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 ad-angle-minergit 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/ad-angle-miner)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/ad-angle-miner"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-angle-miner/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/ad-angle-miner"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-angle-miner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to medium
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 →
- medium Data Exfiltration · line 56 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.
- medium Data Exfiltration · line 70 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.
- medium Data Exfiltration · line 76 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.
- medium Data Exfiltration · line 104 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.
- medium Data Exfiltration · line 117 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.
- medium Data Exfiltration · line 131 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.
- medium Data Exfiltration · line 137 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 | $0.00058 | $0.02656 |
| Opus 5 | $0.00029 | $0.01328 |
| Sonnet 5 | $0.00012 | $0.00531 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
ad-angle-miner 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 13d 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Angle Miner
Dig through customer voice data — reviews, Reddit, support tickets, competitor ads — to extract the specific language, pain points, and outcome desires that make ads convert. The output is an angle bank your team can pull from for any campaign.
Core principle: The best ad angles aren't invented in a brainstorm. They're extracted from what real people are already saying. This skill finds those angles and ranks them by strength of evidence.
When to Use
- "What angles should we run in our ads?"
- "Find pain points we can use in ad copy"
- "What are people complaining about with [competitors]?"
- "Mine reviews for ad messaging"
- "I need fresh ad angles — not the same tired stuff"
Prerequisites
- Environment variable:
APIFY_API_TOKEN— required for review scraping and Reddit scraping - GooseWorks or a direct ScrapeCreators key — for structured social comments and ad-library evidence
- Web search access — for review sources and verification fallbacks
Phase 0: Intake
- Your product — Name + what it does in one sentence
- Competitors — 2-5 competitor names (for review mining)
- ICP — Who are you targeting? (role, company stage, pain)
- Data sources to mine (pick all that apply):
- G2/Capterra/Trustpilot reviews (yours + competitors)
- Reddit threads in relevant subreddits
- Twitter/X complaints or praise
- Social comments on creator, competitor, or brand posts
- Support tickets or NPS comments (paste or file)
- Competitor ads (Meta + Google)
- Any angles you've already tested? — So we can skip those
Phase 1: Source Collection
1A: Review Mining (Apify)
Use the Apify Amazon Reviews Scraper (or web_search for G2/Capterra/TrustRadius reviews).
Option 1: Amazon product reviews via Apify
Start a run of the web_wanderer/amazon-reviews-extractor actor:
POST https://api.apify.com/v2/acts/web_wanderer~amazon-reviews-extractor/runs?token=$APIFY_API_TOKEN
Content-Type: application/json
{
"products": [
"https://www.amazon.com/dp/PRODUCT_ASIN"
],
"maxReviews": 100
}
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.
- 13d ago First seen · 301 lines · 58 tokens per session scan A 181528e0cabd
ad-angle-miner is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 58 tokens to every session and 2,656 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 2026-08-30.
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