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 "what's working on tiktok", "content research tiktok", "tiktok analysis", "tiktok trends"
npx skills add majiayu000/claude-skill-registry --skill tiktok-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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` - `apify-client` Python package - Accounts configured in `.claude/context/tiktok-accounts.md` Verify setup: ```bash python3 -c " import os try: from dotenv import load_dotenv load_dotenv() except ImportError: pass from apify_client import ApifyClient assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set' " && echo "Prerequisites OK" ``` ## Workflow ### 1. Create Run Folder ```bash RUN_FOLDER="tiktok-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER" ``` ### 2. Fetch Content ```bash python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py \ --days 30 \ --limit 50 \ --sorting latest \ --output {RUN_FOLDER}/raw.json ``` Parameters: - `--days`: Days back to search (default: 30) - `--limit`: Max videos per account (default: 50) - `--sorting`: "latest", "popular", or "oldest" (default: latest) - `--usernames`: Override accounts file with specific usernames ### 3. Identify Outliers ```bash python3 .claude/skills/tiktok-research/scripts/analyze_posts.p
- Prerequisites
- Workflow
- 1. Create Run Folder
- 2. Fetch Content
- 3. Identify Outliers
- 4. Analyze Top Videos with AI
- 5. Generate Report
- Quick Reference
- Engagement Metrics
- TikTok-Specific Fields
python3 -c "
import os
from dotenv import load_dotenv
except ImportError:
pass
from apify_client import ApifyClient
assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set'
python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py \
python3 .claude/skills/tiktok-research/scripts/analyze_posts.py \
python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py -o "$RUN_FOLDER/raw.json" && \What does the tiktok-research skill do?
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 "what's working on tiktok", "content research tiktok", "tiktok analysis", "tiktok trends"
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill tiktok-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.