Apple Music DJ

Ultimate personalization engine for Apple Music. Analyzes listening history, Apple Music Replay stats, library data, and taste patterns to create intelligent...

Security Scan

The skill's code, declared requirements, and runtime instructions are coherent with an Apple Music personalization tool — the requested tokens and binaries match its stated purpose and there are no obvious unrelated secrets or hidden endpoints.

Purpose & Capability

Name/description match required artifacts: the skill integrates with MusicKit and therefore legitimately needs APPLE_MUSIC_DEV_TOKEN and APPLE_MUSIC_USER_TOKEN and CLI tools (curl, jq, python3). The scripts and docs focus on Apple Music catalog and /me endpoints, which align with the claimed capabilities (taste profiling, reading library, creating playlists).

Instruction Scope

SKILL.md and referenced docs instruct the agent to use local scripts to call official Apple Music APIs, read user data via the /v1/me/* endpoints, cache profiles under ~/.apple-music-dj, and write playlists into the user's library. There are no instructions to read unrelated system files, to call unknown external endpoints, or to exfiltrate data to third parties.

Install Mechanism

No install spec is provided (instruction-only from the registry perspective), and the repository contains local scripts the skill will run. That lowers supply-chain risk (no external archive download). The only external network use is to api.music.apple.com as expected. The project does require PyJWT/cryptography only for the helper token generator (documented as optional).

Credentials

Only two environment variables are required: APPLE_MUSIC_DEV_TOKEN and APPLE_MUSIC_USER_TOKEN, which are necessary for MusicKit API access. No unrelated credentials or broad secrets are requested. The skill writes caches to ~/.apple-music-dj; changelog and code indicate restrictive file permissions (0o600).

Persistence & Privilege

always:false (no forced permanent inclusion). Metadata lists network and filesystem permissions which are appropriate for an API-driven playlist tool that reads and writes to the user's library and stores local caches. The skill does not request elevated system-wide privileges or access to other skills' configs.

Assessment

This skill appears internally consistent with its Apple Music purpose, but it will read your Apple Music account data and create playlists (it needs your developer and user tokens). Before installing: 1) Verify the upstream source (review the GitHub repo/author) because the registry 'Source: unknown' reduces provenance visibility. 2) Inspect scripts/apple_music_api.sh and scripts/* for any network calls beyond api.music.apple.com if you want extra assurance. 3) Keep your APPLE_MUSIC_DEV_TOKEN and APPLE_MUSIC_USER_TOKEN secret — do not paste them into public places; generate the dev token using your own Apple Developer account and the provided generate_dev_token.py (requires PyJWT/cryptography). 4) Note cache and config live in ~/.apple-music-dj/ (code sets restrictive permissions per changelog) — if you share the machine, clear or rotate tokens. 5) Because the skill can be invoked autonomously by the agent (disable-model-invocation is false by default), consider whether you want to allow automated runs that can modify your library; you can deny autonomous invocation in your agent settings if you prefer. If you want additional assurance, run the scripts locally in a controlled environment (or review the script contents) before giving the skill your tokens.

An intelligent Apple Music personalization engine for OpenClaw. Reads your listening history, Replay stats, ratings, and library to build a deep taste profile — then generates playlists using five strategies, surfaces insights about your listening, and writes everything directly to your Apple Music library. Also includes shareable Taste DNA Cards, compatibility scoring, catalog gap analysis, album deep dives, artist rabbit holes, daily song drops, concert prep playlists, and personalized new release radar.

Architecture

User Request
     │
     ▼
┌─────────────┐    ┌──────────────────────────────────────────────┐
│  Taste       │◄──│  Apple Music API (read)                      │
│  Profiler    │    │  · recently played    · library songs/artists│
│  (cached)    │    │  · heavy rotation     · ratings (loved/hated)│
│              │    │  · recommendations    · Replay summaries     │
└──────┬───────┘    └──────────────────────────────────────────────┘
       │
       ▼
┌─────────────┐    ┌──────────────────────────────────────────────┐
│  Strategy    │───▶│  Apple Music API (catalog)                   │
│  Engine      │    │  · search · charts · artist albums/top songs │
│              │    │  · genres · new releases                     │
└──────┬───────┘    └──────────────────────────────────────────────┘
       │
       ▼
┌─────────────┐    ┌──────────────────────────────────────────────┐
│  Playlist    │───▶│  Apple Music API (write)                     │
│  Builder     │    │  · POST /me/library/playlists                │
│              │    │  · POST /me/library/playlists/{id}/tracks     │
└─────────────┘    └──────────────────────────────────────────────┘

Prerequisites

Two environment variables must be set. If the user doesn't have them, walk them through references/auth-setup.md before doing anything else.

