What does the Music AI skill do?

Generate, remix, extend, edit, and analyze AI music (Mureka). Triggers on: "音乐", "music", "生成音乐", "generate music", "翻唱", "cover", "混音", "remix", "续写", "extend", "纯音乐", "instrumental", "配乐", "soundtrack", "分轨", "stem", "识别歌词", "recognize lyrics", "作曲", "compose", "create a song", "做一首歌".

Why use Music on TypingMind?

Because you install it once and use it with any model. Music is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Music in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marswaveai/skills/tree/main/music. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Music?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Music?

As many as you like. As long as a model supports skills, you can use Music with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Music AI skill free?

Yes. It is published on GitHub by marswaveai under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Music by marswaveai — AI Skill for Any LLM

When to Use

User wants to generate original AI music from a prompt and/or lyrics
User wants to remix / re-create an existing song with new lyrics
User wants a pure instrumental, or a soundtrack scored to an image or video
User wants to extend a song or isolate/generate a single track
User wants to analyze audio — recognize lyrics, describe a song, or split stems
User says "音乐", "music", "生成音乐", "generate music", "翻唱"/"混音"/"remix", "续写"/"extend", "纯音乐"/"instrumental", "配乐"/"soundtrack", "分轨"/"stem", "识别歌词", "作曲", "compose", "create a song", or "做一首歌"

When NOT to Use

User wants text-to-speech reading (use /speech)
User wants a podcast discussion (use /podcast)
User wants an explainer video with narration (use /explainer)
User wants to transcribe spoken audio to text — not song lyrics (use /asr)

Purpose

Full ListenHub music toolkit, powered by the Mureka provider via the listenhub music CLI. Capabilities:

Generation (async — return a task to poll):

generate — text and/or lyrics → a new song. Optional style, title, instrumental, and a cloned --vocal-id.
remix — an existing song + new lyrics → a re-creation. Input is one of an audio file, an audio URL, or a provider song ID.
instrumental — a pure instrumental from a prompt, or guided by a reference audio.
soundtrack — music scored to an image or a video.
track — isolate or generate a single instrument/vocal track from a song.
extend — make a song longer.
cover (deprecated) — older cover flow; prefer remix.

Analysis (sync — return results immediately):

recognize — lyrics with line-level timestamps.
describe — description, tags, genres, instruments.
stem — split a song into separated stems (ZIP download URLs).

Task management: list (recent tasks) and get <taskId> (status/result of one task).

Models for generation commands: auto (default), mureka-7.6, mureka-8, mureka-9, mureka-o2. See references/music-api.md for the full per-command parameter reference.

Hard Constraints

Always read config following shared/config-pattern.md before any interaction
Follow shared/cli-patterns.md for execution modes, error handling, and interaction patterns
Always follow shared/cli-authentication.md for auth checks
Never save files to ~/Downloads/ or .listenhub/ — save artifacts to the current working directory with friendly topic-based names (see shared/config-pattern.md § Artifact Naming)
No speakers involved — music generation does not use speaker selection
File limits (all max 10 MB): audio mp3/m4a (track also accepts wav); image jpg/jpeg/png/webp; video mp4/mov/avi/mkv/webm
All time-range flags are in seconds (--generate-start/--generate-end)
For async generation commands, use a long timeout: run_in_background: true with timeout: 660000 (600s+). Sync commands (recognize, describe, stem) return immediately
cover is deprecated — steer users to remix unless they explicitly ask for cover

Step -1: CLI Auth Check

Follow shared/cli-authentication.md. If the CLI is not installed or the user is not logged in, auto-install and auto-login — never ask the user to run commands manually.

Step 0: Config Setup

Follow shared/config-pattern.md Step 0 (Zero-Question Boot).

If file doesn't exist — silently create with defaults and proceed:

bash
mkdir -p ".listenhub/music"
echo '{"outputMode":"download","language":null}' > ".listenhub/music/config.json"
CONFIG_PATH=".listenhub/music/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Do NOT ask any setup questions. Proceed directly to the Interaction Flow.

