Skills Data Science Cross-Platform Content Analytics Toolkit

Cross-Platform Content Analytics Toolkit

v20260427
apify-content-analytics
A comprehensive toolkit designed to track and analyze content performance, engagement metrics, and Return on Investment (ROI) across major social media platforms. By utilizing specialized Apify Actors, this skill extracts deep insights from Instagram, Facebook, YouTube, TikTok, and more, helping users identify high-performing content, monitor growth, and measure marketing campaign effectiveness.
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Overview

Content Analytics

Track and analyze content performance using Apify Actors to extract engagement metrics from multiple platforms.

When to Use

  • You need engagement, growth, or ROI metrics for posts, reels, videos, ads, or hashtags.
  • The task is to use Apify Actors to collect cross-platform content performance data.
  • You need exported analytics results and a concise interpretation of what content is performing best.

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: Identify content analytics type (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analytics script
- [ ] Step 5: Summarize findings

Step 1: Identify Content Analytics Type

Select the appropriate Actor based on analytics needs:

User Need Actor ID Best For
Post engagement metrics apify/instagram-post-scraper Post performance
Reel performance apify/instagram-reel-scraper Reel analytics
Follower growth tracking apify/instagram-followers-count-scraper Growth metrics
Comment engagement apify/instagram-comment-scraper Comment analysis
Hashtag performance apify/instagram-hashtag-scraper Branded hashtags
Mention tracking apify/instagram-tagged-scraper Tag tracking
Comprehensive metrics apify/instagram-scraper Full data
API-based analytics apify/instagram-api-scraper API access
Facebook post performance apify/facebook-posts-scraper Post metrics
Reaction analysis apify/facebook-likes-scraper Engagement types
Facebook Reels metrics apify/facebook-reels-scraper Reels performance
Ad performance tracking apify/facebook-ads-scraper Ad analytics
Facebook comment analysis apify/facebook-comments-scraper Comment engagement
Page performance audit apify/facebook-pages-scraper Page metrics
YouTube video metrics streamers/youtube-scraper Video performance
YouTube Shorts analytics streamers/youtube-shorts-scraper Shorts performance
TikTok content metrics clockworks/tiktok-scraper TikTok analytics

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., apify/instagram-post-scraper).

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 Findings

After completion, report:

  • Number of content pieces analyzed
  • File location and name
  • Key performance insights
  • Suggested next steps (deeper analysis, content optimization)

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 if required inputs, permissions, safety boundaries, or success criteria are missing.
Info
Category Data Science
Name apify-content-analytics
Version v20260427
Size 5.66KB
Updated At 2026-04-28
Language