minimax-music-playlist

Files (2)

• SKILL.md
• artist_genre_map.json · data/

SKILL.md (first lines)

# MiniMax Music Playlist — Personalized Playlist Generator

Scan the user's music taste, build a taste profile, generate a personalized
playlist, and create an album cover. This skill is designed for both agent and direct
user invocation — adapt interaction style to context.

## Prerequisites

- **mmx CLI** — music & image generation. Install: `npm install -g mmx-cli`. Auth: `mmx auth login --api-key <key>`.
- **Python 3** — for scanning scripts you write on the fly (stdlib only, no pip).
- **Audio player** — `mpv`, `ffplay`, or `afplay` (macOS built-in).

## Language

Detect the user's language from their message. **All user-facing text must be
in the same language as the user's prompt** — do not mix languages. If the user
writes in Chinese, all output (profile summary, theme suggestions, playlist plan,
playback info) must be fully in Chinese. If in English, all in English.

All `mmx` generation prompts should be in English for best quality.
Each song's lyrics language follows its genre (K-pop → Korean, J-pop → Japanese, etc.),
NOT the user's UI language.

---

## Workflow

```
1. Scan local music apps → 2. Build taste profile → 3. Plan playlist
→ 4. Generate songs (mmx music) → 5. Generate cover (mmx image) → 6. Play → 7. Save & feedback
```

---

## Step 1: Gather Music Listening Data

Collect the user's listening data from available sources.

**Supported sources:**

| Source | Method | Data format |
|--------|--------|-------------|
| Apple Music | `osascript` to query Music.app (official AppleScript interface) | Track name, artist, album, genre, play count |
| Spotify | User exports their own data via [Spotify Privacy Settings](https://www.spotify.com/account/privacy/) | JSON files in ZIP (`Streaming_History_Audio_*.json`) |
| Manual input | User describes their taste directly | Free text |

**Spotify data export flow:**
Spotify does not store useful data locally. To include Spotify listening history,
first check if the user already has a Spotify data export:

1. Search for existing exports: `find ~ -maxdepth 4 -name "my_spotify_data.zip" -o -name "Streaming_History_Audio_*.json" 2>/dev/null`
2. If found, ask the user if they want to use it
3. If ZIP, unzip and locate `Spotify Extended Streaming History/Streaming_History_Audio_*.json`
4. If not found, open the Spotify privacy page: `open https://www.spotify.com/account/privacy/`
5. Tell the user to log in, scroll to "Download your data", and click "Request data"
6. Skip Spotify for now and continue with other sources — tell the user they can
   re-run the playlist skill after the data export arrives (usually a few days)

**Spotify data format:**
The export contains `Streaming_History_Audio_YYYY.json` files (one per year), each
is a JSON array of listening events. Key fields to extract:
- `master_metadata_album_artist_name` — artist name
- `master_metadata_track_name` — track name
- `master_metadata_album_album_name` — album name
- `ms_played` — playback duration in milliseconds (use as weight: longer = stronger signal)
- `ts` — timestamp

Filter out entries where `ms_played < 30000` (less than 30 seconds, likely skipped).
Do NOT use or store `ip_addr` or other sensitive fields.

**What to extract from each source:**
- Track names + artist names (primary signal)
- Playlist names and membership (e.g., a playlist named "Chinese Traditional" tells you genre preference)
- Play counts or streaming duration if available (weight frequently played tracks higher)
- Scene/mood tags if available

**Approach:**
1. Check if Apple Music is available (try `osascript` query)
2. Ask if the user has a Spotify data export ZIP to provide
3. If no sources available, ask the user to describe their taste manually

**Privacy rule:** Never show raw track lists to the user. Only show aggregated stats.

---

## Step 2: Build Taste Profile

From the scanned data, build a taste profile covering:

- **Genre distribution** — what styles the user listens to (e.g., J-pop 20%, R&B 15%, Classical 10%)
- **Mood tendencies** — emotional tone preferences (melancholic, energetic, calm, romantic, etc.)
- **Vocal preference** — male vs female voice ratio
- **Tempo preference** — slow / moderate / upbeat / fast distribution
- **Language distribution** — zh, en, ja, ko, etc.
- **Top artists** — most listened artists

**How to infer genre/mood from artist names:**
Most raw data only has artist + track names without genre tags. To enrich this:
1. Look up artists in the local mapping table at `<SKILL_DIR>/data/artist_genre_map.json`
   — this table covers 20,000 popular artists with pre-mapped genres, vocal type, and language
2. For artists not in the mapping table, query the MusicBrainz API:
   `https://musicbrainz.org/ws/2/artist/?query=artist:<name>&fmt=json`
   — extract genre tags from the response; respect rate limit (1 req/sec)
   — cache results to `<SKILL_DIR>/data/artist_cache.json` to avoid re-querying
3. If MusicBrainz returns no results, skip the artist

**Profile caching:**
- Save profile to `<SKILL_DIR>/data/taste_profile.json`
- If a profile less than 7 days old exists, reuse it (offer rescan option)
- If older or missing, rebuild

**Show user a summary:**
```
Your Music Profile:
  Sources: Apple Music 230 | Spotify 140
  Genres: J-pop 20% | R&B 15% | Classical 10% | Indie Pop 9%
  Moods: Melancholic 25% | Calm 20% | Romantic 18%
  Vocals: Female 65% | Male 35%
  Top artists: Faye Wong, Ryuichi Sakamoto, Taylor Swift, Jay Chou, Taeko Onuki

From MiniMax-AI/skills, MIT.

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