If file exists — read config silently and proceed:

bash
CONFIG_PATH=".listenhub/music/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/music/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Setup Flow (user-initiated reconfigure only)

Only run when the user explicitly asks to reconfigure. Display current settings:


当前配置 (music)：
  输出方式：{inline / download / both}
  语言偏好：{zh / en / 未设置}

Then ask:

outputMode: Follow shared/output-mode.md § Setup Flow Question.
Language (optional): "默认语言？"
- "中文 (zh)"
- "English (en)"
- "每次手动选择" → keep null

After collecting answers, save immediately:

bash
NEW_CONFIG=$(echo "$CONFIG" | jq --arg m "$OUTPUT_MODE" '. + {"outputMode": $m}')
if [ "$LANGUAGE" != "null" ]; then
  NEW_CONFIG=$(echo "$NEW_CONFIG" | jq --arg lang "$LANGUAGE" '. + {"language": $lang}')
fi
echo "$NEW_CONFIG" > "$CONFIG_PATH"
CONFIG=$(cat "$CONFIG_PATH")

Interaction Flow

Step 1: Capability

Pick the capability. Skip the question if the user's intent is already clear (e.g., "翻唱"/"混音"/"remix" → remix; "作曲"/"compose"/"做一首歌" → generate; "纯音乐"/"instrumental" → instrumental; "续写"/"extend" → extend; "分轨"/"stem" → stem; "识别歌词" → recognize).


Question: "想做什么？"
Options:
  - "原创 (Generate)" — 用文字 / 歌词生成全新歌曲
  - "混音 (Remix)" — 基于已有歌曲 + 新歌词重新创作
  - "纯音乐 (Instrumental)" — 生成无人声的器乐
  - "配乐 (Soundtrack)" — 为图片或视频配乐
  - "其他" — 续写 / 单轨 / 识别歌词 / 描述 / 分轨

If the user picks "其他", follow up with a second AskUserQuestion listing: 续写 (Extend)、单轨 (Track)、识别歌词 (Recognize)、描述 (Describe)、分轨 (Stem).

get <taskId> and list are not interactive flows — run them directly when the user asks about a task's status.

Step 2: Gather inputs (per capability)

Use the per-capability fields below. Ask for required inputs; offer optional ones. For any audio/image/video file, validate before confirming:

Local path: verify the file exists and the extension matches the allowed list for that command (see Hard Constraints / references/music-api.md).
URL: accept as-is (the CLI validates).
Size: reject local files over 10 MB.

bash
FILE_SIZE=$(stat -f%z "{path}" 2>/dev/null || stat -c%s "{path}" 2>/dev/null)
if [ "$FILE_SIZE" -gt 10485760 ]; then echo "File exceeds 10 MB limit"; fi

generate — --prompt and/or --lyrics (at least one); optional --style, --title, --model, --instrumental, --vocal-id.

remix — exactly one input source: --audio (file) / --audio-url / --provider-song-id; plus --lyrics and --prompt (both required); optional --style, --title, --model.

instrumental — exactly one of --prompt / --reference-audio; optional --title, --model.

soundtrack — exactly one of --image / --video; optional --prompt, --title, --model.

extend — one input source --audio / --provider-song-id; optional --prompt, --model.

recognize / describe / stem — --audio only. stem also takes --model audio-separation-1|audio-separation-2.

For multi-choice fields (model, generate-type, vocal-gender, instrumental yes/no) use the AskUserQuestion tool. Free-text fields (prompt, lyrics, style, title) accept plain text.

Step 3: Confirm

Summarize the capability and every collected parameter, then ask the user to confirm. Examples:

generate:


准备生成音乐：
  能力：原创 (Generate)
  描述：{prompt / 无}
  歌词：{lyrics / 无}
  风格：{style / 自动}
  标题：{title / 自动}
  模型：{model / auto}
  人声：{带人声 / 纯音乐}
  Vocal ID：{vocal-id / 无}
  确认？

remix:


准备混音：
  能力：混音 (Remix)
  原曲：{audio / audio-url / provider-song-id}
  新歌词：{lyrics}
  描述：{prompt}
  风格：{style / 自动}
  标题：{title / 自动}
  模型：{model / auto}
  确认？

For analysis capabilities (recognize / describe / stem) the summary is just the capability + the input audio (+ separation model for stem); these run synchronously, so confirmation can be lightweight.

Wait for explicit confirmation before running any CLI command.

Workflow

Async generation commands

generate, remix, instrumental, soundtrack, track, extend, cover.

Submit (background) with run_in_background: true and timeout: 660000. Always pass --json. Include only the flags the user provided; omit the rest.

generate:

bash
listenhub music generate \
  --prompt "{prompt}" \
  --lyrics "{lyrics}" \
  --model "{model}" \
  --style "{style}" \
  --title "{title}" \
  --instrumental \
  --vocal-id "{vocal-id}" \
  --json

remix:

bash
listenhub music remix \
  --audio "{path}" \
  --lyrics "{lyrics}" \
  --prompt "{prompt}" \
  --style "{style}" \
  --title "{title}" \
  --json

(use exactly one of --audio / --audio-url / --provider-song-id)

instrumental:

bash
listenhub music instrumental \
  --prompt "{prompt}" \
  --model "{model}" \
  --json

(or --reference-audio "{path}" instead of --prompt)

soundtrack:

bash
listenhub music soundtrack \
  --image "{path}" \
  --prompt "{prompt}" \
  --json

(or --video "{path}" instead of --image)

track:

bash
listenhub music track \
  --audio "{path}" \
  --generate-type "Vocals" \
  --lyrics "{lyrics}" \
  --vocal-gender "female" \
  --generate-start 0 --generate-end 30 \
  --json

(or --provider-song-id; --lyrics only when --generate-type Vocals)

extend:

bash
listenhub music extend \
  --audio "{path}" \
  --prompt "{how to continue}" \
  --json

The CLI handles polling internally. Generation can take up to ~10 minutes.

