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 luma-event-attendeesgit 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/luma-event-attendees)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/luma-event-attendees"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/luma-event-attendees/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/luma-event-attendees"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/luma-event-attendees.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 Privilege Escalation · line 44 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00048 | $0.01747 |
| Opus 5 | $0.00024 | $0.00873 |
| Sonnet 5 | $0.00010 | $0.00349 |
| Haiku 4.5 | $0.00005 | $0.00175 |
Grade A, and why
luma-event-attendees 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
luma-event-attendees
Find and extract speakers, hosts, and registered guest profiles from Luma events for outreach prospecting.
Two Modes
1. Direct Scrape (free)
Scrapes Luma event pages directly. Gets event metadata + hosts. Guest profiles only if publicly embedded in the page.
python3 scripts/scrape_event.py https://lu.ma/abc123
2. Apify Search (paid, recommended for guest lists)
Uses the lexis-solutions/lu-ma-scraper Apify actor to search Luma and return full event data including featured guest profiles (name, bio, LinkedIn, Twitter, Instagram, website).
python3 scripts/scrape_event.py --search "AI San Francisco"
Cost: $29/month flat subscription on Apify. Rent: https://console.apify.com/actors/r5gMxLV2rOF3J1fxu
Setup
1. Apify API Token (required for --search mode)
- Create account: https://apify.com/
- Get API token: https://console.apify.com/account/integrations
- Rent the Luma scraper: https://console.apify.com/actors/r5gMxLV2rOF3J1fxu ($29/mo, 24h free trial)
- Set token:
export APIFY_API_TOKEN="apify_api_YOUR_TOKEN_HERE"
# Or create .env file in skill directory
2. Install Dependencies
pip3 install requests
Usage
Direct Scrape (free, hosts only)
# Single event
python3 scripts/scrape_event.py https://lu.ma/pwciozw0
# Multiple events
python3 scripts/scrape_event.py https://lu.ma/abc https://lu.ma/def
# Export to CSV
python3 scripts/scrape_event.py https://lu.ma/abc --output hosts.csv
Apify Search (guest profiles)
# Search for AI events in SF
python3 scripts/scrape_event.py --search "AI San Francisco"
# Just list events (don't extract people)
python3 scripts/scrape_event.py --search "SaaS NYC" --events-only
# Export all guests to CSV
python3 scripts/scrape_event.py --search "AI San Francisco" --output guests.csv
# Export as JSON
python3 scripts/scrape_event.py --search "AI SF" --output guests.json --json
Caching
What ships with it
4 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 · 237 lines · 48 tokens per session scan A 40f9f1591d45
luma-event-attendees is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 48 tokens to every session and 1,747 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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