Variable	Purpose
APPLE_MUSIC_DEV_TOKEN	JWT signed with MusicKit private key (Apple Developer portal)
APPLE_MUSIC_USER_TOKEN	Per-user authorization token (obtained via MusicKit JS authorize())

Verify with: scripts/apple_music_api.sh verify

Taste Profiling

Before generating any playlist, build (or load from cache) a taste profile.

Run: python3 scripts/taste_profiler.py [--cache ~/.apple-music-dj/taste_profile.json] [--max-age 0] [--storefront us]

Use --max-age 0 to force a refresh, bypassing any cached profile.

The profiler pulls from all available data sources:

Source	Endpoint	Signal
Loved/disliked songs	GET /v1/me/ratings/songs	★★★★★ Explicit intent
Heavy rotation	GET /v1/me/history/heavy-rotation	★★★★☆ Repeated engagement
Recently played	GET /v1/me/recent/played/tracks	★★★★☆ Current mood
Replay summaries	GET /v1/me/music-summaries	★★★★☆ Annual deep data
Library songs	GET /v1/me/library/songs?include=catalog	★★★☆☆ Broad taste
Library artists	GET /v1/me/library/artists	★★★☆☆ Artist affinity
Recommendations	GET /v1/me/recommendations	★★☆☆☆ Apple's inference

Output: Taste DNA profile cached at ~/.apple-music-dj/taste_profile.json (7-day TTL by default).

The profile includes: top artists (weighted), genre distribution, era profile, energy classification, variety score, loved/disliked IDs, library song IDs, and Replay highlights.

Sparse Data Handling

If the user has limited history:

1. Check Replay data — covers a full year even if recent history is short
2. Lean on /me/recommendations
3. Ask user to name 3–5 artists/songs they love as manual seeds
4. Use catalog search with those seeds to bootstrap

Playlist Strategies

Five strategies. Read detailed algorithms in references/playlist-strategies.md.

Strategy 1: Deep Cuts Explorer

Triggers: "deep cuts", "hidden gems", "B-sides", "underrated", "album tracks" Find tracks from artists the user loves but hasn't heard yet. Excludes top songs, singles, and anything in library.

Strategy 2: Mood / Activity Matcher

Triggers: "workout", "chill", "focus", "party", "sleep", "cooking", "driving", "running", "morning", "sad", "study", "dinner" — any mood/activity word. Maps mood to musical attributes, filters through user's taste.

Strategy 3: Trend Radar

Triggers: "trending", "what's hot", "charts", "popular", "new releases" Current charts filtered through user's genre preferences. Includes wildcard picks.

Strategy 4: Constellation Discovery

Triggers: "surprise me", "something new", "expand my taste", "I'm bored", "discovery" Journey from familiar artists outward to new territory in 3 "rings" of distance.

Strategy 5: Playlist Refresh / Evolution

Triggers: "refresh my [playlist]", "update my [playlist]", "evolve", "getting stale" Analyzes existing playlist's sonic signature, adds fresh matching tracks, optionally prunes overplayed songs.

Engagement Features

Beyond playlist strategies, these features drive daily engagement and deeper music discovery.

Taste DNA Card

Triggers: "taste card", "taste DNA", "share my taste", "my music identity", "listener profile" Generate a shareable visual card (SVG or text) summarizing the user's taste: archetype label (e.g., "Deep Catalog Digger", "Genre Drifter"), top genres with bars, top artists, era mix, and stats (energy, variety, mainstream, velocity).

Run: python3 scripts/taste_card.py <profile.json> [--format svg|text] [--output card.svg]

Archetype detection is automatic based on profile data. Always present the text version in chat; offer to generate the SVG for sharing. The card is designed to be screenshot-friendly.

Compatibility Score

Triggers: "how compatible am I with [artist]", "compatibility", "do I match [artist]", "compare my taste with", "would I like [artist]" Score how well the user's taste aligns with an artist's catalog DNA (0-100%). Also supports comparing two user profiles if both have run the taste profiler.

