Music Discovery

Recommend music tracks and playlists tailored to mood, activity, BPM, energy, or genre using Spotify and Last.fm data.

Security Scan

The skill claims Spotify/Last.fm-backed music discovery but the packaged code is a local CLI stub that makes no API calls, the documented usage paths and declared dependencies don't match the repo layout, and OAuth/credential needs are referenced but not declared — this mismatch is suspicious and likely a placeholder or incomplete implementation.

Purpose & Capability

The name/description promise Spotify/Last.fm-style discovery, but the included Python tool does not call any external APIs, does not import requests or spotipy, and only records local usage to a data file. Declared capabilities (data-backed discovery using Spotify/Last.fm) are not implemented in the code bundle.

Instruction Scope

SKILL.md instructs installing requests and spotipy and refers to OAuth/rate-limits, but gives no instructions to supply credentials or environment variables. Usage examples point to a non-existent path (docs show scripts/skills/music-discovery/scripts/... while the repo has scripts/music_discovery_tool.py), so following the docs will fail. The instructions are incomplete and inconsistent with the packaged code.

Install Mechanism

There is no install spec (instruction-only skill), which is low-risk from an installer standpoint. The only effect of running the tool is local file writes under the repo's data/ directory; no external downloads or archive extraction are present.

Credentials

The skill declares no required environment variables or primary credential, but the README explicitly references OAuth for Spotify and suggests using real API data. The absence of declared env vars is an inconsistency: if real Spotify integration is intended, credentials would be required but are not specified in the metadata.

Persistence & Privilege

always is false and the skill does not request persistent or elevated privileges. It only writes to a repository-local data file and does not modify system-wide configuration or other skills.

What to consider before installing

This package looks like a placeholder/CLI stub rather than a working Spotify/Last.fm integrator. Before installing or running it: (1) don't provide your Spotify credentials to it until the author documents how they are used and where secrets are stored; (2) verify the code path and usage examples (SKILL.md points to a path that doesn't exist); (3) inspect the Python file locally — it currently only writes a local data file and does not call external APIs; (4) if you expect real API-backed recommendations, request the author to (a) implement/declare API calls, (b) list required env vars (client ID/secret or tokens) in metadata, and (c) fix usage paths; (5) run in a sandbox or limited environment if you want to test. These inconsistencies could be harmless (incomplete skill) but warrant caution.

Music Discovery — Mood, Scene & Playlists

Overview

Helps listeners find tracks and playlists that fit a mood, activity, or taste profile—study, commute, workout, sleep, or “something like this artist.” Use when the user wants personalized picks, scene-based sets, or exploration without manual crate-digging.

Trigger keywords: music recommendation, playlist, mood, BPM, study music, workout, discover similar artists

Prerequisites

pip install requests spotipy

Capabilities

1. Data-backed discovery — Spotify Web API / Last.fm–style metadata (see references/music_discovery_guide.md).
2. Scene-based sets — work, workout, wind-down, commute, focus, party.
3. Vibe matching — BPM, energy, valence/mood tags, genre boundaries.

Commands

Command	Description	Example
recommend	Recommend tracks	python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py recommend [args]
playlist	Build a playlist concept	python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py playlist [args]
mood	Recommend by mood	python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py mood [args]

Usage (from repository root)

python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py recommend --scene office --mood relaxed
python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py playlist --scene workout --bpm 140
python3 scripts/skills/music-discovery/scripts/music_discovery_tool.py mood --feeling happy

Output format (for the agent’s report)

# Music Discovery report

**Generated**: YYYY-MM-DD HH:MM

## Key picks
1. [Track / artist — one-line why]
2. …
3. …

## Snapshot
| Title | Artist | Why it fits |
|-------|--------|---------------|

## Playlist sketch (optional)
- **Theme**: …
- **Tempo / energy**: …
- **Avoid**: …

## Notes
[Ground claims in API or user-stated taste—no invented chart positions.]

References

APIs & libraries

• Spotify Web API
• MusicBrainz API
• Spotipy (Python client)

Patterns & community

• Daily Reddit digest (OpenClaw use case)
• Hacker News — mood-based music ML
• Reddit r/spotify — discussion

Notes

• Prefer real API or user-provided data; do not invent popularity or audio features.
• Mark missing fields as unavailable instead of guessing.
• OAuth and rate limits apply when using Spotify—document when credentials are required.