apify-ultimate-scraper

Universal Web Scraper

Selective Reading Rule

Start with:

• references/senior-master-standard.md
• references/usage-routing.md
• references/quality-checklist.md

Then load only the inherited docs, scripts, assets, or examples that match the user's actual 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.

Prerequisites

(No need to check it upfront)

• .env file with APIFY_TOKEN
• Node.js 20.6+ (for native --env-file support)
• mcpc CLI tool: npm install -g @apify/mcpc

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Understand user goal and select Actor
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the scraper script
- [ ] Step 5: Summarize results and offer follow-ups

Step 1: Understand User Goal and Select Actor

First, understand what the user wants to achieve. Then select the best Actor from the options below.

Instagram Actors (12)

Facebook Actors (14)

TikTok Actors (14)

YouTube Actors (5)

Google Maps Actors (4)

Other Actors (6)

Actor Selection by Use Case

Multi-Actor Workflows

For complex tasks, chain multiple Actors:

Can't Find a Suitable Actor?

If none of the Actors above match the user's request, search the Apify Store directly:

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call search-actors keywords:="SEARCH_KEYWORDS" limit:=10 offset:=0 category:="" | jq -r '.content[0].text'

Replace SEARCH_KEYWORDS with 1-3 simple terms (e.g., "LinkedIn profiles", "Amazon products", "Twitter").

Step 2: Fetch Actor Schema

Fetch the Actor's input schema and details dynamically using mcpc:

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

Replace ACTOR_ID with the selected Actor (e.g., compass/crawler-google-places).

This returns:

• Actor description and README
• Required and optional input parameters
• Output fields (if available)

Step 3: Ask User Preferences

Before running, ask:

1. Output format:
   • Quick answer - Display top few results in chat (no file saved)
   • CSV - Full export with all fields
   • JSON - Full export in JSON format
2. Number of results: Based on character of use case

Step 4: Run the Script

Quick answer (display in chat, no file):

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Results and Offer Follow-ups

After completion, report:

• Number of results found
• File location and name
• Key fields available
• Suggested follow-up workflows based on results:

Error Handling

APIFY_TOKEN not found - Ask user to create .env with APIFY_TOKEN=your_token mcpc not found - Ask user to install npm install -g @apify/mcpc Actor not found - Check Actor ID spelling Run FAILED - Ask user to check Apify console link in error output Timeout - Reduce input size or increase --timeout

Limitations

• 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

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