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AI hotspot monitoring and trending topic discovery across multiple sources (Bing, Google, DuckDuckGo, HackerNews, Sogou, Bilibili, Weibo, Twitter).
Passed 13 of 22 test cases with the skill, and 4 of 22 without it.
and 13 more in the full report
11 fixed4 passed either way10 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
When evaluating raw search hits in the hotspot analysis framework, what boolean property name is used to distinguish authentic news from rumors or clickbait?
| Checked | Without skill | With skill |
|---|---|---|
| The evaluation property name is isReal. | Missed | Passed |
| The property flags whether the item represents factual news or clickbait rumors. | Passed | Passed |
The instructions your model reads when the skill loads.
Search and analyze trending topics across 8+ sources without any server or database. Scripts handle data collection; use your own AI capabilities for analysis.
All scripts are in scripts/. Install dependencies first:
pip install requests beautifulsoup4
Set optional env vars for Twitter (other sources need no keys):
export TWITTER_API_KEY=your_key # optional, for Twitter search
Determine what the user needs:
Run search scripts based on scope. Always combine international + Chinese sources for comprehensive coverage.
International sources (no API keys needed):
python scripts/search_web.py "AI programming" --sources bing,hackernews,duckduckgo
Chinese sources (no API keys needed):
python scripts/search_china.py "AI编程" --sources sogou,bilibili,weibo
test cases passed, vs 4 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.