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apify-content-analytics

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

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查看安装方法

包含的文件(2)

  • SKILL.md4.2 KB
  • reference/scripts/run_actor.js11.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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 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

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