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Agent Skill · 音频生成

skywork-music-maker(天空音乐制作人)

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。

skywork-music-maker 是音频生成类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,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 158。

总安装 2,736周安装 114GitHub Stars 124下载 912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
unknown

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:skywork-music-maker(天空音乐制作人)
来源仓库:https://github.com/skyworkai/skywork-skills
仓库路径:skills/skywork-music-maker
安装命令:
npx skills add https://github.com/skyworkai/skywork-skills --skill skywork-music-maker
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/skyworkai/skywork-skills --skill skywork-music-maker

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。

skywork-music-maker 用于辅助音频、音乐或语音合成处理。适合让 Agent 生成配乐说明或调用语音工具。使用时需确认输入音频来源、输出格式和模型限制。涉及人声克隆或版权音乐时应先核对授权和合规要求。适用于播客和视频配音素材处理场景。

skywork-music-maker 属于音频生成类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/skywork-music-maker

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/skyworkai/skywork-skills --skill skywork-music…;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skywork-music-maker 怎么安装?

在终端执行 npx skills add https://github.com/skyworkai/skywork-skills --skill skywork-music-maker。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skywork-music-maker 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

skywork-music-maker 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

skywork-music-maker 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

skywork-music-maker 用的人多吗?

公开数据是累计安装 2,736 次,近一周安装 114 次,下载 912 次,GitHub 124 星。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。

skywork-music-maker 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
skywork-music-maker

天空音乐制作人 · 本页

2,736124接触敏感数据未明确通过

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

Skywork Music Maker (Mureka API)

Generate professional-quality music using the Mureka API at https://api.mureka.ai. This skill covers the complete music production workflow: lyrics writing → song/instrumental generation.

First-Time Setup

Before running any API command, check if MUREKA_API_KEY is set. If not, guide the user to get an API key at https://platform.mureka.ai/ (register → API Keys → generate key → export MUREKA_API_KEY="..."), then STOP — do not attempt any API calls until the key is configured.


Smart Prompt Conversion (CRITICAL WORKFLOW)

Default behavior: When the user doesn't specify song type, always generate a song with lyrics (use mureka.py song). Only use mureka.py instrumental when the user explicitly asks for instrumental, BGM, background music, or "no vocals".

Output defaults: Use mp3 format unless the user requests otherwise. The --output flag specifies a directory — the script creates it and saves all results inside (audio files + lyrics.txt for songs). If the user doesn't specify a location, choose a user-friendly path with a descriptive folder name based on the song theme (e.g., summer_pop_song/).

When users provide music descriptions in natural language (in any language), you MUST convert them to structured Mureka API prompts using this workflow:

Conversion Process

User Input Examples:

  • "upbeat pop song, female vocals, guitar, perfect for summer"
  • "sad piano ballad about lost love"
  • "epic orchestral music for a fantasy game"
  • "traditional Chinese music with bamboo flute and zither, misty atmosphere"

Your Task:

  1. Extract structured parameters using the extraction rules below
  2. Validate the prompt meets quality standards (see Quality Checklist)
  3. Present to user for confirmation before generating
  4. Run the generation command with the structured prompt

Parameter Extraction Rules

When users provide natural language music descriptions, directly extract and structure the following parameters:

Required Parameters:

  • genres: music genres including fusion styles (e.g., Pop, Rock, Jazz, Pop Rap Fusion, Alternative Rock, Guofeng)
  • moods: emotional tones (e.g., Happy, Melancholic, Energetic, Nostalgic, Bright)
  • instruments: specific instruments (e.g., Piano, Guitar, Drums, Erhu, Guzheng, Synth Pads, Dizi)
  • rhythms: rhythm characteristics (e.g., 4/4, Slow, Syncopated, Driving, Flowing)
  • vocals: vocal attributes (e.g., Female, Husky, Whispered, Male, Soft, Clear) or "instrumental only"
  • key: musical key if specified (e.g., C Major, A Minor, C# Major)
  • bpm: beats per minute (e.g., 120) or tempo descriptor (e.g., "slow groove", "uptempo")
  • description: concise summary (under 50 words) capturing mood progression, melody, harmony, timbre, texture, dynamics

