aihot

Overview of aihot skill

aihot is a web-research skill for getting up-to-date AI news, daily picks, model launches, product launches, and trend summaries from aihot.virxact.com without opening a browser. Use the aihot skill when the user asks what happened today or recently in the AI world, especially in Chinese, or when the request is vague but clearly about LLMs, OpenAI, Anthropic, Google, products, papers, or AI industry movement.

What users usually want is not “more AI text,” but a fast, trustworthy answer to “what matters now?” The main advantage of aihot is that it turns a live public feed into a concise markdown briefing, so you are not relying on stale training data or a generic prompt. It is especially useful for daily briefing workflows, editor-style summaries, and “AI HOT for Web Research” tasks where recency matters more than deep explanation.

The key decision point: if the user wants current AI information, aihot is a better first move than answering from memory. It is less useful for historical research, technical debugging, or non-AI news.

aihot skill fit and boundaries

aihot fits users who need current AI headlines, selected items, or a quick daily digest. It is a good match for “AI news today,” “AI HOT today,” “what did OpenAI ship,” “what’s new in the last week,” and broad queries like “AI 圈有什么新东西.”

It is not a general web search skill and not a substitute for source-by-source verification. If the user needs citations, primary documents, or nuanced analysis across many outlets, treat aihot as a discovery layer, then verify with the original sources.

What makes aihot different

The aihot skill is designed around live retrieval, not static prompt interpretation. That means the result depends on current API content and the selected mode you choose. For installation decisions, the important point is that it already encodes a working news-retrieval workflow, so you do not need to invent one from scratch.

When not to use it

Do not reach for aihot if the user wants code help, product recommendations unrelated to AI news, or a retrospective analysis that does not depend on current events. If the task is “explain Transformers,” “compare vector databases,” or “summarize last year’s AI policy,” aihot is the wrong tool.

How to Use aihot skill

Install aihot

Install the skill with:

npx skills add KKKKhazix/khazix-skills --skill aihot

For setup review, start with SKILL.md. If your environment supports it, also inspect README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ directories. This repo is compact, so the main value is understanding the retrieval rules before you rely on the skill in production.

Start from the right prompt shape

Best results come from prompts that specify the time window, the focus, and the output style. For example:

• “Use aihot to summarize today’s AI news in Chinese, with 5 bullets and a short takeaway.”
• “Use aihot for Web Research to find the most relevant AI product launches from this week.”
• “Use aihot to check recent OpenAI and Anthropic updates, then compare the impact.”

If the user is vague, convert the request into a live-news intent before invoking the skill. “What’s happening in AI?” is enough to trigger aihot; you do not need a perfect query.

Read the API rules first

The most important operational detail in aihot is the API requirement: /api/public/* requests must include a browser-style User-Agent, or curl may return 403 Forbidden. Treat this as a hard install/use constraint, not a minor implementation note.

Practical workflow:

1. Set a UA string once.
2. Use that UA on every public API call.
3. Prefer the skill’s default retrieval path rather than improvising endpoints.

A useful mental model is: aihot is easiest when you let it fetch and summarize, and hardest when you try to use it like a generic scraper.

Use the default reading path

Before changing anything, read the skill’s workflow section and follow its recommended route. In practice, the first files to inspect are SKILL.md and any linked supporting docs if present. Then test the live retrieval flow with one focused query, such as “today’s selected AI items” or “recent AI model launches,” before expanding to broader research.

For better output quality, provide:

• a time range: today, yesterday, this week
• a scope: models, products, papers, funding, regulation
• a region or language: global, Chinese, English
• a format: bullets, timeline, executive summary

aihot skill FAQ

Is aihot only for Chinese queries?

No. The aihot skill is optimized for Chinese-facing AI news queries, but the underlying topic scope includes English-language events too. A good rule is: if the user would reasonably expect a live AI briefing, aihot is relevant.

Do I need an API key or MCP server?

No. The skill is designed to pull from a public REST API and return a markdown briefing. That lowers adoption friction and makes aihot easier to use in standard agent setups.

How is aihot different from a normal prompt?

A normal prompt can paraphrase what the model already knows. aihot is meant to fetch current content first, then summarize it. That makes it a better choice when freshness, selection, and “what’s happening now” are the real requirements.

When should I avoid aihot?

Avoid it when the user wants deep technical explanations, long-form competitive analysis, or source-grounded reporting with multiple primary citations. aihot is strongest as a live discovery and briefing tool, not as an end-to-end research system.

How to Improve aihot skill

Give better retrieval targets

The biggest quality gain comes from narrowing the ask. “AI news” is usable, but “recent AI product launches in the last 3 days” or “today’s selected items about foundation models” produces tighter results. The aihot skill works best when the search intent is specific enough to map cleanly to the feed.

Prefer selected items when speed matters

If the user wants a fast briefing, start with the curated or selected path rather than trying to exhaustively enumerate everything. That reduces noise and usually improves decision quality. For aihot for Web Research, selected items are often the fastest route to “what matters most.”

Watch for the two common failure modes

The first failure mode is undertriggering: treating a live AI news request as a generic chat answer. The second is overbroad prompting: asking for “everything about AI” and expecting a sharp summary. Both reduce usefulness. Fix them by specifying a time window and a topic cluster.

Iterate with a second pass

After the first output, ask for one of three refinements: “filter to product launches,” “expand on the most important item,” or “convert this into a weekly briefing.” That is usually more effective than rerunning the same prompt. If you need a better aihot guide for your team, standardize the input format: topic, date range, and preferred output length.