Install
$ agentstack add skill-tmolavi-mcp-agent-skills-hub-apify-audience-analysis ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Audience Analysis
Analyze and understand your audience using Apify Actors to extract follower demographics, engagement patterns, and behavior data from multiple platforms.
When to Use
- You need audience demographics, engagement patterns, or follower behavior from social platforms.
- The task is to choose and run Apify Actors for audience analysis across Facebook, Instagram, YouTube, or TikTok.
- You need structured extraction plus a summarized interpretation of audience findings.
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 audience analysis type (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analysis script
- [ ] Step 5: Summarize findings
Step 1: Identify Audience Analysis Type
Select the appropriate Actor based on analysis needs:
| User Need | Actor ID | Best For | |-----------|----------|----------| | Facebook follower demographics | apify/facebook-followers-following-scraper | FB followers/following lists | | Facebook engagement behavior | apify/facebook-likes-scraper | FB post likes analysis | | Facebook video audience | apify/facebook-reels-scraper | FB Reels viewers | | Facebook comment analysis | apify/facebook-comments-scraper | FB post/video comments | | Facebook content engagement | apify/facebook-posts-scraper | FB post engagement metrics | | Instagram audience sizing | apify/instagram-profile-scraper | IG profile demographics | | Instagram location-based | apify/instagram-search-scraper | IG geo-tagged audience | | Instagram tagged network | apify/instagram-tagged-scraper | IG tag network analysis | | Instagram comprehensive | apify/instagram-scraper | Full IG audience data | | Instagram API-based | apify/instagram-api-scraper | IG API access | | Instagram follower counts | apify/instagram-followers-count-scraper | IG follower tracking | | Instagram comment export | apify/export-instagram-comments-posts | IG comment bulk export | | Instagram comment analysis | apify/instagram-comment-scraper | IG comment sentiment | | YouTube viewer feedback | streamers/youtube-comments-scraper | YT comment analysis | | YouTube channel audience | streamers/youtube-channel-scraper | YT channel subscribers | | TikTok follower demographics | clockworks/tiktok-followers-scraper | TT follower lists | | TikTok profile analysis | clockworks/tiktok-profile-scraper | TT profile demographics | | TikTok comment analysis | clockworks/tiktok-comments-scraper | TT comment engagement |
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/facebook-followers-following-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 audience members/profiles analyzed
- File location and name
- Key demographic insights
- Suggested next steps (deeper analysis, segmentation)
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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: tmolavi
- Source: tmolavi/mcp-agent-skills-hub
- License: MIT
- Homepage: https://molavi.pro
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.