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 MarsZ42/OrbitOS --skill ai-productsgit clone --depth 1 https://github.com/MarsZ42/OrbitOSWrote 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/marsz42/orbitos/ai-products)<a href="https://agentmods.dev/skills/marsz42/orbitos/ai-products"><img src="https://agentmods.dev/badge/skills/marsz42/orbitos/ai-products/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/marsz42/orbitos/ai-products"><img src="https://agentmods.dev/badge/skills/marsz42/orbitos/ai-products.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00040 | $0.00709 |
| Opus 5 | $0.00020 | $0.00354 |
| Sonnet 5 | $0.00008 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
ai-products 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 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.
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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.md50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_ProductHunt-Raw.md50_资源/产品发布/YYYY-MM/原始数据/YYYY-MM-DD_HackerNews-Raw.md50_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"
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 · 72 lines · 40 tokens per session scan A 2d499ee281ae
ai-products is a skill published in the GitHub repository MarsZ42/OrbitOS (970 stars, last pushed 6mo ago), licensed MIT. It adds 40 tokens to every session and 709 once invoked, about $0.0002 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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