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finding-fitness-brands-on-tiktok

finding-fitness-brands-on-tiktok

github.com/apidojo-ioMarketing & growth1779 installsUpdated 24w ago
Repository

About finding-fitness-brands-on-tiktok

A skill for discovering fitness brands through TikTok posts and evaluating channels with follower, engagement, and verification signals. It uses the apidojo TikTok scraper and provides ranking guidance for prospecting.

Key features

  1. 01TikTok のキーワード・URL検索
  2. 02地域と投稿順の指定
  3. 03投稿数の上限設定
  4. 04フォロワー数や反応率で評価
  5. 05見込み先のランク分類

Use cases

  1. 01営業候補となるフィットネスブランドの発見
  2. 02国別にフィットネス投稿を調査
  3. 03反応や認証状況を基に候補を優先付け

MCP support

Apify MCP の call_actor ツールで apidojo~tiktok-scraper を呼び出せます。

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-fitness-brands-on-tiktok

After installing, type/finding-fitness-brands-on-tiktokin Claude Code to confirm it loaded.

Details

From the project README

Finding Fitness Brands On Tiktok


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

How to Run

Using run_actor.js (recommended)
# Quick answer (table)
node scripts/run_actor.js --actor "apidojo~tiktok-scraper" --input '{"keywords": ["fitness studio", "gym owner"], "sortType": "MOST_LIKED", "maxItems": 100}'

# Save as CSV
node scripts/run_actor.js --actor "apidojo~tiktok-scraper" --input '{"keywords": ["fitness studio", "gym owner"], "sortType": "MOST_LIKED", "maxItems": 100}' --output results.csv --format csv

# Save as JSON
node scripts/run_actor.js --actor "apidojo~tiktok-scraper" --input '{"keywords": ["fitness studio", "gym owner"], "sortType": "MOST_LIKED", "maxItems": 100}' --output results.json --format json
REST API fallback
curl -X POST "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["fitness studio", "gym owner"], "sortType": "MOST_LIKED", "maxItems": 100}'

If Apify MCP is available: Use the Apify MCP call_actor tool with actor apidojo~tiktok-scraper and the input above.


Scoring & Ranking

Score each channel by:

  • followers → normalized 0-1 (cap at 500K), weight 0.35
  • avg_engagement = (likes + comments + shares) / views → weight 0.35
  • verified → 0 or 1, weight 0.30
score = 0.35 * min(followers / 500000, 1.0) + 0.35 * min(avg_engagement / 0.10, 1.0) + 0.30 * int(verified)

Classification

ScoreTierLabel
≥ 0.70APRIME_PROSPECT
0.40–0.69BWARM_LEAD
< 0.40CLOW_PRIORITY

Edge Cases

  • Mixed results with consumers: Filter by channel.followers > 1000 to focus on established brands.
  • Keyword too broad: "fitness" returns individual users. Use "fitness business", "gym owner", "fitness studio" instead.
  • No contact info in TikTok: Cross-reference with Instagram or website links in bio.
  • Duplicate channels: Same brand may appear multiple times — deduplicate by channel.username.
  • Inflated views: TikTok can have high views but low followers for viral one-offs — balance both metrics.

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