apidojo-io/apidojo-skills

finding-brand-collaborations-on-tiktok

Discovers brand collaboration patterns and sponsored content on TikTok using apidojo's TikTok scrapers on Apify.

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使用方法

  1. 复制安装命令,在自己的智能体环境安装前先检查源码包。
  2. 从原始文档核对所需工具、凭据和支持的宿主。
  3. 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-brand-collaborations-on-tiktok

开始前需了解

  • 安装不包含模型订阅、API 额度或访问文件的权限。
  • 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
仓库原始内容

按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。

Finding Brand Collaborations on TikTok

Maps active brand-creator partnerships in a product category. Identifies which brands are investing in TikTok influencer marketing, which creators they're working with, and the performance benchmarks for sponsored content.

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
locationstringOptional—ISO 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
customMapFunctionstringOptional—JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search sponsored content hashtags for category
- [ ] Step 2: Filter for sponsored signals
- [ ] Step 3: Extract brand and creator pairs
- [ ] Step 4: Analyze collaboration patterns
- [ ] Step 5: Deliver brand-creator map

Step 1: Search Posts

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": ["#ad", "#sponsored", "#[category]partner", "#[category]collab"],
  "keywords": ["[BRAND_NAME] sent", "use code", "thanks to [CATEGORY] brand"],
  "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": ["#ad", "#sponsored", "#fitnesspartner"],
    "maxItems": 300
  }'

Step 2: Extract Brand-Creator Pairs

From each sponsored post, extract:

  • brand_name: From caption mention (@[brand], [Brand] sent me, thanks to [Brand])
  • creator_handle: authorMeta.name
  • post_views: playCount
  • post_date: createTime
  • collaboration_type: #gifted (product seeding) vs. #ad/#sponsored (paid)

Step 3: Score Campaign Performance

sponsored_performance = playCount / 1000 (views in thousands)
creator_roi_proxy = playCount / followerCount  (view-to-follower ratio for sponsored post)

Brand collaboration frequency per creator:

collab_depth = count(posts where same brand appears) / total_posts_in_date_range

Step 4: Edge Cases

  • No #ad hashtag but clearly sponsored: Also scan for "gifted", "c/o", "partnership" in caption; TikTok disclosure is inconsistently applied
  • Brand name ambiguous (e.g. "Nike" in sports context): Verify by checking if creator's bio mentions brand partnership or if post has dedicated product showcase format
  • < 20 sponsored posts found: Category may be under-sponsored on TikTok; check if brands are using darker patterns (no disclosure) — widen search to product names alone

Output Format

# TikTok Brand Collaboration Map: [CATEGORY]
Posts analyzed: [N] | Sponsored posts identified: [N] | Unique brands: [N] | Date: [DATE]

## Brands Running Active TikTok Campaigns
| Brand | # Creators Used | # Posts | Avg Views/Post | Collaboration Type |
|-------|----------------|---------|----------------|-------------------|
| [brand] | [N] | [N] | [N] | [Gifted/Paid/Mixed] |

## Creator-Brand Pairs
| Creator | @Handle | Brand | Posts | Avg Views | Collab Type | Est. Reach |
|---------|---------|-------|-------|-----------|-------------|-----------|
| [name] | @[handle] | [brand] | [N] | [N] | [Gifted/Paid] | [N] |

## Benchmark: Sponsored Post Performance in [CATEGORY]
- Median views for sponsored posts: [N]
- Top-performing brand campaign: [brand] — [N] avg views
- Most-used creator tier: [micro/macro/mega]

Troubleshooting

Disclosure data sparse: TikTok doesn't always surface #ad in scraped captions. Check for brand mentions in text and product codes. Results outside the category: Refine search by combining #ad with a niche keyword: "#ad #fitness" or "#sponsored #skincare". Can't find competitor campaigns: If competitor avoids disclosure, search for their brand @mention directly in TikTok search.

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