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 landing-page-intelgit 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/landing-page-intel)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/landing-page-intel"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/landing-page-intel/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/landing-page-intel"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/landing-page-intel.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 | $0.00048 | $0.00753 |
| Opus 5 | $0.00024 | $0.00377 |
| Sonnet 5 | $0.00010 | $0.00151 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
landing-page-intel 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Page Intel
Extract GTM-relevant intelligence from any company's landing page by scraping its HTML source.
Quick Start
Only dependency is pip install requests. No API key needed.
# Basic scan of a single URL
python3 skills/landing-page-intel/scripts/scrape_landing_page.py \
--url "https://example.com"
# Scan multiple pages of the same site
python3 skills/landing-page-intel/scripts/scrape_landing_page.py \
--url "https://example.com" --pages "/,/pricing,/about"
# Output as summary table instead of JSON
python3 skills/landing-page-intel/scripts/scrape_landing_page.py \
--url "https://example.com" --output summary
# Save full report to file
python3 skills/landing-page-intel/scripts/scrape_landing_page.py \
--url "https://example.com" --output json > report.json
What It Extracts
| Category | Details |
|---|---|
| Tech Stack | Analytics (GA4, Mixpanel, Amplitude, PostHog, Heap), marketing automation (HubSpot, Marketo, Pardot), chat widgets (Intercom, Drift, Crisp, Zendesk), A/B testing (Optimizely, VWO, LaunchDarkly), session recording (Hotjar, FullStory, LogRocket), CDPs (Segment, Clearbit, 6sense) |
| Ad Pixels | Meta Pixel, Google Ads, LinkedIn Insight Tag, TikTok pixel, Twitter pixel |
| Customer Logos | Image URLs from "trusted by" / logo carousel sections, grouped by directory |
| SEO Metadata | Title, meta description, Open Graph tags, Twitter Cards, canonical URL, structured data (JSON-LD), hreflang tags |
| CTAs & Sales Motion | All CTA button text and links — reveals PLG vs sales-led motion |
| Social Proof | Testimonials, customer counts, case study links, badge images |
| Integrations | Links to integration/partner pages, embedded third-party widgets |
| Hidden Elements | Content in display:none, hidden, or HTML comments that may reveal upcoming features |
| Infrastructure | CMS platform (Webflow, WordPress, Next.js, etc.), detected from HTML signatures |
CLI Reference
What ships with it
2 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 · 68 lines · 48 tokens per session scan A d4851a4cee7c
landing-page-intel 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 753 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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