ad-angle-miner

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Mine the highest-converting ad angles from customer reviews, Reddit complaints, support tickets, and competitor ads. Extracts actual pain language, competitor weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank with proof quotes and recommended ad formats per angle.

Data & Documents 1,063 stars 194 forks Updated 4 days ago MIT

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Skill Content

# 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 - **Web search access** — your AI agent must support `web_search` or equivalent for Twitter/X and competitor ad lookups ## Phase 0: Intake 1. **Your product** — Name + what it does in one sentence 2. **Competitors** — 2-5 competitor names (for review mining) 3. **ICP** — Who are you targeting? (role, company stage, pain) 4. **Data sources to mine** (pick all that apply): - G2/Capterra/Trustpilot reviews (yours + competitors) - Reddit threads in relevant subreddits - Twitter/X complaints or praise - Support tickets or NPS comments (paste or file) - Competitor ads (Meta + Google) 5. **Any angles you've already tested?** — So we can skip those ## ...

Details

Author
gooseworks-ai
Repository
gooseworks-ai/goose-skills
Created
4 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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