Run:

• python3 scripts/compatibility.py artist <profile.json> <storefront> <artist_name>
• python3 scripts/compatibility.py profile <profile1.json> <profile2.json>

Present the percentage, a verdict ("Deeply compatible", "Strong overlap", "Wild card"), matching genres, and whether the artist is already in their library.

Listening Insights

Triggers: "listening timeline", "how has my taste changed", "listening streaks", "milestones", "year in review", "my [year] in music", "wrapped", "listening stats"

Three subcommands:

Timeline: python3 scripts/listening_insights.py timeline <profile.json> Shows taste evolution across Replay years — top artist, genre, and listen time per year. Present as a narrative: "In 2022 you were deep into shoegaze, by 2024 you shifted to jazz."

Streaks: python3 scripts/listening_insights.py streaks <profile.json> Surfaces milestones (from Replay API) and computed insights: artist loyalty, genre dominance, discovery rate, library size, rating activity.

Year in Review: python3 scripts/listening_insights.py year-review <profile.json> [--year 2025] Comprehensive year analysis — deeper than Apple Replay. Includes obscurity score, variety analysis, top songs/artists/albums, total listen time, and narrative insights.

Catalog Gap Analysis

Triggers: "what have I missed", "what am I missing from [artist]", "catalog gaps", "albums I haven't heard", "discography check" Scans the user's favorite artists' full discographies and identifies albums with zero tracks in the user's library. Ranks recommendations by release date.

Run: python3 scripts/catalog_explorer.py gap-analysis <profile.json> <storefront>

Present as: "You love Radiohead but you've only heard 3 of their 9 albums. Here's what you're missing, ranked by how much you'd probably like each one."

Album Deep Dive

Triggers: "tell me about [album]", "album deep dive", "what's on [album]", "should I listen to [album]", "break down [album]" Pull track listing, classify each track as single vs deep cut, show where the album sits in the artist's discography, and recommend which deep cuts to try first.

Run: python3 scripts/catalog_explorer.py album-dive <storefront> <album_name> [--artist <name>]

Artist Rabbit Hole

Triggers: "rabbit hole from [artist]", "take me on a journey from [artist]", "artist chain", "explore from [artist]", "where does [artist] lead" Interactive chain exploration. Starts from one artist, searches for adjacent artists, maps outward step by step. Each step tagged as familiar/adjacent/frontier zone. Outputs tracks from each artist for a potential journey playlist.

Run: python3 scripts/catalog_explorer.py rabbit-hole <profile.json> <storefront> <artist_name> [--depth 4]

Present each step with the artist name, zone, genres, and sample tracks. Ask if the user wants to go deeper or turn the exploration into a playlist.

Daily Song Drop

Triggers: "daily song", "song of the day", "give me one track", "daily pick" One track per day with a one-sentence rationale. Uses a date-based seed for consistency (same pick all day). Draws from deep cuts and trending tracks scored against the user's taste.

Run: python3 scripts/daily_pick.py daily <profile.json> <storefront>

What Should I Listen To Right Now?

Triggers: "what should I listen to", "what should I play", "recommend something now", "I need music", "play something" Context-aware instant recommendation. Factors in time of day (morning → gentle indie, late night → ambient), maps to genre boosts, and scores candidates accordingly.

Run: python3 scripts/daily_pick.py now <profile.json> <storefront>

Concert Prep Playlist

Triggers: "seeing [artist] live", "concert prep", "going to [artist] show", "setlist for [artist]", "get ready for [artist] concert" Builds a prep playlist: top songs (likely setlist material) + deep cuts to learn before the show. Creates directly in Apple Music library.

Run: scripts/concert_prep.sh <storefront> <artist_name> [playlist_name]

Personalized New Release Radar

Triggers: "new releases", "what's new from my artists", "any new music", "release radar", "new albums this week" Scans the user's top 20 artists for releases in the last 7 days, plus searches for genre-relevant new releases from adjacent artists. Can optionally create a playlist.

