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Agent Skill · 音频生成

music-discovery-guide(音乐发现指南)

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。它适合让 Agent 生成配乐说明、整理音频流程、调用语音工具或处理播客和视频配音素材。使用时需要确认输入音频来源、输出格式、时长和模型限制;涉及人声克隆、版权音乐或公开发布时,应先核对授权和合规边界。

music-discovery-guide 是音频生成类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在音频生成类的 283 个 Skill 里排第 103。

总安装 4,137周安装 169GitHub Stars 公开资料未说明下载 1,325核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:music-discovery-guide(音乐发现指南)
来源仓库:https://github.com/tetsuakira-vk/music-discovery-guide
安装命令:
openclaw skills install music-discovery-guide
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install music-discovery-guide

这个 Skill 是做什么的

音频生成类 Skill 处理配音、配乐和音频素材的生成与转换。

music-discovery-guide 根据心情、流派或艺术家生成个性化音乐推荐。支持主流与利基艺术家探索,扩展音乐视野。适用于日常聆听与主题活动配乐准备。music-discovery-guide 属于音频生成类 Skill,可作为该场景下的辅助能力补充。

安装于 OpenClaw,需确认是否触发外部音乐平台 API 调用。建议在使用前核对推荐来源与地域可用性限制。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install music-discovery-guide;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

music-discovery-guide 怎么安装?

在终端执行 openclaw skills install music-discovery-guide。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

music-discovery-guide 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

music-discovery-guide 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

music-discovery-guide 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

music-discovery-guide 用的人多吗?

公开数据是累计安装 4,137 次,近一周安装 169 次,下载 1,325 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

music-discovery-guide 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理音频生成类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

音频生成类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
music-cog

音乐齿轮 · OpenClaw

121,1044需要联网MIT-0通过
elevenlabs-music

十一实验室音乐 · OpenClaw

108,6181接触敏感数据MIT-0通过
music-generation

音乐一代 · OpenClaw

100,1385需要联网MIT-0有提醒
ace-music

王牌音乐 · OpenClaw

95,8682接触敏感数据MIT-0有提醒
mcp-applemusic

MCP applemusic 音频 · OpenClaw

79,3893调用外部服务MIT-0有提醒
music-discovery-guide

音乐发现指南 · 本页

4,137—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

适合场景

01

生成背景音乐

02

生成歌曲或旋律

03

视频和播客配乐

04

社媒内容音频素材

能力概览

能力 1

调用音乐生成模型

能力 2

支持文本到音乐或歌曲生成

能力 3

提供 CLI 示例和使用场景

能力 4

适合音频内容工作流

平台分布

OpenClaw

74.54%
按下载量换算988

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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