apify-trend-analysis
Multi-platform trend discovery and tracking across Google Trends, Instagram, Facebook, YouTube, and TikTok.
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What it does
Supports 19 specialized Apify Actors covering search trends, hashtag tracking, engagement metrics, and viral content discovery across five major platforms
Dynamically fetches Actor schemas using mcpc CLI to retrieve input parameters and output fields before execution
Outputs results in three formats: quick chat display, CSV export, or JSON export with customizable result counts
Installation Guide
How to use apify-trend-analysis on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
apify-trend-analysis
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches apify-trend-analysis from apify/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate apify-trend-analysis. Access via /apify-trend-analysis in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Trend Analysis
Discover and track emerging trends using Apify Actors to extract data from multiple platforms.
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 trend 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 Trend Type
Select the appropriate Actor based on research needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Search trends | apify/google-trends-scraper |
Google Trends data |
| Hashtag tracking | apify/instagram-hashtag-scraper |
Hashtag content |
| Hashtag metrics | apify/instagram-hashtag-stats |
Performance stats |
| Visual trends | apify/instagram-post-scraper |
Post analysis |
| Trending discovery | apify/instagram-search-scraper |
Search trends |
| Comprehensive tracking | apify/instagram-scraper |
Full data |
| API-based trends | apify/instagram-api-scraper |
API access |
| Engagement trends | apify/export-instagram-comments-posts |
Comment tracking |
| Product trends | apify/facebook-marketplace-scraper |
Marketplace data |
| Visual analysis | apify/facebook-photos-scraper |
Photo trends |
| Community trends | apify/facebook-groups-scraper |
Group monitoring |
| YouTube Shorts | streamers/youtube-shorts-scraper |
Short-form trends |
| YouTube hashtags | streamers/youtube-video-scraper-by-hashtag |
Hashtag videos |
| TikTok hashtags | clockworks/tiktok-hashtag-scraper |
Hashtag content |
| Trending sounds | clockworks/tiktok-sound-scraper |
Audio trends |
| TikTok ads | clockworks/tiktok-ads-scraper |
Ad trends |
| Discover page | clockworks/tiktok-discover-scraper |
Discover trends |
| Explore trends | clockworks/tiktok-explore-scraper |
Explore content |
| Trending content | clockworks/tiktok-trends-scraper |
Viral content |
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/google-trends-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 results found
- File location and name
- Key trend insights
- Suggested next steps (deeper analysis, content opportunities)
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
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- YYusuf Smith★★★★★Dec 28, 2024
I recommend apify-trend-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- EEvelyn Farah★★★★★Dec 16, 2024
Keeps context tight: apify-trend-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- PPratham Ware★★★★★Dec 8, 2024
Solid pick for teams standardizing on skills: apify-trend-analysis is focused, and the summary matches what you get after install.
- SSakshi Patil★★★★★Nov 27, 2024
We added apify-trend-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- CChaitanya Patil★★★★★Oct 18, 2024
apify-trend-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- OOmar Torres★★★★★Sep 17, 2024
apify-trend-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
- MMia Shah★★★★★Sep 5, 2024
Keeps context tight: apify-trend-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- NNikhil Smith★★★★★Aug 24, 2024
apify-trend-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- LLayla Diallo★★★★★Aug 8, 2024
Solid pick for teams standardizing on skills: apify-trend-analysis is focused, and the summary matches what you get after install.
- AArjun Smith★★★★★Jul 27, 2024
We added apify-trend-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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