Connect X / Twitter Public Data to ChatGPT

Multi-Client X Presence Pulse

Using the Catchr MCP, pull X / Twitter Public Data for [List of Client Accounts] over [Period] and compare it with [Comparison Period]. For each account, report Followers Count, Tweet Count, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count. Calculate public engagement per tweet and engagement rate as (likes + replies + retweets + quotes + bookmarks) / impressions when impressions are available, flag the accounts with the sharpest deterioration against [Client Targets or Historical Baseline], and return a ranked consultant action list with one evidence-based explanation and one next step per client.

Client Competitive Content Brief

Using the Catchr MCP, compare [Client Account Name/Username] with [Competitor Account Usernames] on X over [Period]. Use Account Name, Account Username, Followers Count, Tweet Created At, Tweet Text, Tweet Language, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count. Normalize interactions by impressions and by tweet volume, identify the content themes and individual tweets where the client leads or trails, and produce a client-ready brief with three strengths, three competitive gaps, and three specific content actions for [Next Period]. Do not infer sales, sentiment, or audience demographics that are not present in the public data.

Portfolio Reputation and Content Risk Watchlist

Using the Catchr MCP, review X / Twitter Public Data for [List of Client Accounts] over [Recent Period]. Inspect Tweet Text, Tweet Created At, Tweet Possibly Sensitive, Tweet Reply Settings, Tweet Country Codes, Tweet Copyright, Account Protected, Account Country Codes, Reply Count, Quote Count, and Retweet Count. Flag newly published content restrictions, sensitive-content markers, unusual reply or quote surges versus [Baseline Period], and accounts whose public visibility has changed. Return a prioritized cross-client watchlist with the exact account and Tweet Id, the supporting fields, the potential client-facing risk, and the verification or response action the consultant should take; treat engagement spikes as signals to review, not proof of negative sentiment.

Morning Client Attention Queue

Using the Catchr MCP, scan X / Twitter Public Data for [List of Client Accounts] for [Recent Period, e.g., the last 7 days] and compare it with [Baseline Period]. For each client, summarize Tweet Count, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, Bookmark Count, and Followers Count; calculate engagement per tweet and impression-normalized engagement where possible. Flag inactivity, sudden reach or interaction declines, and exceptional posts worth amplifying, then rank the clients I should work on today with one concise reason and one action for each priority.

Monthly Organic X Client Report

Using the Catchr MCP, pull public X data for [Client Account Name/Username] for [Reporting Period] and [Previous Period]. Report Followers Count, Tweet Count, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count, then use Tweet Id, Tweet Created At, and Tweet Text to highlight the strongest and weakest posts. Calculate period-over-period changes and engagement rate as total public interactions / impressions when available, and write a concise client-ready report in plain English covering what improved, what declined, likely content explanations supported by the posts, and three priorities for [Next Period].

Evidence-Based Content Idea Backlog

Using the Catchr MCP, analyze public posts from [Client Account Username] and [Reference or Competitor Usernames] over [Lookback Period]. Use Tweet Text, Tweet Created At, Tweet Language, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count to find recurring topics, hooks, questions, and calls to action among high-performing tweets, using [Minimum Impressions or Minimum Interactions] to avoid tiny-sample winners. Build a prioritized backlog of [Number of Ideas] original X post concepts for the client, each with the supporting data pattern, suggested angle, intended interaction, and a measurable public KPI; do not reproduce another account's wording.

Product Launch Organic Resonance Review

Using the Catchr MCP, analyze public X posts from [Brand Account Username] containing [Product, Collection, or Campaign Keywords] during [Launch Period], with [Pre-Launch Period] as the baseline. Use Tweet Text, Tweet Created At, Tweet Language, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count. Rank the launch tweets by impression-normalized engagement, show how reach and interaction changed from baseline, identify which messages and formats generated the strongest public response, and recommend three concrete organic content adjustments for the remainder of the launch. Do not claim revenue or conversion impact from public engagement data alone.

