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How to find the search terms behind a new AI name with New Term Radar

Turn a new name into the generic searches people make, sized and checked for page-one room.

Who it is forSite owner deciding which page to build for a newly launched AI tool.

The problemA brand name has a short life as a keyword, while the job it does is what people keep searching for.

The outcomeA keyword table with estimated searches a day, rising status and a prioritize, watch or skip decision for each term.

What you need first

  • Python environment to install the package (inferred from the PyPI badge: pip install new-term-radar)
  • AIsa API key, needed for the X, keyword table and most other signals
  • Optional GoAnyAPI key for Google Trends related queries and page-one data
  • Optional TrustMRR key for the revenue signal
  • Optional GitHub token to raise the rate limit

Steps

  1. Install the package with pip install new-term-radar. (inferred, check the README)
  2. Set your AIsa key in config.json and keep the benchmark keyword the default gpts unless you change it.
  3. Run the radar and open the keyword table it writes for the day's new names.
  4. Read the Page one column: open means a new page can compete, crowded means strong sites hold the results.
  5. Wait three days and read the second reading before committing to any prioritize row.

Prompt for your agent

In the new-term-radar repo, produce the keyword table for today's new names and return the rows with their decision and page-one status. Verify that no prioritize row has a crowded page one, since the README relabels those as watch with a down arrow.

Caveat. Search volumes are estimates, and the README notes that generic terms did not reliably rise with the names within five days, so treat early decisions as provisional.

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