Automate VLC media player configuration, playback control, and verification via Python.
npx skills add https://github.com/xlang-ai/CUA-Gym --skill vlc
Please help me install this Agent Skill. Skill: vlc Source: https://github.com/xlang-ai/CUA-Gym/tree/main/.claude/skills/vlc Command: npx skills add https://github.com/xlang-ai/CUA-Gym --skill vlc
This Skill automates the configuration, control, and verification of VLC media player state, streamlining setup and reward generation processes for agents.
Use the vlc skill to play a video file '/path/to/video.mp4' and set the volume to 100.
High-intent search queries and answers about installing and using this skill.
Automate VLC media player configuration by programmatically modifying vlcrc files using Python. This approach streamlines setup generation for media playback environments, allowing agents to automatically apply necessary settings without manual intervention.
Verify VLC playback state, media configuration, and subtitle settings using Python. The verification process checks the media player's current state to ensure correctness for reward generation agents in RLVR training environments.
FFmpeg is optionally required for media operations when automating VLC media player control with Python. Core dependencies include subprocess, requests, xml.etree.ElementTree, librosa, cv2, imagehash, and Pillow for full playback and verification functionality.
Automate video playback, pausing, and seeking in VLC using Python subprocess calls. This method applies directly to RLVR training environments, serving setup-gen and reward-gen agents by controlling media player states programmatically.
Python media verification uses librosa for audio analysis and cv2 for video frame processing to validate VLC playback state. Combined with imagehash and Pillow, these libraries verify media configuration correctness and subtitle settings during automated operations.
VLC setup automation uses vlcrc file modification via xml.etree.ElementTree to configure settings persistently. This method ensures reliable configuration states for reward generation agents, bypassing transient API states by writing directly to the configuration file.
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