Apify Content Analytics
Pull post, reel, hashtag, and follower metrics from Instagram and related platforms via Apify Actors to measure campaign ROI without hand-scraping dashboards.
Overview
Apify Content Analytics is an agent skill for the Grow phase that tracks social content engagement and campaign performance using Apify Actors and mcpc-driven schema discovery.
Install
npx skills add https://github.com/apify/agent-skills --skill apify-content-analyticsWhat is this skill?
- Five-step checklist workflow: pick Actor, fetch schema with mcpc, confirm output format, run script, summarize findings
- Actor routing table for posts, reels, followers, comments, hashtags, mentions, and comprehensive Instagram scraping
- Uses Apify MCP client (mcpc) to load Actor schemas before execution
- Requires APIFY_TOKEN in .env and Node 20.6+ native --env-file support
- Covers Instagram-first analytics with framing for Facebook, YouTube, and TikTok performance
- Five-step analytics workflow checklist
- Seven Instagram-focused Actor mappings in the selection table
- Node.js 20.6+ required for native --env-file support
Adoption & trust: 2.6k installs on skills.sh; 2.1k GitHub stars; 1/3 security scanners passed (skills.sh audits).
What problem does it solve?
You are publishing on multiple social channels but lack a consistent, automatable way to pull engagement metrics and compare campaign ROI.
Who is it for?
Solo builders who already use Apify, store APIFY_TOKEN locally, and want repeatable Instagram-centric analytics runs from the agent.
Skip if: Teams that need enterprise social listening suites, guaranteed compliance review of scraping targets, or analytics without any Apify account or token.
When should I use this skill?
You need to track engagement metrics, measure campaign ROI, or analyze content performance across Instagram, Facebook, YouTube, and TikTok.
What do I get? / Deliverables
You get a runbook-selected Apify scrape, executed with validated inputs, plus an agent-written summary of content performance you can act on in lifecycle or distribution planning.
- Completed analytics script run for the chosen Apify Actor
- User-confirmed output format and filename
- Summarized engagement and performance findings
Recommended Skills
Journey fit
Canonical shelf is Grow because the skill’s outcome is engagement measurement and performance reporting after you have content in market. Analytics subphase fits extracting and summarizing cross-platform metrics rather than drafting posts or running ads.
How it compares
Use this procedural Apify + mcpc workflow instead of one-off chat requests to “scrape Instagram stats” without Actor schemas or token handling.
Common Questions / FAQ
Who is apify-content-analytics for?
It is for solo and indie builders in the Grow phase who measure content and campaign performance and want their coding agent to orchestrate Apify Actors rather than manual dashboard exports.
When should I use apify-content-analytics?
Use it when you need post, reel, follower, hashtag, or comment metrics after launch—especially during Grow analytics reviews, lifecycle retrospectives, or when comparing branded hashtag performance before the next distribution push.
Is apify-content-analytics safe to install?
It expects network access, your Apify API token, and shell execution via Node and mcpc; review the Security Audits panel on this Prism page and treat social scraping targets and rate limits as your compliance responsibility.
SKILL.md
READMESKILL.md - Apify Content Analytics
# 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: ```bash 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):** ```bash node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \ --actor "ACTOR_ID" \ --input 'JSON_INPUT' ``` **CSV:** ```bash 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:** ```bash 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_toke