According to AlphaMoat, as of 2026-09, Music has 4,107 downloads and 5 stars — ranked #2,462 of 5,078 AI skills overall, and #94 of 246 in Life Service.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 116 | 3.5K | 4 | - |
| SkillHub ↗ | 116 | 4.1K | 5 | — |
Platforms count differently and update at different times.
It covers discovery queues, song and album favorites, context-based playlists, concert records, and music memories. Notes may be stored in local files; review personal preferences and comments before sharing or syncing them.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.1K | — | 5 |
| 2026-08 | 3.6K | +6.0% | 5 |
| 2026-07 | 3.4K | +11.3% | 5 |
| 2026-06 | 3.1K | +8.1% | 4 |
| 2026-05 | 2.8K | — | 4 |
| 2026-03 | 2.0K | — | 4 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
---
name: Music
description: Build a personal music system for tracking discoveries, favorites, concerts, and listening memories.
metadata: {"clawdbot":{"emoji":"🎵","os":["linux","darwin","win32"]}}
---
## Core Behavior
- User shares song/album → offer to save with context
- User asks for music → check their saved collection first
- User mentions concert → track in events- Create `~/music/` as workspace
## File Structure
```
~/music/
├── discover/
│ └── to-listen.md
├── favorites/
│ ├── songs.md
│ ├── albums.md
│ └── artists.md
├── playlists/
│ ├── workout.md
│ ├── focus.md
│ └── road-trip.md
├── concerts/
│ ├── upcoming.md
│ └── attended/
├── collection/
│ └── vinyl.md
└── memories/
└── 2024.md
```
## Discovery Queue
```markdown
# to-listen.md
## Albums
- Blonde — Frank Ocean (recommended by Jake)
- Kid A — Radiohead (classic I never explored)
## Artists to Explore
- Japanese Breakfast — heard one song, dig deeper
- Khruangbin — background music recs
```
## Favorites Tracking
```markdown
# songs.md
## All-Time
- Purple Rain — Prince
- Pyramids — Frank Ocean
- Paranoid Android — Radiohead
## Current Rotation
- [updates frequently]
# albums.md
## Perfect Front to Back
- Abbey Road — The Beatles
- Channel Orange — Frank Ocean
- In Rainbows — Radiohead
```
## Playlists by Context
```markdown
# focus.md
## For Deep Work
- Brian Eno — Ambient 1
- Tycho — Dive
- Bonobo — Black Sands
## Why These Work
Instrumental, steady tempo, no lyrics distraction
```
## Concert Tracking
```markdown
# upcoming.md
- Khruangbin — May 15, Red Rocks — tickets bought
- Tame Impala — TBD, watching for dates
# attended/radiohead-2018.md
## Date
July 2018, Madison Square Garden
## Highlights
- Everything in Its Right Place opener
- Idioteque crowd energy
## Notes
Best live show ever, would see again anywhere
```
## Physical Collection
```markdown
# vinyl.md
## Own
- Dark Side of the Moon — Pink Floyd
- Rumours — Fleetwood Mac
## Want
- Kind of Blue — Miles Davis
- Vespertine — Björk
```
## Music Memories
```markdown
# 2024.md
## Summer Soundtrack
- Brat — Charli XCX
- GNX — Kendrick
## Discovery of the Year
Japanese Breakfast — finally clicked
```
## By Mood/Activity
- Workout: high energy, tempo 120+
- Focus: instrumental, ambient, lo-fi
- Cooking: upbeat, familiar favorites
- Sad hours: cathartic, emotional
- Party: crowd-pleasers, danceable
- Road trip: singalongs, classics
## What To Surface
- "You saved that album 3 months ago, still unlistened"
- "Artist you like is touring near you"
- "Last time you needed focus music you liked Tycho"
- "This sounds like artists in your favorites"
## Artist Deep Dives
When user discovers artist they love:
- Map discography chronologically
- Note fan-favorite albums
- Flag essential tracks for sampling
- Track which albums explored vs pending
## What To Track Per Entry
- Song/album/artist name
- How discovered (who, where, when)
- Context (mood it fits, activity)
- Rating after listening
- Standout tracks on albums
## Progressive Enhancement
- Week 1: list current favorite songs/albums
- Ongoing: save discoveries with source
- Build mood-based playlists over time
- Log concerts attended
## What NOT To Do
- Assume streaming platform integration
- Push genres they don't enjoy
- Over-organize — simple lists work
- Forget to ask what they're in the mood for
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.