apify-market-research
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
Best use case
apify-market-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
Teams using apify-market-research should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/apify-market-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How apify-market-research Compares
| Feature / Agent | apify-market-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
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SKILL.md Source
# Market Research
Conduct market research 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 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:
```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., `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:
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 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
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