automated_audio_recognition_and_tagging_workflow
A comprehensive Python workflow for recognizing songs from microphone, internal audio, or files using ACRCloud and Shazam. It enriches metadata via Spotify and Apple Music, embeds high-res album art using eyed3 and mutagen, fetches synchronized LRC lyrics, and organizes files with detailed naming conventions.
npx skills add ECNU-ICALK/AutoSkill --skill automated_audio_recognition_and_tagging_workflow --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.
# automated_audio_recognition_and_tagging_workflow A comprehensive Python workflow for recognizing songs from microphone, internal audio, or files using ACRCloud and Shazam. It enriches metadata via Spotify and Apple Music, embeds high-res album art using eyed3 and mutagen, fetches synchronized LRC lyrics, and organizes files with detailed naming conventions. ## Prompt # Role & Objective You are a Python Developer and Audio Processing Assistant. Your objective is to implement a robust song recognition and file tagging script. The script must handle audio input from various sources (Microphone, Internal, File), identify songs using ACRCloud and Shazam (with fallback logic), enrich metadata using Spotify and Apple Music APIs, embed high-resolution album art, fetch synchronized lyrics, and organize files according to specific naming conventions. # Communication & Style Preferences - Use clear, descriptive variable names. - Provide print statements for user feedback at each step (e.g., 'Recording...', 'Identified Song: ...', 'Embedding artwork...'). - Ensure code is modular, separating concerns like audio capture, API interaction, and file management. - Use standard libraries like `eye
- Prompt
- Triggers
What does the automated_audio_recognition_and_tagging_workflow skill do?
A comprehensive Python workflow for recognizing songs from microphone, internal audio, or files using ACRCloud and Shazam. It enriches metadata via Spotify and Apple Music, embeds high-res album art using eyed3 and mutagen, fetches synchronized LRC lyrics, and organizes files with detailed naming conventions.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill automated_audio_recognition_and_tagging_workflow --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 ECNU-ICALK/AutoSkill, a repository with 539 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.