Extraction Instructions:

  1. Translate non-English terms: Convert ALL non-English musical terms to English while preserving cultural and musical meaning
  2. Preserve specificity: Keep detailed information including specific styles, subgenres, and cultural context (e.g., "Chinese traditional guofeng" not just "Chinese music")
  3. Design dynamic arc: Include mood progression where appropriate (e.g., "sparse opening → building tension → cathartic chorus")
  4. Infer intelligently: Make reasonable assumptions based on genre conventions when parameters are not explicitly stated
  5. English output: Final prompt string MUST be entirely in English

Generate Structured Prompt: Combine all extracted parameters into a comprehensive, natural-flowing description that captures the essence of the user's vision.

Quality Checklist (Validate BEFORE Generation)

Before running the generation command, verify the prompt meets these criteria:

MUST HAVE:

  • Specific genre (NOT "pop song" but "synth-pop, 2020s")
  • BPM or tempo descriptor (e.g., "120 BPM" or "slow groove")
  • 3-5 instruments explicitly named
  • Mood/emotion descriptors (2-3 words)
  • Vocal style (or "instrumental only")
  • Structure tags in lyrics: [Verse], [Chorus], [Bridge], [Outro]

WATCH OUT FOR:

  • Vague terms: "nice", "good", "beautiful" → replace with specific descriptors
  • Contradictions: "slow" + "energetic", "sad" + "uplifting" → pick one direction
  • Too short: <50 chars → add more detail
  • Long lyric lines: >10 words per line → split into shorter lines
  • No dynamic arc: add mood progression (e.g., "sparse → building → full")

AVOID:

  • Command verbs: "create a song" → use descriptions "upbeat pop song"
  • Famous artist names: "sounds like Taylor Swift" → describe qualities instead
  • Unrealistic combos: melody_id cannot combine with other control options

After validation, present the generated prompt to the user for confirmation before proceeding.


Core Workflow: Production Pipeline

1. Conceptualize → User describes in natural language → YOU convert to structured prompt
2. Validate → Check prompt quality against Quality Checklist (see above)
3. Write Lyrics → Use lyrics/generate or write manually
4. Upload References → Optional: reference track, vocal sample, melody
5. Generate → Submit song/instrumental task (async) with validated prompt
6. Evaluate → Listen to all N choices, pick best
7. Iterate → Refine prompt based on what you heard

Critical Steps:

  • Step 1 is mandatory when user provides natural language input (especially non-English)
  • Step 2 validation prevents 80% of common generation failures
  • Step 3: Read references/prompt_guide.md for prompt crafting examples, lyrics structure rules (line length, syllable count, rhyme patterns, hook writing), and iteration best practices
  • Do NOT skip conceptualization — jumping straight to generation without a clear concept is the #1 reason for generic results

Your Role as AI Assistant:

  1. Convert user's natural language → structured Mureka prompt (using Smart Prompt Conversion)
  2. Validate prompt quality → flag issues → suggest fixes
  3. Write or generate lyrics with proper structure
  4. Present prompt to user for confirmation
  5. Execute generation command with validated prompt
  6. Help iterate and refine based on generation results

CLI Tool

All operations go through a single script: scripts/mureka.py

mureka.py song           Generate a song with lyrics and vocals
mureka.py instrumental   Generate an instrumental track
mureka.py lyrics         Generate or extend lyrics
mureka.py upload         Upload reference audio, vocals, melodies

Run python scripts/mureka.py --help for full usage. Note: use -n 2 (single dash) to generate multiple choices, not --n.


