Apify content analytics

Skill sinhoneyy/master-skills/plugins/master-skills-backend-engineer/skills/apify-content-analytics

Unified skill library for Claude, Codex, Cursor, Antigravity & AI agents — 2,658 skills across 15 domains

Install

npx -y skills add sinhoneyy/master-skills --skill apify-content-analytics

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

SKILL.md

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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:

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
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.