Competitor Social Merchandising Benchmark

Using the Catchr MCP, benchmark [Brand Account Username] against [Competitor Account Usernames] on X over [Period]. Filter or classify Tweet Text using [Product Category, Offer, Launch, or Promotion Keywords], then compare Tweet Count, Followers Count, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count by account. Calculate interactions per tweet and engagement rate from impressions where available, surface the product stories, offers, and posting patterns competitors use most effectively, and deliver five evidence-based merchandising opportunities the brand can test without copying competitors verbatim.

Best-Time and Message Playbook

Using the Catchr MCP, pull public X posts for [Brand Account Username] over [Lookback Period, e.g., 90 days]. Break performance down by Day of the week, Hour of the day, Tweet Language, and Tweet Source, and use Tweet Text, Impression Count, Like Count, Reply Count, Retweet Count, Quote Count, and Bookmark Count to calculate median impressions and median impression-normalized engagement for each segment. Control for low-volume groups with [Minimum Tweet Count], identify repeatable timing and message patterns rather than one-off viral posts, and create a four-week publishing playbook with recommended slots, themes, test hypotheses, and success metrics.

Public X Data Integrity Audit

Using the Catchr MCP, audit X / Twitter Public Data for [Account Name/ID or All Connected Accounts] over [Period]. Check Account Id, Account Username, Tweet Id, Tweet Author Id, Tweet Conversation Id, Tweet Created At, Extracted Date, Tweet Text, Tweet Language, and all public engagement counts for completeness and consistency. Flag duplicate Tweet Id values, tweets whose author does not match the requested account, missing identifiers or timestamps, future creation dates, extraction dates earlier than tweet dates, negative counts, and records where Bookmark Count, Like Count, Reply Count, Retweet Count, Quote Count, or Impression Count is unexpectedly null. Return an exception table with account, Tweet Id, failed rule, observed value, severity, and recommended pipeline check.

Tweet Engagement Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of X / Twitter Public Data for [Account Name/ID or Account List], grouped by Date and Tweet Id. For each tweet, calculate total interactions as Like Count + Reply Count + Retweet Count + Quote Count + Bookmark Count and engagement rate as total interactions / Impression Count when impressions are greater than zero. Compare each metric with a trailing [Baseline Window, e.g., 30-day] median and median absolute deviation, flag outliers beyond [Threshold], and classify each as likely genuine content performance, low-volume distortion, extraction issue, or unresolved. Include the exact fields and values supporting every classification.

Account Snapshot and Freshness Reconciliation

Using the Catchr MCP, reconcile public account and tweet snapshots for [Account Name/ID or All Connected Accounts] across [Period]. Track Extracted Date alongside Account Id, Account Username, Account Most Recent Tweet Id, Followers Count, Following Count, Listed Count, Tweet Count, and the latest Tweet Id and Tweet Created At. Flag stale extractions older than [Freshness SLA], non-monotonic cumulative counts that require investigation, repeated snapshots with conflicting values, a most-recent Tweet Id that does not match the latest collected tweet, and unexplained username or verification-status changes. Produce a reconciliation summary by account plus a remediation queue that separates probable API/source behavior from probable ingestion defects.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect X / Twitter Public Data to ChatGPT

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How to connect X / Twitter Public Data to ChatGPT ?

Authorize the X / Twitter Public Data account in Catchr

Add the Catchr MCP from official ChatGPT store

Choose, adapt, and run a prompt.

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Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

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Twelve X / Twitter Public Data prompts for analyzing real account data.

X / Twitter Public Data to ChatGPT FAQs

What X / Twitter Public Data data can ChatGPT analyze through Catchr?

How granular can X / Twitter Public Data analysis be in ChatGPT?

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Can ChatGPT analyze audience growth and publishing patterns in X / Twitter Public Data?

What types of prompts work best for X / Twitter Public Data analysis?

Can I combine X / Twitter Public Data with other data sources in ChatGPT?

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How recent is the X / Twitter Public Data data, and how much history can I analyze?

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