Use a written record of favorite and recommended artists, tracks, and genres to build playlists, suggest music, and discover adjacent artists. New suggestions are checked against the taste profile and distinguished from music already in the user's collection.
Learns the user's music taste as a written corpus and uses it to build playlists, recommend tracks, and discover adjacent artists. USE WHEN my music, my music taste, what do I like, build me a playlist, make a playlist, playlist for focus or a drive, recommend music, songs like, what should I listen to, new bands, discover new artists, find bands like the ones I have, expand my taste, add to my music, add this band, my favorite bands, music reference, update my music list. NOT FOR song generation, audio editing (AudioEditor), video audio, audio or studio gear.
Run score Not tested
See how to installKeep a written record of favorite and recommended tracks, artists, and genres, then use it to build playlists, recommend music, and discover new artists related to those you already like. Before making suggestions, the skill reads your taste profile, including preferences and things you want to avoid. It checks the existing list so that items already recorded are not presented as new discoveries. New music is connected to something in your collection, and additions are appended as a new batch with source information.
Use it to choose music for focused work or a drive, explore artists related to your favorites, or keep recommendations from other people in a separate category from favorites. It does not generate songs, edit audio, handle video audio, or help choose audio equipment.
A configuration file and corpus for recording your taste are required. If Spotify integration is available, its app keys are needed for search, and user sign-in is needed to create playlists. Without that integration, suggestions are checked against web sources and playlists are provided as lists you can paste into a music service.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).
Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 28, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Holds the user's music taste as a durable corpus (favorite tracks, artists, and genres, each tagged favorite or recommended) and uses it to build playlists, recommend tracks that fit, discover new artists next to the ones they already love, and grow the list as they feed in more. It reasons from the corpus, not from memory.
| Intent | Trigger | File |
|---|---|---|
| BuildPlaylist | "build me a playlist", "make a playlist", "playlist for <mood>" | Intents/BuildPlaylist.md |
| Recommend | "recommend music", "what should I listen to", "songs like X" | Intents/Recommend.md |
| DiscoverArtists | "new bands", "discover new artists", "bands like the ones I have", "expand my taste" | Intents/DiscoverArtists.md |
| AddMusic | "add to my music", "add this band", "log this album", "update my music list" | Intents/AddMusic.md |
| File | Read when |
|---|---|
Setup.md | first-time setup: the config file, the corpus format and playlist defaults, and a read-only proof |
Read <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Music/Config.md first (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). It names the corpus and holds the playlist defaults; when it is absent, the corpus is <configRoot>/LIFEOS/USER/MUSIC/MusicReference.md and the defaults in Intents/BuildPlaylist.md stand. First-time setup: Setup.md.
The corpus (Config.md Corpus: line):
## Taste Profile: the poles of the user's taste with anchor artists for each, and any stated bias (vocals, production, eras, things to avoid). That section is ground truth, never memory.### Batch N section under ## Tracks or ## Artists / Bands, with Artist as the second column, and a ## Counts block closes the file.LIFEOS/HERMES/Heartbeat/ArtistScan.ts) runs, every ⭐ row's Artist cell becomes a live tour-date search. Star only what the user loves.No account or key is needed for the skill itself. Existence checks and playlist creation use the Spotify skill when it is installed (search needs its app keys, playlists also need its user sign-in); without it, suggestions are checked against a fetched web source and playlists are delivered as a pasteable list.
Example 1: Build a playlist
User: "build me a focus playlist for deep work"
→ BuildPlaylist
→ A tight numbered list (Track — Artist — Album) of ⭐ corpus tracks ordered for flow, to paste into any service, or a Spotify playlist via the Spotify skill
Example 2: Discover new artists
User: "what new bands would I like?"
→ DiscoverArtists
→ Adjacent artists not in the corpus, grouped as "Because you have <Anchor> → <New artist>, <New artist>" with safe-fit/stretch tags
Example 3: Grow the corpus
User: "add these to my music: <three artists and an album>"
→ AddMusic
→ A new ### Batch section with schema rows and source tags, and updated counts
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