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 competitor-ad-intelligencegit 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/competitor-ad-intelligence)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-ad-intelligence"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-ad-intelligence/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/competitor-ad-intelligence"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-ad-intelligence.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 | $0.00081 | $0.03639 |
| Opus 5 | $0.00041 | $0.01819 |
| Sonnet 5 | $0.00016 | $0.00728 |
| Haiku 4.5 | $0.00008 | $0.00364 |
Grade A, and why
competitor-ad-intelligence scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Or use `curl` if `fetch_webpage` is unavailable. How it starts
The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Ad Intelligence
Scrape competitor ads from Meta, TikTok, Google, and LinkedIn, analyze creative patterns, reverse-engineer landing page funnels, and produce a full strategic teardown — hooks, formats, positioning bets, vulnerabilities, and counter-plays.
Core principle: A competitor's ad portfolio is evidence about its growth strategy, not access to its results. Long-running ads suggest sustained use. New ads suggest active testing. Landing pages reveal positioning bets. Use these signals to form differentiated tests without claiming conversion, spend, or causality the libraries do not expose.
When to Use
- "What ads are my competitors running?"
- "Tear down [competitor]'s ad strategy"
- "Find new creative angles for our paid campaigns"
- "Reverse-engineer [competitor]'s paid funnel"
- "What hooks are working in [our space]?"
- "Audit the ad landscape before we launch"
- "Find weaknesses in [competitor]'s ad strategy"
- "What format — video, image, carousel — is dominant in our category?"
Phase 0: Intake
Gather from the user:
- Competitor names + domains (e.g.,
apollo.io,clay.run) - Your product/domain — for comparison framing
- Channels: Meta, TikTok, Google, LinkedIn, or all relevant libraries? (default: all channels relevant to the brand and market)
- Depth level:
- Standard: Ad scrape + creative analysis + landing page analysis
- Deep: Standard + historical comparison + funnel reconstruction + counter-plays
- Product category — helps frame analysis
- Known competitor landing pages? — any URLs already spotted in their ads
Phase 1: Scrape Meta Ads
For each competitor domain, scrape ads from Meta Ad Library.
Use scrapecreators-api as the primary collection path. Resolve the advertiser first, then fetch its ads and individual ad details:
- provider: scrapecreators
method: GET
path: /v1/facebook/adLibrary/search/companies
query:
query: "[competitor_name]"
- provider: scrapecreators
method: GET
path: /v1/facebook/adLibrary/company/ads
query:
companyName: "[competitor_name]"
- provider: scrapecreators
method: GET
path: /v1/facebook/adLibrary/ad
query:
id: "[ad_id]"
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.
- 12d ago First seen · 420 lines · 81 tokens per session scan A 3820f6dd2347
competitor-ad-intelligence is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 81 tokens to every session and 3,639 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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