AI listening

Produce one report: the names of new AI models, products and repositories seen in the last 24 hours, where each was seen, and how strong the signal is. This is collection, not analysis. For search demand behind a name, hand the names to the seo-new-keywords-research skill.

Before you run anything

1. Find the radar. Use $NEW_TERM_RADAR_DIR if set, else ~/new-term-radar. If neither exists, clone it: git clone https://github.com/zhuyansen/new-term-radar ~/new-term-radar. Run every command from that directory. Python 3.10 or newer; no packages to install.
2. Check an AIsa key is available: AISA_API_KEY, or the AIsa CLI logged in (ls ~/.aisa/key). Without one only the free signals run (Hugging Face, GitHub, sitemaps), no X posts are read, and nothing judges which terms are names. Say so in the report. A key comes from https://go.aisa.one/yansen-zhu.
3. If there is no config.json, copy config.example.json to config.json. Tell the user they are on the example list of X accounts and sitemaps and can edit it.

Cost and time, say this first

A run reads every X account in config.json: about 0.003 USD and 10 seconds each, so the example list of 35 accounts is about 0.11 USD and 6 minutes. Tell the user the estimate before you start, and do not run it twice in a row: the second run reports only what changed since the first.

Run

python -m radar daily --skip usecases,review,newsites --out listening.md