Content Analytics Skill

Content Analytics

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

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

Agent Skills: Content Analytics

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Skill Files