Apify market research

Skill newmindsgroup/ai-agent-skills-library/sources/sickn33-antigravity-awesome-skills/skills/apify-market-research

Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.From its SKILL.md

Install

npx -y skills add newmindsgroup/ai-agent-skills-library --skill apify-market-research

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

1 stars1 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 file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.8 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

Market Research

Conduct market research using Apify Actors to extract data from multiple platforms.

When to Use

You need market sizing, regional demand, pricing, trend, or consumer behavior data.
The task is to gather research inputs from maps, travel, Facebook, Instagram, or trend sources with Apify.
You need structured market data plus a synthesized view of opportunities or risks.

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 market research 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 Market Research Type

Select the appropriate Actor based on research needs:

User Need	Actor ID	Best For
Market density	`compass/crawler-google-places`	Location analysis
Geospatial analysis	`compass/google-maps-extractor`	Business mapping
Regional interest	`apify/google-trends-scraper`	Trend data
Pricing and demand	`apify/facebook-marketplace-scraper`	Market pricing
Event market	`apify/facebook-events-scraper`	Event analysis
Consumer needs	`apify/facebook-groups-scraper`	Group research
Market landscape	`apify/facebook-pages-scraper`	Business pages
Business density	`apify/facebook-page-contact-information`	Contact data
Cultural insights	`apify/facebook-photos-scraper`	Visual research
Niche targeting	`apify/instagram-hashtag-scraper`	Hashtag research
Hashtag stats	`apify/instagram-hashtag-stats`	Market sizing
Market activity	`apify/instagram-reel-scraper`	Activity analysis
Market intelligence	`apify/instagram-scraper`	Full data
Product launch research	`apify/instagram-api-scraper`	API access
Hospitality market	`voyager/booking-scraper`	Hotel data
Tourism insights	`maxcopell/tripadvisor-reviews`	Review analysis

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 results found
File location and name
Key market insights
Suggested next steps (deeper analysis, validation)

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.

What ships with it: 1 file

11.5 KB alongside SKILL.md, 1 of them executable

reference/

scripts/run_actor.jsruns11.5 KB

Gives 0 of the 12 instructions most research analysis skills give in ~1.1k tokens

Counted across 1,063 of the 1,754 authors here whose files we hold, read 2026-08-07

Generate a markdown reportin 32 of 1063, across 23 files
Cite each claim's sourcein 30 of 1063, across 15 files
Define the ideal customer profilein 20 of 1063, across 2 files
Search for companies matching the criteriain 20 of 1063, across 2 files
Assign a fit score from one to tenin 20 of 1063, across 2 files
Analyze the codebase to understand the productin 19 of 1063, across 1 file
Ask clarifying questions about the value propositionin 19 of 1063, across 1 file
Look for signals of immediate needin 19 of 1063, across 1 file
Identify the target decision maker rolein 19 of 1063, across 1 file
Suggest a personalized contact strategyin 19 of 1063, across 1 file
Provide conversation starters for outreachin 19 of 1063, across 1 file
Format results in a scannable markdown templatein 19 of 1063, across 1 file

Said here and by no other author read

select appropriate actor for research needs
ask user for output format and result count
summarize findings upon completion
stop if required inputs or criteria are missing

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.