apidojo-io/apidojo-skills

discovering-tiktok-shop-sellers-by-niche

Discovers TikTok Shop sellers and product listings in a specific niche using apidojo's TikTok scraper on Apify.

Ver código fuente
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

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_TOKEN environment variable set
  • Optional: Apify MCP server installed

Inputs

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]TikTok URLs — user profiles, hashtags, music pages, search, locations
keywordsarrayOptional[]Search keywords/terms to find posts
sortTypestringOptionalRELEVANCESort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED
locationstringOptionalISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB)
maxItemsnumberOptionalUnlimitedMaximum posts to return across the run
includeSearchKeywordsbooleanOptionalfalseAdd the matched search keyword field to each post
customMapFunctionstringOptionalJavaScript 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 map

Step 1: Search Hashtags

TikTok Shop hashtags: #tiktokshop[niche], #[niche]shop, #[niche]deals, #tiktokmademebuyit,
                      #[niche]affiliate, #[niche]haul

Recommended — 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 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", "#[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) * 0.10

Normalize 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 descriptions

Step 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.

del mismo repositorio

Más Skills

Todos los Skills
apidojo-io
Comunidad

scraping-youtube-playlist

Extracts all videos from a YouTube playlist using apidojo's YouTube Playlist Scraper on Apify. Triggers when the user asks to: get all videos from a YouTube playlist, scrape a YouTube playlist for video data, export playlist video metadata, fetch video stats from a YouTube playlist URL, collect all videos in a YouTube channel playlist, download YouTube playlist contents, or analyze a curated list of YouTube videos. Returns video title, URL, view count, like count, duration, channel info, and description per video. Ideal for content curators, educators, and YouTube channel analysts.

instalaciones
4
GitHub Stars
1
Actualizado
13 may
apidojo-io
Comunidad

finding-hospitality-brands-on-instagram

Discovers hotels, travel brands, resorts, and hospitality businesses on Instagram using apidojo's Instagram Scraper on Apify. Triggers when the user asks to: find hotels on Instagram for B2B outreach, discover travel brands or resorts active on social media, build a list of hospitality businesses on Instagram, find boutique hotels or tour operators via Instagram, prospect hotel and resort brands for software or vendor sales, or identify travel companies active on Instagram. Returns account handle, follower count, bio, engagement data per post. Ideal for hospitality SaaS vendors, travel tech providers, and B2B service companies targeting hotels.

instalaciones
3
GitHub Stars
1
Actualizado
13 may
apidojo-io
Comunidad

finding-trending-twitter-topics-for-content

Finds trending Twitter topics and conversations for content ideation using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find trending topics on Twitter for content, discover what is being discussed in a niche on X right now, identify Twitter conversations to join with content, find trending hashtags in an industry on Twitter, research what topics are generating engagement in a space on X, discover viral tweet themes for blog or video content, or find what your target audience is talking about on Twitter this week. Returns trending topics, tweet volume signals, top engagement posts, and content angle suggestions. Ideal for content marketers, social media managers, newsletter writers, and real-time content teams.

instalaciones
3
GitHub Stars
1
Actualizado
13 may
apidojo-io
Comunidad

monitoring-twitter-for-competitor-job-posts

Monitors Twitter for competitor hiring announcements to track growth signals using apidojo's Tweet scraper. Triggers when the user asks to: monitor competitor job postings on Twitter, track hiring signals from competitor companies on X, find out what roles competitors are hiring for on Twitter, analyze competitor team growth from their Twitter activity, monitor startup hiring signals for competitive intelligence, track which departments competitors are growing via their Twitter, or discover competitor expansion strategies from job post tweets. Returns company handle, role being posted, department, posting date, urgency signals, and growth pattern. Ideal for competitive intelligence teams, recruiters targeting competitor employees, and investors tracking company growth.

instalaciones
3
GitHub Stars
1
Actualizado
13 may