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/get-qualified-leads-from-lumaWrote 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/get-qualified-leads-from-luma)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/get-qualified-leads-from-luma"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/get-qualified-leads-from-luma/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/get-qualified-leads-from-luma"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/get-qualified-leads-from-luma.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 216 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.
- medium Data Exfiltration · line 228 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00108 | $0.02368 |
| Opus 5 | $0.00054 | $0.01184 |
| Sonnet 5 | $0.00022 | $0.00474 |
| Haiku 4.5 | $0.00011 | $0.00237 |
Grade B, and why
get-qualified-leads-from-luma scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl -X POST -H 'Content-Type: application/json' -d '{"text":"test"}' YOUR_WEBHOOK_URL Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Use Python with `urllib.request` to POST to the webhook: How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Get Qualified Leads from Luma Events
Search Luma for events by topic and location, extract all attendees and hosts, qualify them against your ICP, export to a Google Sheet, and send a Slack alert with the top leads.
This is a 5-step pipeline that chains together luma-event-attendees, lead-qualification, Google Sheets output, and Slack alerting.
Step 0: Clarify Search Parameters
Before doing anything, make sure you have clear answers to these questions. If the user's prompt already covers them, skip ahead. Otherwise, ask:
- Location — Where should events be? (e.g., "San Francisco", "New York", "London")
- Topics/Keywords — What event topics? Suggest 3-5 keyword variations to maximize coverage. For example, if the user says "growth marketing", also suggest: "GTM", "demand gen", "startup growth", "growth hacking", "marketing leadership"
- Timeframe — How recent should the events be? (e.g., "past 2 weeks", "past month", "this quarter"). Default to past 30 days if the user doesn't specify. Luma search can return events from months or years ago, so always confirm a timeframe to avoid stale results.
- Qualification prompt — Does the user have an existing qualification prompt in
skills/lead-qualification/qualification-prompts/? If not, what's their ICP at a high level? (Can uselead-qualificationintake mode to build one) - Slack channel/webhook — Where should the alert go? A webhook URL or Slack channel name?
- How many top leads in the Slack alert? (default: 5)
Present these as a numbered list. The user can answer in one shot.
Step 1: Search Luma and Extract Attendees
Use the luma-event-attendees skill with multiple keyword variations to maximize coverage.
Run parallel searches
Generate 3-5 keyword variations combining the user's topic with their location. Run them all in parallel:
# Run each search variation in parallel
python3 skills/luma-event-attendees/scripts/scrape_event.py --search "AI San Francisco" --output /tmp/luma_search_1.csv
python3 skills/luma-event-attendees/scripts/scrape_event.py --search "Growth Marketing San Francisco" --output /tmp/luma_search_2.csv
python3 skills/luma-event-attendees/scripts/scrape_event.py --search "GTM San Francisco" --output /tmp/luma_search_3.csv
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 · 230 lines · 108 tokens per session scan B 5f7e485977a9
get-qualified-leads-from-luma is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 108 tokens to every session and 2,368 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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.
commit-message
Analyze git changes and generate conventional commit messages. Supports batch commits for multiple unrelated changes. Use when: (1) Creating git commits, (2) Reviewing staged changes, (3) Splitting large changesets into logical commits.
python-backend
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring…