# Apify Trend Analysis

> Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy.

- Skill: `techwavedev/apify-trend-analysis` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add techwavedev/apify-trend-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/apify-trend-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/apify-trend-analysis

---


# Trend Analysis

Discover and track emerging trends 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 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:

```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., `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:
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 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`


## When to Use
Use this skill when tackling tasks related to its primary domain or functionality as described above.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

```bash
# Check for prior backend/API context before starting
python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Apify Trend Analysis"
```

### Storing Results

After completing work, store backend/API decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \
  --type decision --project <project> \
  --tags apify-trend-analysis backend
```

### Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>
```

### Agent Team: Code Review

After implementation, dispatch `code_review_team` for two-stage review (spec compliance + code quality) before merging.

<!-- AGI-INTEGRATION-END -->