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 event-signalsgit 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/event-signals)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/event-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/event-signals/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/event-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/event-signals.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 30 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 | $0.00067 | $0.03830 |
| Opus 5 | $0.00034 | $0.01915 |
| Sonnet 5 | $0.00013 | $0.00766 |
| Haiku 4.5 | $0.00007 | $0.00383 |
Grade A, and why
event-signals 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 — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Event Signals
Extract leads from the conference and events ecosystem. Events represent public declarations of priorities — a VP speaking about "scaling our infrastructure" is a stronger signal than a Reddit comment, and a company sponsoring a conference category has committed real budget.
When to Use
- User wants to find leads from conferences, meetups, or developer events
- User wants speaker lists, sponsor lists, or attendee data from events
- User mentions KubeCon, AWS re:Invent, DevOpsDays, or any industry conference
- User wants to find people who spoke or presented about relevant topics
- User asks about hackathon participants using relevant technologies
- User wants podcast guests who discussed relevant challenges
- User describes prospects who attend industry events or speak at conferences
Prerequisites
- Python 3.9+ with
requestsand optionallypython-dotenv - Apify API token in
.env(for Meetup and Luma — optional) - ListenNotes API key in
.envasLISTENNOTES_API_KEY(for podcast search — optional, free tier gives 300 req/mo) - Working directory: the project root containing this skill
Phase 1: Collect Context
Step 1: Gather Product & ICP Information
If not already available from prior skills:
"To find the right event-based leads, I need:
- What does your product do? (one-liner)
- What industry/vertical are you in? (this determines which conferences matter)
- What technologies or keywords are in your space?
- Who is your ideal buyer? (their role, what conferences they'd attend)
- Any specific conferences or events you know about? (names, URLs)
- Are you registered on Luma or Meetup for any events? (if yes, we may be able to access attendee lists)"
Phase 2: Discover Relevant Events
This is the most agent-driven phase. The agent must actively research to find the right events.
Step 2: Discover Conferences
Use multiple approaches in parallel:
2a. Confs.tech (automated):
The tool can query confs.tech for conferences by topic. Common topics available: devops, javascript, ruby, python, golang, rust, java, php, css, data, security, ux, android, ios, general, dotnet, elixir, networking, cloud, ai.
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 · 363 lines · 67 tokens per session scan A a68af9941d00
event-signals is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 67 tokens to every session and 3,830 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.
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