SKILL.md
AI Product Discovery
Fetch, deduplicate, and rank AI product launches from multiple sources.
Sources
| Source | URL | Notes |
|---|---|---|
| Product Hunt | https://www.producthunt.com/feed |
Filter for AI-related |
| Hacker News | https://hn.algolia.com/api/v1/search?tags=showhn&numericFilters=createdat_i>TIMESTAMP |
Show HN posts, 24h window |
| GitHub Trending | https://mshibanami.github.io/GitHubTrendingRSS/daily/python.xml |
Python repos |
| Techmeme | https://techmeme.com/river |
Product announcements |
Workflow
- Check cache: Look for
50_Resources/ProductLaunches/YYYY-MM/YYYY-MM-DD-Digest.md. If exists with today's date, return cached.
- Fetch sources: Use WebFetch on each. Extract product name, URL, description, and engagement metrics (votes/points/stars).
- Filter: Keep only AI-related products (keywords: AI, ML, LLM, GPT, Claude, automation, agent, model).
- Deduplicate: Same product across sources = merge. Keep best description, combine metrics, track all sources.
- Rank by:
- AI relevance - Engagement (normalize: PH votes/500, HN points/100, GH stars/1000) - Content potential (tutorial-friendly, review-worthy, open source bonus) - Recency and novelty
- Generate digest: See [TEMPLATE.md](TEMPLATE.md). Sections:
- Top Picks (3-5) with content angles - LLM & AI Models - Developer Tools - Productivity & Automation - Open Source Highlights
- Save files:
- 50Resources/ProductLaunches/YYYY-MM/YYYY-MM-DD-Digest.md - 50Resources/ProductLaunches/YYYY-MM/Raw/YYYY-MM-DDProductHunt-Raw.md - 50Resources/ProductLaunches/YYYY-MM/Raw/YYYY-MM-DDHackerNews-Raw.md - 50Resources/ProductLaunches/YYYY-MM/Raw/YYYY-MM-DD_GitHub-Raw.md
Output Format
Manual invocation: Full digest with all sections.
From /start-my-day: Condensed list:
**Product Launch Opportunities (5):**
- [Product] - [Angle] - [Top metric]
...
Full digest: [[YYYY-MM-DD-Digest]]Error Handling
- Source down: Continue with others, note in digest
- <2 sources available: Fall back to yesterday's archive
- Empty results: Create minimal digest noting "No new AI products"
Content Angle Logic
- High engagement + tutorial-friendly: "Tutorial opportunity"
- Novel + early stage: "First-mover advantage"
- Open source + complex: "Deep dive analysis"
- SaaS + practical: "Tool review"
- Similar to existing: "Comparison vs [competitor]"