Hot Topic Radar Student Edition

Problem

Content creators often get stuck at three break points: what is trending today, which trends are relevant to their industry, and how to turn a trend into usable assets quickly. Hot Topic Radar Student Edition chains these steps into a fixed workflow: collect trending topics, filter them by industry, city, and scope, then guide the user to choose a topic and produce content for different channels. It is better suited to fast content production than deep research reports.

Workflow and Limits

The skill first confirms industry, city, and hot-topic scope, asking in one pass to avoid multi-turn back-and-forth. It then uses WebFetch to visit tophub.today/ and extract the Top 10 from Weibo, Douyin, Zhihu, and Baidu hot searches. If local topics are requested, it uses WebSearch to add same-day local news. Before output, it applies:
- Cross-platform deduplication: merge the same event and label source platforms;
- Major-trend detection: flag multi-platform appearances or high rankings on one platform;
- Industry relevance: separate strong matches, borrowable angles, and unrelated items.

After the user selects a numbered topic, the skill searches for the event timeline, key data, reactions, and latest updates, then creates a 5–8 sentence fact summary and moves into social copy, short-video scripts, paid-ad scripts, or article outlines.

Note: it depends on live hot-search and search tools; missing data triggers fallback or a notice. It should not invent heat scores, add unverified facts, or force irrelevant topics into industry relevance. Without a business profile, it mainly surfaces national top trends.