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-signal-scannergit 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-signal-scanner)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/newsletter-signal-scanner"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/newsletter-signal-scanner/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-signal-scanner"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/newsletter-signal-scanner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 86 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00069 | $0.01500 |
| Opus 5 | $0.00034 | $0.00750 |
| Sonnet 5 | $0.00014 | $0.00300 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
newsletter-signal-scanner 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newsletter Signal Scanner
Turn your newsletter subscriptions into a structured intelligence feed. Monitors an AgentMail inbox for incoming newsletters, extracts signal-relevant content by keyword campaign, and delivers a weekly digest of what matters — competitor mentions, ICP pain language, market shifts, and emerging topics.
When to Use
- "Monitor industry newsletters for competitor mentions"
- "Alert me when newsletters mention [topic] or [company]"
- "What are newsletters writing about this week in our space?"
- "Set up newsletter monitoring for [client]"
Phase 0: Intake
Newsletters to Monitor
- Which newsletters should be subscribed to and monitored? (List names or URLs)
- If unknown, ask: "What 3-5 newsletters does your ICP read?" — then use
sponsored-newsletter-finderto discover others.
- If unknown, ask: "What 3-5 newsletters does your ICP read?" — then use
- Which AgentMail inbox should receive them? (Or should we create a new one?)
Keyword Campaigns
- Competitor names to track (e.g., "Clay", "Apollo", "Outreach")
- ICP pain-language terms to track (e.g., "outbound struggling", "pipeline dried up", "SDR ramp")
- Market shift terms (e.g., "AI SDR", "agent-led growth", "GTM engineer")
- Your brand name (to catch mentions)
Output
- Digest delivery: Slack channel, email, or markdown file? (default: markdown file)
- Frequency: daily or weekly? (default: weekly)
Save campaign config to the current working directory as newsletter-signals.json (or user-specified path).
{
"inbox_id": "<agentmail_inbox_id>",
"keyword_campaigns": {
"competitors": ["Clay", "Apollo", "Outreach", "Salesloft"],
"pain_language": ["pipeline is down", "outbound isn't working", "SDR ramp"],
"market_shifts": ["AI SDR", "GTM engineer", "agent-led"],
"brand_mentions": ["YourCompany", "yourcompany.com"]
},
"newsletters": [
{"name": "Exit Five", "from_domain": "exitfive.com"},
{"name": "The GTM Newsletter", "from_domain": "gtmnewsletter.com"}
],
"output": {
"format": "markdown",
"path": "newsletter-signals-[DATE].md"
}
}
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 · 203 lines · 69 tokens per session scan A 57dec2033510
newsletter-signal-scanner is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 69 tokens to every session and 1,500 once invoked, about $0.0003 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.
Other skills, from other repositories
Observability Checklist
Reviews a service or codebase against a full observability checklist — logs, metrics, traces, and alerting gaps.
Error Log Summarizer
Parses raw error logs and produces a concise, prioritized summary of unique issues with root cause hints.
excalidraw-ai
Create professional Excalidraw diagrams by generating JSON directly. This skill provides the Excalidraw JSON schema reference and professional icon libraries for AI agents to autonomously create diagrams without templates.
error-handling
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError…
linting
Python linting with Ruff - an extremely fast linter written in Rust. Use when: (1) Standardizing code quality, (2) Fixing style warnings, (3) Enforcing rules in CI, (4) Replacing flake8/isort/pyupgrade/autoflake, (5) Configuring lint rules and suppressions.
logfire
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.