Apify competitor intelligence

Skill pinkpixel-dev/skills-collection-1/SKILLS/apify-competitor-intelligence

Part 1 of a large AI and agent skills collection featuring 900+ reusable skill folders, prompt workflows, references, scripts, and assets across engineering, cloud, security, research, writing, design, and automation.

Install

npx -y skills add pinkpixel-dev/skills-collection-1 --skill apify-competitor-intelligence

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Analyze competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, YouTube, and TikTok.

SKILL.md

5.2 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Competitor Intelligence

Analyze competitors using Apify Actors to extract data 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 competitor 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 Competitor Analysis Type

Select the appropriate Actor based on analysis needs:

User Need	Actor ID	Best For
Competitor business data	`compass/crawler-google-places`	Location analysis
Competitor contact discovery	`poidata/google-maps-email-extractor`	Email extraction
Feature benchmarking	`compass/google-maps-extractor`	Detailed business data
Competitor review analysis	`compass/Google-Maps-Reviews-Scraper`	Review comparison
Hotel competitor data	`voyager/booking-scraper`	Hotel benchmarking
Hotel review comparison	`voyager/booking-reviews-scraper`	Review analysis
Competitor ad strategies	`apify/facebook-ads-scraper`	Ad creative analysis
Competitor page metrics	`apify/facebook-pages-scraper`	Page performance
Competitor content analysis	`apify/facebook-posts-scraper`	Post strategies
Competitor reels performance	`apify/facebook-reels-scraper`	Reels analysis
Competitor audience analysis	`apify/facebook-comments-scraper`	Comment sentiment
Competitor event monitoring	`apify/facebook-events-scraper`	Event tracking
Competitor audience overlap	`apify/facebook-followers-following-scraper`	Follower analysis
Competitor review benchmarking	`apify/facebook-reviews-scraper`	Review comparison
Competitor ad monitoring	`apify/facebook-search-scraper`	Ad discovery
Competitor profile metrics	`apify/instagram-profile-scraper`	Profile analysis
Competitor content monitoring	`apify/instagram-post-scraper`	Post tracking
Competitor engagement analysis	`apify/instagram-comment-scraper`	Comment analysis
Competitor reel performance	`apify/instagram-reel-scraper`	Reel metrics
Competitor growth tracking	`apify/instagram-followers-count-scraper`	Follower tracking
Comprehensive competitor data	`apify/instagram-scraper`	Full analysis
API-based competitor analysis	`apify/instagram-api-scraper`	API access
Competitor video analysis	`streamers/youtube-scraper`	Video metrics
Competitor sentiment analysis	`streamers/youtube-comments-scraper`	Comment sentiment
Competitor channel metrics	`streamers/youtube-channel-scraper`	Channel analysis
TikTok competitor analysis	`clockworks/tiktok-scraper`	TikTok data
Competitor video strategies	`clockworks/tiktok-video-scraper`	Video analysis
Competitor TikTok profiles	`clockworks/tiktok-profile-scraper`	Profile data

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., compass/crawler-google-places).

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 competitors analyzed
File location and name
Key competitive insights
Suggested next steps (deeper analysis, benchmarking)

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

Gives 0 of the 12 instructions most social media skills give in ~1.3k tokens

Counted across 489 of the 492 authors here whose files we hold, read 2026-08-06

build content around three to five pillarsin 24 of 489, across 12 files
read product marketing context before asking questionsin 23 of 489, across 13 files
respond to all comments on your postsin 21 of 489, across 9 files
adapt tone and structure per platformin 18 of 489, across 8 files
adapt content for each platformin 15 of 489, across 10 files
use the output flag to specify an output directoryin 14 of 489, across 4 files
Generate output logo images with white backgroundin 13 of 489, across 4 files
Fix failing generation scripts directlyin 13 of 489, across 4 files
ask user about html preview after logo generationin 12 of 489, across 3 files
run the download script with a URLin 12 of 489, across 3 files
implement exponential backoff for 429 responsesin 12 of 489, across 3 files
include a single clear call to actionin 12 of 489, across 9 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.