How do I install the AI Products skill?

Run npx skillmds@latest add marsz42/ai-products in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI Products safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with AI Products?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Products free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI Products?

marsz42 (@marsz42) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Products by marsz42 — Agent Skill

Name: AI Products
Author: marsz42

File contents

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=show_hn&numericFilters=created_at_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_资源/产品发布/YYYY-MM/YYYY-MM-DD-摘要.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. Sections:
- 精选推荐 (3-5) with content angles
- LLM与AI模型
- 开发者工具
- 生产力与自动化
- 开源亮点
Save files:
- 50_资源/产品发布/YYYY-MM/YYYY-MM-DD-摘要.md
- 50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_ProductHunt-Raw.md
- 50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_HackerNews-Raw.md
- 50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_GitHub-Raw.md

Output Format

Manual invocation: Full digest with all sections.

From /start-my-day: Condensed list:

**产品发布机会 (5):**
- [产品名] - [内容角度] - [关键指标]
...
完整摘要: [[YYYY-MM-DD-摘要]]

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 "今日无新AI产品"

Content Angle Logic

High engagement + tutorial-friendly: "教程机会"
Novel + early stage: "抢先报道优势"
Open source + complex: "深度分析"
SaaS + practical: "工具评测"
Similar to existing: "对比 vs [竞品]"

---name: ai-productsdescription: Curate AI product launches from Product Hunt, Hacker News, GitHub, and Techmeme. Use when user invokes /ai-products or when /start-my-day needs product launches.---# AI Product DiscoveryFetch, 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=show_hn&numericFilters=created_at_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 |## Workflow1. **Check cache**: Look for `50_资源/产品发布/YYYY-MM/YYYY-MM-DD-摘要.md`. If exists with today's date, return cached.2. **Fetch sources**: Use WebFetch on each. Extract product name, URL, description, and engagement metrics (votes/points/stars).3. **Filter**: Keep only AI-related products (keywords: AI, ML, LLM, GPT, Claude, automation, agent, model).4. **Deduplicate**: Same product across sources = merge. Keep best description, combine metrics, track all sources.5. **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 novelty6. **Generate digest**: See [TEMPLATE.md](TEMPLATE.md). Sections: - 精选推荐 (3-5) with content angles - LLM与AI模型 - 开发者工具 - 生产力与自动化 - 开源亮点7. **Save files**: - `50_资源/产品发布/YYYY-MM/YYYY-MM-DD-摘要.md` - `50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_ProductHunt-Raw.md` - `50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_HackerNews-Raw.md` - `50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_GitHub-Raw.md`## Output Format**Manual invocation**: Full digest with all sections.**From /start-my-day**: Condensed list:```**产品发布机会 (5):**- [产品名] - [内容角度] - [关键指标]...完整摘要: [[YYYY-MM-DD-摘要]]```## 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 "今日无新AI产品"## Content Angle Logic- High engagement + tutorial-friendly: "教程机会"- Novel + early stage: "抢先报道优势"- Open source + complex: "深度分析"- SaaS + practical: "工具评测"- Similar to existing: "对比 vs [竞品]"

Frequently asked questions

AI Products