
Content Analytics
FreeMeasure engagement and performance across social media platforms.
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 source1. Install with the skills CLI
npx skills add sickn33/agentic-awesome-skills/apify-content-analytics --agent claude-code2. 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 sickn33Content 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)
.envfile withAPIFY_TOKEN- Node.js 20.6+ (for native
--env-filesupport) mcpcCLI 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.
Frequently asked questions about Content Analytics
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