music-discovery

# Music Discovery — Mood, Scene & Playlists ## Overview Helps listeners find **tracks and playlists** that fit a **mood**, **activity**, or **taste profile**—study, commute, workout, sleep, or “something like this artist.” Use when the user wants personalized picks, scene-based sets, or exploration without manual crate-digging. **Trigger keywords**: music recommendation, playlist, mood, BPM, study music, workout, discover similar artists ## Prerequisites ```bash pip install requests spotipy ``` ## Capabilities 1. **Data-backed discovery** — Spotify Web API / Last.fm–style metadata (see `references/music_discovery_guide.md`). 2. **Scene-based sets** — work, workout, wind-down, commute, focus, party. 3. **Vibe matching** — BPM, energy, valence/mood tags, genre boundaries. ## Commands | Command | Description | Example | |---------|-------------|---------| | `recommend` | Recommend tracks | `python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py recommend [args]` | | `playlist` | Build a playlist concept | `python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py playlist [args]` | | `mood` | Recommend by mood | `python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py mood [args]` | ## Usage (from repository root) ```bash python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py recommend --scene office --mood relaxed python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py playlist --scene workout --bpm 140 python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py mood --feeling happy ``` ## Output format (for the agent’s report) ```markdown # Music Discovery report **Generated**: YYYY-MM-DD HH:MM ## Key picks 1. [Track / artist — one-line why] 2. … 3. … ## Snapshot | Title | Artist | Why it fits | |-------|--------|---------------| ## Playlist sketch (optional) - **Theme**: … - **Tempo / energy**: … - **Avoid**: … ## Notes [Ground claims in API or user-stated taste—no invented chart positions.] ``` ## References ### APIs & libraries - [Spotify Web API](https://developer.spotify.com/documentation/web-api) - [MusicBrainz API](https://musicbrainz.org/doc/MusicBrainz_API) - [Spotipy (Python client)](https://github.com/spotipy-dev/spotipy) ### Patterns & community - [Daily Reddit digest (OpenClaw use case)](https://github.com/hesamsheikh/awesome-openclaw-usecases/blob/main/usecases/daily-reddit-digest.md) - [Hacker News — mood-based music ML](https://news.ycombinator.com/item?id=42457780) - [Reddit r/spotify — discussion](https://www.reddit.com/r/spotify/comments/1014b31yyz/music_recommender_ai/) ## Notes - Prefer **real** API or user-provided data; do not invent popularity or audio features. - Mark missing fields as **unavailable** instead of guessing. - OAuth and rate limits apply when using Spotify—document when credentials are required.