Crisis-Statement Reaction Gauge

SKILL: Crisis-Statement Reaction Gauge
Replies and quote-tweets carry the first verdict on a public apology, but the room is bigger than one thread. Score the sentiment of the statement's own comments and quotes, then optionally fold in fresh Reddit, news, and forum reaction to learn within hours whether the wider audience is cooling off or escalating — and where the loudest detractors are concentrated.

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:
  - tweet_id (required): <tweet_id — ASK THE USER>
      Numeric ID of the apology or crisis-statement tweet to evaluate
  - statement_topic (optional): (optional — e.g. Acme data breach apology)
      Brand name or short topic phrase describing the incident, used as the search query for the optional cross-platform reaction steps. Required if you enable any platform other than twitter.
  - platforms (optional): (optional — e.g. twitter, reddit, news, forums)
      Comma-separated list of platforms to run — options: twitter, reddit, news, forums. Omit to run all of them; name specific platforms to limit the run.
  - news_window (optional): 1d (default — override if you like)
      Freshness window for the news sweep, mapped to search_news time_published. Use a tight window for a live incident. Accepts 1h, 1d, 7d, 1y, or anytime.
  - country (optional): (optional — e.g. us)
      Optional ISO country code to scope the news and forum sweeps to a single market; omit for all regions.

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_tweet_details` — twitter_tweet_details(tweet_id={tweet_id}, get_sentiment=true)
     Record the statement's impressions, retweets, and likes to size the audience you are measuring against.
  2. Tool `twitter_tweet_comments` — twitter_tweet_comments(tweet_id={tweet_id}, get_sentiment=true, pages=5)
     Compute the positive/negative/neutral split of replies and flag every reply whose dominant_emotion is anger or disgust.
  3. Tool `twitter_tweet_quotes` — twitter_tweet_quotes(tweet_id={tweet_id}, get_sentiment=true, pages=5)
     Quotes signal stronger conviction, so surface the high-reach negative quote-tweets as the loudest detractors.
  4. Tool `search_reddit_comments` — search_reddit_comments(query={statement_topic}, get_sentiment=true, pages=3, sort_by=most_recent)
     Only run if {platforms} includes reddit and {statement_topic} is set: pull the freshest Reddit comments reacting to the incident, add their positive/negative split to the cross-platform rollup, and quote the most upvoted critical take.
  5. Tool `search_news` — search_news(query={statement_topic}, time_published=1d, limit=25)
     Only run if {platforms} includes news and {statement_topic} is set: scan how the press is framing the statement (no sentiment param here, so read the headline tone) — a rising share of skeptical or negative headlines means the room is still inflamed despite a calm reply thread.
  6. Tool `search_forums` — search_forums(query={statement_topic}, get_sentiment=true, time=week, page=1)
     Only run if {platforms} includes forums and {statement_topic} is set: capture niche industry/enthusiast forum discussion and fold its sentiment into the cross-platform negative-share percentage.

DELIVER: A calmed-vs-inflamed verdict that combines the tweet's own reply and quote sentiment with optional Reddit, news, and forum reaction — including the cross-platform negative-share percentage, the top high-reach detractors, and a recommended next move.

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.