Crisis-Statement Reaction Gauge
Measure whether your crisis statement actually calmed the room or poured gas on it
https://apidirect.io/mcp?token=YOUR_API_KEY
Did our apology tweet {tweet_id} calm people down or make it worse? Break down the reaction and flag the loudest critics
crisis-statement-reaction-gauge.
Any agent can also call get_skill(skill_id="crisis-statement-reaction-gauge") to pull these steps on demand.
What it does
Replies and quote-tweets carry the real verdict on a public apology. By scoring the sentiment of comments and quotes against the statement's own reach, you learn within hours whether the audience is cooling off or escalating.
Who it's for: Comms leads and PR teams managing a live incident
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
tweet_id |
Yes | Numeric ID of the apology or crisis-statement tweet to evaluate | 1789012345678901234 |
How your agent runs it
-
1
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.
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2
twitter_tweet_comments(tweet_id={tweet_id}, get_sentiment=true, pages=5)Compute the positive/negative/neutral split and flag every reply whose dominant_emotion is anger or disgust.
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3
twitter_tweet_quotes(tweet_id={tweet_id}, get_sentiment=true, pages=5)Quotes signal stronger conviction, so surface high-reach negative quote-tweets as the loudest detractors.
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4
twitter_tweet_retweets(tweet_id={tweet_id}, pages=3)List who is amplifying the statement to judge whether supporters or critics are spreading it (no sentiment available here).
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the crisis-statement-reaction-gauge prompt or get_skill tool), with your inputs filled in.
SKILL: Crisis-Statement Reaction Gauge
Replies and quote-tweets carry the real verdict on a public apology. By scoring the sentiment of comments and quotes against the statement's own reach, you learn within hours whether the audience is cooling off or escalating.
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>
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 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 high-reach negative quote-tweets as the loudest detractors.
4. Tool `twitter_tweet_retweets` — twitter_tweet_retweets(tweet_id=<tweet_id>, pages=3)
List who is amplifying the statement to judge whether supporters or critics are spreading it (no sentiment available here).
DELIVER: A calmed-vs-inflamed verdict with the 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.