bundled-skills/apify-market-research/SKILL.md
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
npx skillsauth add FrancoStino/opencode-skills-antigravity apify-market-researchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Conduct market research using Apify Actors to extract data from multiple platforms.
(No need to check it upfront)
.env file with APIFY_TOKEN--env-file support)mcpc CLI tool: npm install -g @apify/mcpcCopy 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
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 |
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:
Before running, ask:
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
After completion, report:
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
tools
Authorized security assessment of LLM applications and AI agents: prompt injection, tool abuse, RAG exposure, memory poisoning, system-prompt extraction, and agent-compliance engineering per OWASP LLM/ASI Top 10.
development
Builds two parameterized UI modes—流光溢彩白 (iridescent white) and 五彩斑斓黑 (colorful black)—with OKLCH, WebGL/CSS fallback, vision gating, screenshot QA, and total/per-color intensity reports. Use when a UI request names either mode or needs measured color parameters.
tools
Delegate coding tasks to the Kimi Code CLI (`kimi`) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
development
Front-end JavaScript reverse engineering: locate signature chains, analyze encrypted request parameters, sample runtime behavior, and reproduce logic locally in Node for evidence-based output.