Geo-Trend Divergence Radar

SKILL: Geo-Trend Divergence Radar
Trends surface in one geography first. By set-differencing two regional X trend lists and reading the driver posts, you spot a topic surging in market A that hasn't hit market B yet. Optionally confirm each divergent topic on TikTok (region-scoped) and in the lead market's regional news to separate X-only blips from genuine cross-platform breakouts — a verified early-mover window.

You are running this skill on API Direct via its MCP tools. Execute the steps below yourself by calling the named tools in order — values in <angle brackets> come from a previous step. Then deliver the result described at the end.

INPUTS:
  - region_a_woeid (required): <region_a_woeid — ASK THE USER>
      WOEID of the lead market to scan for emerging trends
  - region_b_woeid (required): <region_b_woeid — ASK THE USER>
      WOEID of the comparison market to diff against
  - platforms (optional): (optional — e.g. twitter, tiktok, news)
      Comma-separated list of platforms to run — options: twitter, tiktok, news. Omit to run all of them; name specific platforms to limit the run.
  - region_a_code (optional): (optional — e.g. US)
      Region code for the lead market (region A), used to scope the optional TikTok breakout check to the same geography as region_a_woeid. Maps to search_tiktok's region param.
  - region_a_country (optional): (optional — e.g. us)
      Country code for the lead market (region A), used to scope the optional regional-news breakout check. Maps to search_news's country param.
  - tiktok_window (optional): 7 (default — override if you like)
      Recency window for the optional TikTok check, as a publish_time value: 1, 7, 30, 90, or 180 days (0 = no time filter). Tighter windows catch the freshest surges.

PLATFORM SELECTION: some steps are gated with "Only run if {platforms} includes X". If the user supplied a `platforms` value, run only the steps whose platform is listed. If the user did NOT supply `platforms`, run ALL steps — every platform the skill supports.

STEPS:
  1. Tool `twitter_trends` — twitter_trends(woeid={region_a_woeid})
     Pull the live trend list for the lead market and capture each trend name and tweet volume.
  2. Tool `twitter_trends` — twitter_trends(woeid={region_b_woeid})
     Pull the comparison market's list and set-difference it to isolate trends present in A but absent in B.
  3. Tool `search_twitter` — search_twitter(query=<divergent trend>, sort_by=most_recent, pages=3, get_sentiment=true)
     Read the freshest posts behind each divergent trend to identify the driver and whether sentiment is positive momentum or backlash.
  4. Tool `search_twitter` — search_twitter(query=<divergent trend>, sort_by=relevance, pages=2)
     Pull the highest-engagement posts to estimate amplification potential and rank which divergent topics to act on first.
  5. Tool `search_tiktok` — search_tiktok(query=<divergent trend>, publish_time=7, sort_by=most_recent, get_sentiment=true)
     Only run if {platforms} includes tiktok: confirm the divergent topic is genuinely surging on TikTok in the lead market (region A) and not just an X-only blip; capture recent post velocity and sentiment to validate cross-platform momentum.
  6. Tool `search_news` — search_news(query=<divergent trend>, time_published=1d)
     Only run if {platforms} includes news: check whether the topic has broken into the lead market's regional news in the last 24h — a strong sign it is crossing from social into mainstream and worth front-running before the comparison market catches up.

DELIVER: A ranked shortlist of topics trending in the lead region but not yet in the comparison region, each with its driver, sentiment, an optional cross-platform confirmation (TikTok post velocity and regional-news pickup), and an early-mover recommendation.

Note: each underlying tool call is billed at its normal endpoint price; get_sentiment adds a small per-page surcharge. Page through results as needed but stop once you have enough to deliver the outcome.