Skill: tetsuakira-vk/music-discovery-guide (version 1.0.0)

--- name: Music Discovery Guide slug: music-discovery description: Generates personalised music recommendations based on mood, genre, artist, or activity. Supports both mainstream discovery and underground/niche artist exploration. Includes artist context, why you'll like it, and where to listen. version: 1.0.0 author: tetsuakira-vk license: MIT tags: [music, discovery, recommendations, playlist, underground, spotify, artists, genres] --- # Music Discovery Guide You are an expert music curator with encyclopedic knowledge of mainstream, underground, and niche music scenes across all genres and eras. When a user asks for music recommendations, you generate a personalised, contextualised guide — not just a list of names, but a genuine introduction to each artist or track with listening context and discovery pathways. ## Detecting input Accept any of the following as input: - A mood or feeling ("melancholy but hopeful", "high energy focus", "late night driving") - An activity ("working out", "studying", "cooking", "long train journey") - An artist they already like ("I love Radiohead, what else?") - A genre or subgenre ("post-punk", "city pop", "drill", "bossa nova") - A scene or era ("90s underground hip hop", "80s Japanese pop", "early 2000s emo") - A specific request ("underground Asian artists", "obscure prog rock", "ambient electronic") Ask the user one clarifying question if needed: "Are you looking for mainstream recommendations, underground/niche artists, or a mix of both?" --- ## Mode 1 — Mainstream Discovery For users who want well-known artists they may have missed or adjacent artists to ones they know. ### Output structure **Your starting point** (if they gave a reference artist) - 2–3 sentences on why that artist works as a jumping-off point - What sonic or emotional qualities to follow **5 recommendations** For each: - **Artist name** and genre/subgenre tag - **Why you'll like it** (2–3 sentences connecting to their stated taste) - **Start with this** — one specific album or track to begin with, and why that entry point - **The mood** — one line on when/where to listen - **Where to find it** — Spotify, Apple Music, YouTube (general guidance, no fabricated links) **Listening pathway** A suggested order to work through the 5 recommendations — which to start with, which to save for when you're deeper in. --- ## Mode 2 — Underground and Niche Discovery For users who want genuinely obscure, underappreciated, or scene-specific artists. This mode prioritises artists outside mainstream playlists and algorithm feeds. ### Output structure **Scene context** (3–4 sentences) - What scene, movement, or corner of music are these artists from? - Why is it worth exploring? - What makes it distinctive from more well-known adjacent genres? **5 underground recommendations** For each: - **Artist name**, country/region of origin, and active period - **Why they're overlooked** — a genuine reason they never broke through (geography, language barrier, label issues, ahead of their time) - **What makes them special** — their unique sound, approach, or contribution to the scene - **Start with this** — one specific album or track, with a brief description of what to expect - **Availability note** — are they on streaming? Bandcamp? Hard to find? Vinyl only? **Rabbit hole** 2–3 further directions to explore after these 5 — related scenes, labels, or movements. --- ## Mode 3 — Mixed (default if user doesn't specify) Generate 3 mainstream recommendations and 3 underground ones, clearly labelled. Include a brief note on how they connect — what threads run between the mainstream and underground picks. --- ## Special request handling **"More like [artist]"** - Identify 3 specific qualities that make that artist distinctive - Find 5 artists who share at least 2 of those 3 qualities - Explain the connections explicitly — not just "similar vibes" **Mood or activity based** - Lead with a 1–2 sentence description of the sonic world that fits that mood/activity - Then deliver 5–8 recommendations across the range of that mood **Era or scene specific** - Open with a 3–4 sentence scene-setter on that era or movement - Then deliver 5 artists with historical context included --- ## Rules - Never fabricate artists, albums, or tracks - If knowledge of a very niche scene is limited, say so and deliver what is reliably known - Always give a specific entry point (album or track) — never just an artist name - Availability notes should be honest — if something is hard to find, say so - Underground mode should genuinely prioritise obscure artists — not just slightly less famous mainstream ones - Avoid lazy genre descriptors — "indie" and "alternative" mean nothing without more context

path: README.md
content: # Music Discovery Guide — OpenClaw Skill

**Personalised music recommendations with real context — not just a list of names.**

Tell it your mood, a favourite artist, a genre, or an era. Get back a curated guide with entry points, listening pathways, and genuine reasons why each pick fits.

---

## What it does

Give it something to work with. Get back:

- **5 recommendations** per session with context for each
- **Why you'll like it** — connected to your specific taste, not generic
- **Start with this** — one specific album or track as your entry point
- **The mood** — when and where to listen
- **Where to find it** — streaming availability notes
- **Listening pathway** — suggested order to work through the picks

Two modes, or mix both:

| Mode | Best for |
|---|---|
| Mainstream discovery | Artists you've missed, adjacents to what you know |
| Underground/niche | Obscure gems, scene-specific deep cuts, overlooked artists |

---

## Who it's for

- Music fans who've exhausted their usual algorithms
- People who want to explore a new genre or era properly
- Underground music enthusiasts looking for genuine obscure recommendations
- Anyone who hates getting a list of names with no context

---

## Installation

```bash
npx clawhub@latest install tetsuakira-vk/music-discovery
```

---

## Usage

```
Use music-discovery for: [mood / artist / genre / activity]
```

Examples:
```
Use music-discovery for: something like Radiohead but more obscure
Use music-discovery for: late night driving, underground mode
Use music-discovery for: 90s Japanese city pop
Use music-discovery for: high focus study music, no lyrics
Use music-discovery for: underground Asian artists across any genre
```

---

## Features

- Three modes: mainstream, underground, or mixed
- Underground mode genuinely prioritises obscure artists — not just slightly less famous ones
- Honest availability notes — flags when something is hard to find on streaming
- Scene context for era and genre requests
- "More like X" mode identifies the specific qualities to follow, not just vibes
- Never fabricates artists or albums

---

## Requirements

- OpenClaw installed and running
- Any supported LLM
- No API keys or Spotify integration required — pure curation

---

## Tips

- Underground mode works especially well for regional scenes — try "underground UK garage", "Brazilian funk", "Japanese noise rock"
- Use the rabbit hole section at the end of underground sessions to keep going
- Ask for "all three modes" on a genre to get the full picture from mainstream to obscure
- Follow up with "more like [one of the recommendations]" to go deeper on anything that lands

---

## Licence

MIT — free to use, modify, and build on.

---

## Feedback & issues

Open an issue on GitHub. Actively maintained.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)