
],
"days_back": 7,
"min_reactions": 20,
"keywords": ["GTM", "growth", "AI", "outbound", "founder"],
"output_path": "kol-monitor-[DATE].md"
}
Phase 1: Scrape LinkedIn Posts
Run linkedin-profile-post-scraper for all KOL LinkedIn profiles:
python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
--profiles "<url1>,<url2>,<url3>" \
--days <days_back> \
--max-posts 20 \
--output json
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 · 216 lines · 92 tokens per session scan A bac035324695
kol-content-monitor is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 92 tokens to every session and 1,772 once invoked, about $0.0005 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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