tiktok-research

Research high-performing TikTok videos, identify outliers, and analyze top video content for hooks and structure. Verify setup: Parameters: Output JSON contains: Extracts from each video: See the video-content-analyzer skill for full output schema and hook/format types. Read {RUN_FOLDER}/outliers.json and {RUN_FOLDER}/video-analysis.json , then generate {RUN_FOLDER}/report.md . Report Structure: Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent. Full pipeline: Then read both JSON files and generate the report. 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 std_dev) Engagement Rate : (score / followers) x 100 - APIFY_TOKEN environment variable or in .env - GEMINI_API_KEY environment variable or in .env - apify-client and google-genai Python packages - Accounts configured in .claude/context/tiktok-accounts.md - --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 - total_videos : Number of videos analyzed - outlier_count : Number of outliers found - topics : Top hashtags, sounds, and keywords - accounts : List of accounts analyzed - outliers :...