analyzing-tiktok-hashtag-performance skill by apidojo-io

Analyzing TikTok Hashtag Performance

Scrapes TikTok hashtag pages to pull top-performing videos, engagement data, and creator information. Compares multiple hashtags side-by-side to identify which ones deliver the best reach for a given content category.

Prerequisites

APIFY_TOKEN environment 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: Define hashtags to analyze
- [ ] Step 2: Run tiktok-scraper for each hashtag
- [ ] Step 3: Calculate hashtag-level metrics
- [ ] Step 4: Identify top content and creators
- [ ] Step 5: Deliver strategy recommendations

Step 1: Clarify Parameters

Ask the user for:

Hashtags to analyze — up to 10 (without #)
Posts per hashtag (default: 30 — enough for reliable stats)
Goal — choosing hashtags for a post, auditing a competitor's hashtag strategy, or general research

If the user hasn't provided hashtags yet and wants recommendations, ask for:

Content niche (e.g., "fitness", "cooking", "personal finance")
Then generate a mix of: 2 mega hashtags (100M+ views), 3 mid-tier (10M–100M), 3 niche (1M–10M), 2 micro (<1M)

Step 2: Run the Actor Per Hashtag

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

# Quick answer (prints table to chat)
node scripts/run_actor.js

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

REST API fallback:

Wait for SUCCEEDED. Fetch dataset.

Step 3: Calculate Hashtag Metrics

For each hashtag, from its video results:

Use challengeInfo.stats.videoCount (if available) as total hashtag size proxy.

Step 4: Identify Top Content and Creators

For each hashtag, surface:

Top 3 videos by play count (with creator handle and video URL)
Top 3 creators by frequency in the hashtag's top content
Common content formats in top videos (based on descriptions/captions)

Step 5: Format Output

Output Format

Troubleshooting

Very low view counts: Hashtag may be misspelled or very new. Verify spelling and try alternate versions. All results look the same: Mega-hashtags (#fyp, #foryou) surface algorithmically promoted content, not organic. Use niche hashtags for better signal. Engagement rate seems too high or low: Engagement rate varies heavily by content type — compare within the same content format for fair benchmarking.