Agent skill · marketing growth · bradautomates
tiktok-research
Research high-performing TikTok videos from tracked accounts using Apify's TikTok Scraper. Identifies outlier content, analyzes top 5 videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending TikTok content in a niche - Research what's performing on TikTok - Identify high-performing video patterns - Analyze competitors' TikTok content - Generate content ideas from TikTok trends - Run TikTok research - Find viral TikToks - Analyze hooks and content structure Triggers: "tiktok research", "tt research", "find trending tiktoks", "analyze tiktok accounts", "what's working on tiktok", "content research tiktok", "tiktok analysis", "tiktok trends
Why this skill is useful
Adds executable scripts for TikTok content analysis and reporting that enhance the AI's capabilities in social media research.
What it needs
Requires apify-client, google-genai installed locally. Requires apify account access. About 3k tokens when loaded. Last updated 2026-01-23. 197 stars on the source repository.
What this skill does
TikTok Research Research high-performing TikTok videos, identify outliers, and analyze top video content for hooks and structure. Prerequisites APIFYTOKEN environment variable or in .env GEMINIAPIKEY environment variable or in .env apify-client and google-genai Python packages Accounts configured in .claude/context/tiktok-accounts.md Verify setup: Workflow 1. Create Run Folder 2. Fetch Content Parameters: --days: Days back to search (default: 30) --limit: Max videos per account (default: 50) --sorting: "latest", "popular", or "oldest" (default: latest) --usernames: Override accounts file with specific usernames 3. Identify Outliers Output JSON contains: totalvideos: Number of videos analyzed outliercount: Number of outliers found topics: Top hashtags, sounds, and keywords accounts: List of accounts analyzed outliers: Array of outlier videos with engagement metrics 4. Analyze Top Videos with AI Extracts from each video: Hook technique and replicable formula Content structure and sections Retention techniques CTA strategy See the video-content-analyzer skill for full output schema and hook/format types. 5. Generate Report Read {RUNFOLDER}/outliers.json and {RUNFOLDER}/video-analysis.json, then generate {RUNFOLDER}/report.md. Report Structure: Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent. Quick Reference Full pipeline: Then read both JSON files and generate the report. Engagement Metrics Engagement Score: likes + (3 x comments) + (2 x shares) + (2 x saves) + (0.05 x views) Outlier Detection: Videos with engagement rate > mean + (threshold x stddev) Engagement Rate: (score / followers) x 100 TikTok-Specific Fields diggCount: Likes/hearts shareCount: Shares playCount: Video views commentCount: Comments collectCount: Saves/bookmarks authorFollowers: Creator's follower count musicName: Sound used in video musicOriginal: Whether sound is original
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills bradautomates/tiktok-research