tiktok-research

TikTok Research Research high-performing TikTok videos, identify outliers, and analyze top video content for hooks and structure. Prerequisites 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 Verify setup: python3 -c " import os try: from dotenv import load_dotenv load_dotenv() except ImportError: pass from apify_client import ApifyClient from google import genai assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set' assert os.environ.get('GEMINI_API_KEY'), 'GEMINI_API_KEY not set' " && echo "Prerequisites OK" Workflow 1. Create Run Folder RUN_FOLDER="tiktok-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER" 2. Fetch Content python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py \ --days 30 \ --limit 50 \ --sorting latest \ --output {RUN_FOLDER}/raw.json 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 python3 .claude/skills/tiktok-research/scripts/analyze_posts.py \ --input {RUN_FOLDER}/raw.json \ --output {RUN_FOLDER}/outliers.json \ --threshold 2.0 Output JSON contains: total_videos: Number of videos analyzed outlier_count: 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 python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \ --input {RUN_FOLDER}/outliers.json \ --output {RUN_FOLDER}/video-analysis.json \ --platform tiktok \ --max-videos 5 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 {RUN_FOLDER}/outliers.json and {RUN_FOLDER}/video-analysis.json, then generate {RUN_FOLDER}/report.md. Report Structure: # TikTok Research Report Generated: {date} ## Top Performing Hooks Ranked by engagement. Use these formulas for your content. ### Hook 1: {technique} - @{username} - Opening: "{opening_line}" - Why it works: {attention_grab} - Replicable Formula: {replicable_formula} - Engagement: {diggCount} likes, {commentCount} comments, {playCount} views - Watch Video [Repeat for each analyzed video] ## Content Structure Patterns | Video | Format | Pacing | Key Retention Techniques | |-------|--------|--------|--------------------------| | @username | {format} | {pacing} | {techniques} | ## CTA Strategies | Video | CTA Type | CTA Text | Placement | |-------|----------|----------|-----------| | @username | {type} | "{cta_text}" | {placement} | ## All Outliers | Rank | Username | Likes | Comments | Shares | Views | Engagement Rate | |------|----------|-------|----------|--------|-------|-----------------| [List all outliers with metrics and links] ## Trending Topics ### Top Hashtags [From outliers.json topics.hashtags] ### Top Sounds [From outliers.json topics.sounds] ### Top Keywords [From outliers.json topics.keywords] ## Actionable Takeaways [Synthesize patterns into 4-6 specific recommendations] ## Accounts Analyzed [List accounts] Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent. Quick Reference Full pipeline: RUN_FOLDER="tiktok-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && \ python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py -o "$RUN_FOLDER/raw.json" && \ python3 .claude/skills/tiktok-research/scripts/analyze_posts.py -i "$RUN_FOLDER/raw.json" -o "$RUN_FOLDER/outliers.json" && \ python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py -i "$RUN_FOLDER/outliers.json" -o "$RUN_FOLDER/video-analysis.json" -p tiktok 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 std_dev) 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 Weekly Installs338Repositorybradautomates/h…-contentGitHub Stars39First SeenJan 28, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled onopencode312gemini-cli308codex302github-copilot298cursor297kimi-cli291