What problem does it solve?
Gathering structured public X (Twitter) data for research requires navigating the Xquik Apify Actor's 15 modes, target types, and output formats while controlling paid-run costs. This Skill guides the agent to map requests to the correct mode, build valid Actor input, enforce cost ceilings, and return clean structured results.
Core Features & Use Cases
- 15 Research Modes: Search posts, read profiles, replies, quotes, threads, lists, articles, retweeters, and best-effort favoriters with validated target compatibility.
- Cost and Confirmation Controls: Displays live Apify pricing and a proposed charge ceiling, then blocks execution until the user explicitly confirms the paid run.
- Structured Output Envelope: Returns a stable JSON object separating post records from diagnostic rows, with exact record counts and clear status values.
- Use Case: Ask your agent to find the latest 20 English posts about AI agents; it shows the exact Actor input, current pricing, and a 1 USD ceiling, then waits for your confirmation before running.
Quick Start
Ask your agent to use xquik-x-tweet-scraper to find the latest 20 English posts about a topic, show the exact Actor input with live Apify pricing and a charge ceiling, and wait for your confirmation before running.