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Content Analytics

Free

Measure engagement and performance across social media platforms.

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Free · Opens the source repo

What Content Analytics does

Content Analytics is a skill designed for developers and marketers who need to track and analyze content performance across major social media platforms such as Instagram, Facebook, YouTube, and TikTok. By leveraging Apify Actors, this skill allows users to extract engagement metrics, assess campaign ROI, and gain insights into what content resonates most with audiences. The structured workflow guides users through selecting the appropriate Actor for their specific analytics needs, ensuring a tailored approach to data collection.

The skill operates by first identifying the type of content analytics required, whether it's engagement metrics for posts, follower growth tracking, or performance analysis of ads and hashtags. Users can choose from a variety of Actors, each designed to scrape relevant data from different platforms. Once the Actor is selected, the skill provides a straightforward process for fetching the Actor's schema, allowing users to understand the input parameters and output fields available.

After gathering user preferences regarding output format and the number of results, the skill executes the analytics script. Results can be displayed directly in chat or exported in CSV or JSON formats, making it easy to integrate findings into reports or further analysis. Finally, the skill summarizes key insights and suggests next steps, enabling users to optimize their content strategy effectively. This makes Content Analytics an essential tool for anyone looking to enhance their social media performance through data-driven decisions.

When to use it

Use this skill when you need to analyze engagement metrics for social media content across multiple platforms and require actionable insights.

When not to use it

This skill is not suitable for tasks that require real-time data or for platforms not supported by the Apify Actors listed.

What you can build with it

Analyzing Instagram Posts

Use the Instagram post scraper Actor to gather engagement metrics for your latest posts, helping you understand what content drives the most interaction.

Tracking Facebook Ad Performance

Leverage the Facebook ads scraper to evaluate the effectiveness of your advertising campaigns by analyzing engagement and reach metrics.

Evaluating TikTok Content

Utilize the TikTok scraper to assess the performance of your videos, gaining insights into viewer engagement and trends.

How to install Content Analytics

View source

1. Install with the skills CLI

npx skills add sickn33/agentic-awesome-skills/apify-content-analytics --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by sickn33

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 NeedActor IDBest For
Post engagement metricsapify/instagram-post-scraperPost performance
Reel performanceapify/instagram-reel-scraperReel analytics
Follower growth trackingapify/instagram-followers-count-scraperGrowth metrics
Comment engagementapify/instagram-comment-scraperComment analysis
Hashtag performanceapify/instagram-hashtag-scraperBranded hashtags
Mention trackingapify/instagram-tagged-scraperTag tracking
Comprehensive metricsapify/instagram-scraperFull data
API-based analyticsapify/instagram-api-scraperAPI access
Facebook post performanceapify/facebook-posts-scraperPost metrics
Reaction analysisapify/facebook-likes-scraperEngagement types
Facebook Reels metricsapify/facebook-reels-scraperReels performance
Ad performance trackingapify/facebook-ads-scraperAd analytics
Facebook comment analysisapify/facebook-comments-scraperComment engagement
Page performance auditapify/facebook-pages-scraperPage metrics
YouTube video metricsstreamers/youtube-scraperVideo performance
YouTube Shorts analyticsstreamers/youtube-shorts-scraperShorts performance
TikTok content metricsclockworks/tiktok-scraperTikTok 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.

Frequently asked questions about Content Analytics

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