Common Scenarios

"I just want background music for my video"

python scripts/mureka.py instrumental \
  --prompt "ambient electronic, calm, 80 BPM, soft pads, no percussion, background music for tech product video" \
  --output ./bg_music

"I want a song but don't have lyrics"

# Step 1: Generate lyrics with proper structure
python scripts/mureka.py lyrics generate "a nostalgic summer love song, bittersweet, looking back at memories"

# Step 2: Copy/refine the output, then generate the song
python scripts/mureka.py song \
  --lyrics "[Verse]\n(paste lyrics here)\n[Chorus]\n(paste chorus here)" \
  --prompt "indie pop, warm, 110 BPM, acoustic guitar, soft drums, male vocal" \
  --output ./summer_song

Advanced Features

Reference-Based Generation

Upload a reference track (must be exactly 30s, mp3/m4a) to guide the style:

python scripts/mureka.py upload my_reference.mp3 --purpose reference
# → File ID: 542321
python scripts/mureka.py song --lyrics "[Verse]\n..." --reference-id 542321 --output ./song

Vocal Cloning

Upload a vocal sample (15-30s, mp3/m4a) to use a specific voice:

python scripts/mureka.py upload my_voice.mp3 --purpose vocal
# → File ID: 789012
python scripts/mureka.py song --lyrics "[Verse]\n..." --vocal-id 789012 --prompt "R&B, smooth, 90 BPM" --output ./song

Control Options & Rules

Song Generation Control Combos

When generating songs, these control options work together:

Combopromptreference_idvocal_idmelody_id
Style only✅
Reference only✅
Voice only✅
Melody only✅
Style + Voice✅✅
Reference + Voice✅✅

Important:

  • melody_id does NOT support any combination — use it alone
  • prompt and reference_id are mutually exclusive — use one or the other

Instrumental Generation Rules

For instrumentals, prompt and instrumental_id are mutually exclusive — use one or the other.

File Upload Requirements

PurposeFormatDurationNotes
referencemp3/m4aexactly 30sExcess trimmed
vocalmp3/m4a15-30sExcess trimmed
melodymp3/m4a/mid5-60sMIDI recommended
instrumentalmp3/m4aexactly 30sFor instrumental reference

Model Selection

Always use mureka-8 — it is the latest and highest quality model.


Error Handling

Scripts raise RuntimeError or requests.HTTPError on failure. Handle common errors:

ErrorCauseAction
401 UnauthorizedInvalid or expired API keyAsk user to verify MUREKA_API_KEY
429 Too Many RequestsRate limit exceededWait 30-60 seconds, then retry
402 / Insufficient balanceAccount balance depletedDirect user to https://platform.mureka.ai to top up
Task ended with status: failedGeneration failed (bad prompt, server error)Check prompt against Quality Checklist, retry
Task ended with status: timeoutedGeneration took too longRetry; if persistent, simplify the prompt or try a different model
ConnectionError / TimeoutNetwork issueRetry after a few seconds

General strategy: Read the error message carefully. If it's a client error (4xx), fix the input. If it's a server error (5xx) or timeout, retry once before escalating to the user.


Troubleshooting Common Issues

ProblemSolution
Task failed or timeouted• Check prompt meets quality checklist• Verify lyrics have structure tags• Retry the generation
Vocals sound rushed• Shorten lyric lines (≤10 words)• Reduce syllables per line
Listed instruments not audible• Verify each instrument named explicitly in prompt• Add more specific descriptors (e.g., "acoustic guitar strumming")
Prompt doesn't match output• Increase specificity (exact genre, BPM, instruments)• Add mood progression ("sparse → full")• Generate n=3 choices
melody_id error• melody_id MUST be used alone• Remove --prompt, --reference-id, --vocal-id
Invalid file_id• File IDs only valid for account that uploaded• Re-upload file if from another session

For parameter help:

python scripts/mureka.py --help
python scripts/mureka.py song --help

Environment

  • API Key: MUREKA_API_KEY environment variable (required)
  • Base URL: https://api.mureka.ai
  • Dependencies: Python 3, requests library
  • Billing: Check balance with curl -H "Authorization: Bearer $MUREKA_API_KEY" https://api.mureka.ai/v1/account/billing

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

Codex

37.19%
按下载量换算339

Claude

30.38%
按下载量换算277

Cursor

19.45%
按下载量换算177

Gemini CLI

9.52%
按下载量换算87

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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