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 newsletter-monitorgit 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/newsletter-monitor)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/newsletter-monitor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/newsletter-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/newsletter-monitor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/newsletter-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 | $0.00049 | $0.00603 |
| Opus 5 | $0.00024 | $0.00302 |
| Sonnet 5 | $0.00010 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
newsletter-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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newsletter Monitor
Scan an AgentMail inbox for newsletter signals using configurable keyword campaigns. Designed for monitoring accounting industry newsletters for buying signals like acquisitions, Sage Intacct migrations, staffing challenges, and technology adoption.
Quick Start
# Set your API key
export AGENTMAIL_API_KEY="your_key_here"
# Scan inbox with all campaigns (summary view)
python3 skills/newsletter-monitor/scripts/scan_newsletters.py --output summary
# Scan specific campaign, last 7 days
python3 skills/newsletter-monitor/scripts/scan_newsletters.py --campaign acquisitions --days 7 --output summary
# JSON output for downstream processing
python3 skills/newsletter-monitor/scripts/scan_newsletters.py --output json --limit 50
Dependencies
pip3 install agentmail python-dotenv
Configuration
Keyword campaigns are defined in config/campaigns.json. Each campaign has a description and a list of keywords for case-insensitive substring matching.
Built-in campaigns:
- acquisitions - CPA firm M&A activity
- sage_intacct - Sage Intacct migration and implementation signals
- staffing - Accounting talent and staffing challenges
- technology - Accounting technology adoption
CLI Options
| Flag | Description | Default |
|---|---|---|
--campaign NAME |
Run only a specific campaign | All campaigns |
--days N |
Only scan emails from last N days | No limit |
--keywords "a,b,c" |
Custom keywords (overrides campaigns) | Use campaigns.json |
--output json|summary |
Output format | json |
--inbox ADDRESS |
Override inbox address | AGENTMAIL_INBOX env or [email protected] |
--limit N |
Max messages to fetch | 100 |
Output
JSON mode (default)
Returns an array of matched messages with:
message_id,from,subject,datematched_campaigns- which campaigns triggeredmatched_keywords- specific keywords foundcontext_snippets- 200-char window around each matchcompanies_mentioned- capitalized multi-word phrases near matches
What ships with it
3 files 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 · 77 lines · 49 tokens per session scan A 0f3f234c0033
newsletter-monitor is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 49 tokens to every session and 603 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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