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apify-ultimate-scraper

combined

Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X/Twitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task. AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

When to use

• The user needs web data extraction but has not yet chosen a specific Apify Actor.
• You need a general-purpose Apify entry point that maps a broad scraping goal to the most suitable Actor.
• The task spans multiple platforms and benefits from one unified workflow for actor selection, execution, and summarization.

When not to use

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The two copies describe the same high-level task (select an Apify Actor, fetch schema, run, deliver results) but they disagree on the execution mechanism and include material steps the other lacks. The apify/agent-skills copy is the canonical procedure: it mandates the Apify CLI, defines universal command rules (--json, --user-agent, 2>/dev/null), provides a dynamic actor search fallback, and includes a workflow-guide routing table. The sickn33 copy is a different implementation: it requires the mcpc CLI, hard-codes a large Actor catalog, adds a pricing-approval gate, and uses a checklist-driven workflow. Crowning one would hide a real alternative execution path and omit either the CLI rules or the pricing/actor-catalog constraints.