Youtube Chapter Generator

Auto-generate YouTube video chapters from subtitles. Parses VTT/SRT files, identifies topic transitions, and produces timestamped chapter markers. Triggers:...

This skill appears to do what it says, but there are important inconsistencies you should resolve before use: - Missing binary and script: The SKILL.md expects yt-dlp and a file named generate_chapters.py, but the skill metadata does not declare required binaries and the script is not included. Do not run commands from the SKILL.md until you (a) have yt-dlp installed from a trusted source and (b) have inspected or provided the generate_chapters.py code yourself. The included Python snippet is only partial and may not be the actual script the agent would run. - Trust and provenance: The skill's Source/Homepage are unknown. Running an external downloader (yt-dlp) and arbitrary Python scripts found or created at runtime can execute code on your machine. Only run these in a sandbox or after reviewing the script's contents. - What to check/steps to take: - Verify yt-dlp is installed from the official project and up-to-date. - Ask the skill author (or inspect the skill bundle) for the full generate_chapters.py. Review it for network calls, file writes, or subprocess.exec usage. - If you must proceed without the script, consider implementing your own chapter-generation script using the provided snippet and review it carefully. - Run initial tests on non-sensitive VM or container with sample subtitle files. - Functional note (non-security): The provided timestamp parsing is simplistic (ignores milliseconds in output selection and uses gap-only logic), so review accuracy requirements for your videos. If the author can supply the missing script or update the metadata to list yt-dlp and python3 as required, re-evaluate; until then treat this skill as suspicious.

Type: OpenClaw Skill Name: youtube-chapter-generator Version: 1.0.0 The skill bundle provides straightforward logic for parsing VTT subtitle files to generate YouTube chapter markers. The Python code in SKILL.md uses standard regex and basic arithmetic to identify time gaps, and the suggested use of yt-dlp is consistent with the tool's stated purpose without any signs of malicious intent, data exfiltration, or prompt injection.