Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill discovering-tiktok-shop-sellers-by-nicheAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
Discovering TikTok Shop Sellers by Niche
Maps the TikTok Shop seller landscape in a product category. Identifies which products are being heavily promoted, which creators drive the most sales content, and where product gaps exist.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | Optional | [] | TikTok URLs — user profiles, hashtags, music pages, search, locations |
keywords | array | Optional | [] | Search keywords/terms to find posts |
sortType | string | Optional | RELEVANCE | Sort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED |
location | string | Optional | — | ISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB) |
maxItems | number | Optional | Unlimited | Maximum posts to return across the run |
includeSearchKeywords | boolean | Optional | false | Add the matched search keyword field to each post |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Search niche hashtags with TikTok Shop signals
- [ ] Step 2: Filter posts with shop product links
- [ ] Step 3: Extract seller profiles and products
- [ ] Step 4: Rank by sales signal strength
- [ ] Step 5: Deliver seller/product mapStep 1: Search Hashtags
TikTok Shop hashtags: #tiktokshop[niche], #[niche]shop, #[niche]deals, #tiktokmademebuyit,
#[niche]affiliate, #[niche]haulRecommended — run_actor.js (handles waiting, output, and file saving automatically):
bash
# 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 jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
"keywords": ["#tiktokshop", "#[niche]shop", "#tiktokmademebuyit"],
"maxItems": 300
}REST API fallback:
bash
curl -X POST "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" -H "Content-Type: application/json" -d '{
"keywords": ["#tiktokshop", "#kitchentools", "#tiktokmademebuyit"],
"maxItems": 300
}'Step 2: Score Seller Strength
sales_signal = (playCount / 10000) * 0.35
+ (diggCount / 1000) * 0.25
+ (commentCount / 100) * 0.15
+ (shareCount / 500) * 0.15
+ (hasShopLink ? 1 : 0) * 0.10Normalize to [0, 1]. Sellers with multiple high-signal posts get multi_post_bonus = 0.15.
Step 3: Classify Seller Type
seller_type:
BRAND = account.verified OR followerCount > 50K AND bio contains brand name
CREATOR_AFFILIATE = individual account, earns commission per sale
DROPSHIPPER = posts multiple unrelated products with generic descriptionsStep 4: Edge Cases
- Non-Shop posts in results: Drop posts where caption has no shop signal AND no product link
- Same product from many creators: De-duplicate by product name; count unique promoters as a "sales momentum" signal
- Saturated niche (> 50 active sellers): Flag as HIGH_COMPETITION; present gap analysis (underserved sub-niches)
- Views inflated by one viral post: Use median views across seller's last 10 posts as the signal, not max
Output Format
# TikTok Shop Seller Map: [NICHE]
Posts scanned: [N] | Unique sellers: [N] | Brands: [N] | Creator-affiliates: [N] | Date: [DATE]
## Top Sellers by Sales Signal
| Seller | Type | Product(s) | Avg Views/Post | Shop Posts | Signal Score |
|--------|------|-----------|----------------|-----------|-------------|
| @[handle] | Creator | [product] | [N] | [N] | [0.XX] |
## Top Products (by Promotion Frequency)
| Product Name | # Sellers Promoting | Avg Post Views | Price Range |
|-------------|--------------------|--------------------|-------------|
| [product] | [N] | [N] | $[X]-$[X] |
## Market Saturation
Competition level: [LOW / MEDIUM / HIGH] ([N] active sellers)
Potential gap: [sub-niche or product type with < 3 sellers]Troubleshooting
Hashtag returns non-shop content: Combine niche hashtag with #tiktokshop in the same search to anchor on shop-linked posts. Can't determine product price: TikTok Shop prices aren't always in the video — note URL pattern includes product ID which can be cross-referenced manually. All results from one mega-creator: Add max_posts_per_creator = 5 to surface diverse sellers.
