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tiktok-scraping-yt-dlp

Use for TikTok crawling, content retrieval, and analysis

Why use this skill?

Easily download TikTok videos, profiles, and metadata using yt-dlp. Analyze trends, archive content, and gather data with advanced filtering options.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/romneyda/tiktok-crawling
Or

What This Skill Does

The TikTok Scraping skill leverages the powerful yt-dlp command-line interface to enable comprehensive crawling and content retrieval from TikTok. This skill is designed to download videos, audio, and associated metadata from individual TikTok posts, entire user profiles, or even from search results, hashtags, and sounds. It offers advanced filtering capabilities based on dates, view counts, duration, and keywords within titles, allowing users to target specific content.

Furthermore, the skill can be used to preview download information without actually downloading files, and to export metadata efficiently in JSON or JSONL formats. This makes it invaluable for researchers, marketers, content creators, and anyone needing to gather and analyze TikTok data programmatically.

Installation

To install this skill, use the following command:

clawhub install openclaw/skills/skills/romneyda/tiktok-crawling

Dependencies:

For optimal performance, especially for merging audio and video streams or post-processing, it is recommended to have ffmpeg installed on your system. yt-dlp itself can be installed via pip:

pip install yt-dlp

Use Cases

  • Content Archiving: Download entire TikTok profiles or specific videos for personal archiving or backup.
  • Market Research: Scrape content related to specific hashtags or keywords to understand trends and user engagement.
  • Trend Analysis: Identify popular videos based on view counts or engagement metrics within specific date ranges.
  • Creator Analytics: Analyze the content performance of specific creators by downloading their video metadata.
  • Sound Trend Identification: Discover and download videos associated with trending sounds on the platform.
  • Data for Machine Learning: Export video metadata in JSON or JSONL format for use in training AI models or further data analysis.

Example Prompts

  1. "Download all videos from the TikTok profile '@username' and save them into a folder named 'username_tiktok_data', including metadata JSON files."
  2. "Find and download the top 50 most viewed TikTok videos tagged with #booktok from the last month, storing them with detailed information."
  3. "Extract metadata for all TikTok videos containing the keyword 'recipe' in their title, posted in February 2026, and save it as a JSONL file."

Tips & Limitations

  • Rate Limiting: Be mindful of TikTok's rate limits. Excessive scraping in a short period might lead to temporary blocks. Use the --download-archive option to avoid re-downloading videos and space out requests if necessary.
  • Format Selection: Use yt-dlp -F <url> to list available formats and -f <format_code> to download specific ones. The default usually selects the best available quality.
  • Metadata: The --write-info-json or -j flags are crucial for obtaining detailed metadata. The --simulate flag is excellent for testing your query and filters without downloading.
  • Legality and Ethics: Always respect TikTok's Terms of Service and copyright laws. Use this skill responsibly for data analysis and personal use, not for unauthorized redistribution or commercial exploitation of content.
  • ffmpeg Requirement: For downloading formats that require merging audio and video, or for other post-processing tasks like converting formats, ffmpeg is often a necessary dependency.

Metadata

Author@romneyda
Stars1133
Views12
Updated2026-02-18
View Author Profile
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-romneyda-tiktok-crawling": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#tiktok#scraping#yt-dlp#content-analysis#data-collection
Safety Score: 4/5

Flags: network-access, file-write, data-collection