Run: scripts/new_releases.sh <profile.json> <storefront> [--create-playlist]

Quick Commands

User says	Action
"Analyze my taste"	Run taste profiler, present Taste DNA report
"Show my taste card"	Generate and display Taste DNA Card
"Make me a playlist"	Ask what kind, then select strategy
"Surprise me"	Constellation Discovery — no questions asked
"More like [artist]"	Deep Cuts + Constellation hybrid seeded from named artist
"Refresh my workout playlist"	Strategy 5 on the named playlist
"What have I been into?"	Present recent listening summary
"How compatible am I with [artist]?"	Run compatibility score
"What have I missed from [artist]?"	Catalog gap analysis
"Tell me about [album]"	Album deep dive
"Take me on a rabbit hole from [artist]"	Artist chain exploration
"Give me one song"	Daily song drop
"What should I listen to right now?"	Context-aware instant pick
"I'm seeing [artist] next week"	Concert prep playlist
"Any new releases for me?"	Personalized new release scan
"My year in review"	Year in review (previous year)
"Set up weekly playlists"	Configure cron (see below)

Cron / Automated Playlists

OpenClaw has first-class cron and heartbeat support. This skill can automate:

Weekly Mix (recommended default):

Schedule: Every Sunday at 9:00 AM (user's local time)
Task:
  1. Refresh taste profile cache
  2. Run Trend Radar (15 tracks)
  3. Run Constellation Discovery (10 tracks)
  4. Merge into "Weekly Mix · {date}" playlist
  5. Create in library
  6. Notify user: "🎧 Your weekly mix is ready — 25 fresh tracks"

New Release Watch:

Schedule: Daily at 8:00 AM
Task:
  1. scripts/new_releases.sh <profile> <storefront>
  2. If found: notify user with artist + album/single name
  3. Optionally: --create-playlist to auto-create a playlist

Daily Song Drop:

Schedule: Daily at 9:00 AM
Task:
  1. python3 scripts/daily_pick.py daily <profile> <storefront>
  2. Notify user: "🎧 Today's pick: {song} by {artist} — {reason}"

Playlist Health Check:

Schedule: Weekly (Saturday)
Task:
  1. Scan user's playlists
  2. Flag any tracks removed from Apple Music catalog (404 on lookup)
  3. Suggest replacements

When the user asks to set up automation, configure via OpenClaw's cron system and confirm the schedule with them before activating.

Assembly Rules

After any strategy produces candidate tracks, apply before creating:

Sequencing:

1. Tracks 1–3: Open strong (high-confidence picks)
2. Tracks 4–8: Build energy, introduce variety
3. Tracks 9–15: Peak — best discoveries here
4. Tracks 16–22: Sustain interest, new textures
5. Tracks 23–28: Wind down
6. Final track: Memorable closer, not filler

Hard rules:

• Min 5 tracks between same-artist repeats
• Max 2 tracks from same album
• No tracks from disliked artists
• No explicit content unless user's library already has it
• All IDs must be catalog IDs (not library IDs)
• Target: 25–40 tracks unless user specifies otherwise

Naming: {Strategy} · {Context} · {Mon YYYY}

Storefront Detection

Catalog endpoints need a storefront code (us, gb, jp...). Detection order:

1. APPLE_MUSIC_STOREFRONT env var → use it
2. GET /v1/me/storefront → auto-detect and cache
3. Fall back to us, warn user

Error Handling

Code	Meaning	Action
401	Dev token invalid/expired	Regenerate JWT. See references/auth-setup.md
403	User token expired (~6mo)	Re-authorize via browser. No refresh flow exists
404	Resource removed from catalog	Skip, log warning, continue
429	Rate limited	Exponential backoff: 1s→2s→4s→8s, max 3 retries
Empty data	New user or privacy settings	Fall back: Replay → recs → manual seeds

Presenting Results

After creating a playlist:

1. ✅ Confirmation with playlist name
2. Brief explanation connecting the curation to their taste
3. Track listing (# · Song · Artist)
4. Offer refinement: "Want me to adjust the energy, swap tracks, or make it longer?"

Tone: Music-savvy friend. Be specific about why tracks were chosen. Say "I noticed you've been deep into shoegaze lately, so I pulled Slowdive deep cuts and some bands in that orbit" — not "Here's an awesome playlist just for you!"

Reference Files

File	Read when...
references/auth-setup.md	User needs token setup help
references/api-reference.md	Need endpoint details, formats, genre IDs
references/playlist-strategies.md	Before executing any playlist strategy
references/troubleshooting.md	User hits errors, things aren't working

Privacy & Data Disclosure

This skill accesses user data through the Apple Music API. Here's exactly what it reads, stores, and transmits.

