Imported from apidojo-io/apidojo-skills (skills/intent/finding-tiktok-shop-trending-products/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill finding-tiktok-shop-trending-products. Copyright stays with the author (Apache-2.0).

Finding TikTok Shop Trending Products

Identifies products gaining momentum on TikTok Shop by analyzing creator promotion density and engagement. A product promoted by many creators simultaneously is a strong viral commerce signal.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

Progress:
- [ ] Step 1: Search TikTok Shop hashtags for category
- [ ] Step 2: Extract products from promotional posts
- [ ] Step 3: Count creator promotion density per product
- [ ] Step 4: Score product trend momentum
- [ ] Step 5: Deliver trending product list

Step 1: Search Posts

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
  "keywords": ["#tiktokshop[category]", "#[category]deals", "#tiktokmademebuyit"],
  "maxItems": 300
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["#tiktokmademebuyit", "#skincaretiktokshop"], "maxItems": 300}'

Step 2: Score Products

Group posts by product name from captions. For each product:

promotion_density = count(unique creators promoting product)
total_reach = sum(playCount for all promoting posts)
engagement_rate = sum(diggCount) / total_reach * 100

trend_score = (promotion_density / 5, max 1) * 0.35
            + (total_reach / 1000000, max 1) * 0.35
            + (engagement_rate > 3 ? 1 : engagement_rate/3) * 0.30

Trend window: 70%+ promotions in last 7 days = PEAKING; spread evenly = SUSTAINED; mostly > 7 days = FADING

Step 3: Edge Cases

• Product name extraction unreliable: Extract most specific noun phrase from caption alongside shop link
• Same product different variants: Group by product name similarity; sum reach across variants
• Saturated trend (> 50 creators): Window may be closing; flag as LATE_STAGE
• Counterfeit signals: Price far below market (e.g. < $5 for normally $30+ item) → flag

Output Format

# TikTok Shop Trending Products: [CATEGORY]
Posts analyzed: [N] | Unique products: [N] | Date: [DATE]

## Trending Now (Peak Momentum)
| Product | Creators | Total Views | Avg Eng Rate | Stage | Score |
|---------|---------|------------|-------------|-------|-------|

## Sustained Performers
| Product | Creators | Total Views | Stage |
|---------|---------|-----------|-------|

## Saturation Alert
[Products with > 30 creators — difficult to enter profitably]

Troubleshooting

Few products identified: Check bio links of top promoting creators for product page. Trend already peaked: Run every 3-4 days to catch trends early. Suspicious pricing: Flag and verify before sourcing.