Data read (via Apple Music API):

Data	Endpoint	Purpose
Recently played tracks	/v1/me/recent/played/tracks	Current taste signal
Heavy rotation	/v1/me/history/heavy-rotation	Repeated listening signal
Library songs & artists	/v1/me/library/songs, /artists	Broad taste mapping
Ratings (loved/disliked)	/v1/me/ratings/songs	Explicit preferences
Recommendations	/v1/me/recommendations	Apple's inference data
Replay summaries	/v1/me/music-summaries	Annual listening stats
Storefront	/v1/me/storefront	Region detection

Data stored locally:

File	Contents	Location
Taste profile cache	Aggregated taste data (JSON)	~/.apple-music-dj/taste_profile.json
Taste DNA Card	Generated SVG image	User-specified output path

Data NOT collected:

• No data is sent to any third-party service
• No analytics or telemetry
• No data leaves the user's machine (all API calls go directly to Apple)
• Tokens are never logged or stored by the skill (env vars only)

Cache management:

• Taste profile cache has a 7-day TTL by default
• Cache can be cleared: rm -rf ~/.apple-music-dj/
• Use --max-age 0 to bypass cache and fetch fresh data

Scripts

Script	Lang	Purpose
scripts/_common.py	Python	Shared utilities (API calls, profile loading, search)
scripts/apple_music_api.sh	Bash	API wrapper — all endpoints, retry logic
scripts/taste_profiler.py	Python	Taste DNA profiler with caching
scripts/build_playlist.sh	Bash	Playlist creation & refresh (create or add tracks)
scripts/generate_dev_token.py	Python	Developer JWT generator
scripts/taste_card.py	Python	Shareable Taste DNA Card (SVG/text)
scripts/compatibility.py	Python	Taste compatibility scoring (artist or profile)
scripts/listening_insights.py	Python	Timeline, streaks, milestones, year in review
scripts/catalog_explorer.py	Python	Gap analysis, album deep dive, artist rabbit hole
scripts/daily_pick.py	Python	Daily song drop & instant recommendation
scripts/playlist_history.py	Python	Playlist creation history tracking
scripts/concert_prep.sh	Bash	Concert prep playlist builder
scripts/new_releases.sh	Bash	Personalized new release radar
scripts/verify_setup.sh	Bash	Setup verification (prereqs, tokens, API)

Example Interactions

"I've been in a funk, make me something uplifting" → Strategy 2 (Mood). Target: uplifting energy from their preferred genres.

"I keep listening to the same 20 songs" → Strategy 4 (Constellation). Start from heavy rotation, expand outward.

"Make me a running playlist" → Strategy 2 (Activity). 140–180 BPM feel, high energy, filtered through taste.

"What's trending that I'd actually like?" → Strategy 3 (Trend Radar). Charts filtered through taste fingerprint + wildcards.

"I love Radiohead and Björk but I've heard everything" → Strategy 1 (Deep Cuts) + Strategy 4 (Constellation) combined.

"My gym playlist is getting stale" → Strategy 5 (Refresh). Analyze vibe, add fresh tracks, prune overplayed ones.

"Set me up with weekly auto-playlists" → Cron config. Confirm schedule, set up weekly Trend Radar + Constellation mix.

"Show me my taste card" → Generate Taste DNA Card. Present text version in chat, offer SVG export.

"How compatible am I with Tyler, the Creator?" → Compatibility score. Show percentage, verdict, matching genres.

"What albums am I missing from Radiohead?" → Catalog gap analysis. List unheard albums, recommend starting points.

"Tell me about In Rainbows" → Album deep dive. Track listing, deep cuts vs singles, discography context.

"Take me on a rabbit hole from Radiohead" → Artist chain: Radiohead → Talk Talk → Bark Psychosis → Disco Inferno. Offer playlist.

"Give me one song for today" → Daily song drop. One track, one reason, done.

"What should I listen to right now?" → Context-aware pick. Time of day + taste → instant recommendation.

"I'm seeing Phoebe Bridgers next month" → Concert prep playlist. Top songs + deep cuts, created in Apple Music.

"Any new releases from my artists?" → New release radar. Scan top artists + genre discoveries, report findings.

"How was my 2025 in music?" → Year in review. Listen time, top artists, obscurity score, narrative insights.