Tell the user the task is submitted and that they'll be notified when it finishes. If they only have a taskId, they can check with listenhub music get <taskId> --json or listenhub music list --json.

When notified of completion, present the result. The CLI JSON is a task object — the song is in tracks[0], credit is creditCost, and duration is in seconds. Parse the key fields:

bash
AUDIO_URL=$(echo "$RESULT" | jq -r '.tracks[0].audioUrl // empty')
TITLE=$(echo "$RESULT" | jq -r '[.tracks[0].title, .params.title, "Untitled"] | map(select(. != null and . != "")) | .[0]')
# duration is seconds (older pre-rollout Mureka tasks may still be ms → a value ≥ 3600 means ms)
DURATION=$(echo "$RESULT" | jq -r '.tracks[0].duration // 0' \
  | awk '{d=$1; if (d>=3600) d/=1000; printf "%d:%02d", int(d/60), int(d%60)}')
CREDITS=$(echo "$RESULT" | jq -r '.creditCost // empty')

Read OUTPUT_MODE from config. Follow shared/output-mode.md for behavior.

inline or both: Display the audio URL as a clickable link.


音乐已生成！

标题：{title}
在线收听：{audioUrl}
时长：{duration}
消耗积分：{credits}

download or both: Also download the file. Generate a slug from the title following shared/config-pattern.md § Artifact Naming.

bash
SLUG="{slug}"  # e.g. "summer-breeze"
NAME="${SLUG}.mp3"
# Dedup: if file exists, append -2, -3, etc.
BASE="${NAME%.*}"; EXT="${NAME##*.}"; i=2
while [ -e "$NAME" ]; do NAME="${BASE}-${i}.${EXT}"; i=$((i+1)); done
curl -sS -o "$NAME" "{audioUrl}"


已保存到当前目录：
  {NAME}

Sync analysis commands

recognize, describe, stem return results in the same call — run them in the foreground (no background, no long timeout) and present immediately.

recognize (lyrics + timestamps):

bash
listenhub music recognize --audio "{path}" --json

describe (description, tags, genres, instruments):

bash
listenhub music describe --audio "{path}" --json

stem (separated stems → ZIP download URLs):

bash
listenhub music stem --audio "{path}" --model "audio-separation-2" --json

In download/both mode, download the ZIP URL(s) promptly to cwd.

Task management

bash
listenhub music list --json            # recent tasks
listenhub music get "{taskId}" --json   # one task's status / result

After Successful Generation

Update config with the language used this session if the user explicitly specified one:

bash
if [ -n "$LANGUAGE" ]; then
  NEW_CONFIG=$(echo "$CONFIG" | jq --arg lang "$LANGUAGE" '. + {"language": $lang}')
  echo "$NEW_CONFIG" > "$CONFIG_PATH"
fi

Estimated times:

Music generation: 5-10 minutes

Resources

Per-command parameter reference: references/music-api.md
CLI authentication: shared/cli-authentication.md
CLI patterns: shared/cli-patterns.md
Config pattern: shared/config-pattern.md
Output mode: shared/output-mode.md

Composability

Invokes: nothing
Invoked by: content-planner (Phase 3)

Examples

Generate original:

"帮我做一首关于夏天海边的歌"

Detect: generate mode ("做一首歌")
Read config (first run: create defaults with outputMode: "download")
Infer: mode = generate, prompt = "夏天海边的歌"
Ask: style? title? instrumental?
Confirm summary → user confirms

bash
listenhub music generate \
  --prompt "关于夏天海边的歌" \
  --json

Wait for CLI to return result, then download {slug}.mp3 to cwd.

Remix an existing song:

"用 demo.mp3 重新填词混音，把它做成 city pop 风格"

Detect: remix capability ("混音")
Validate: demo.mp3 exists, is mp3/m4a, under 10 MB
Ask: new lyrics (--lyrics, required), prompt/direction (--prompt, required), style, title
Confirm summary → user confirms

bash
listenhub music remix \
  --audio "demo.mp3" \
  --lyrics "{new lyrics}" \
  --prompt "rework as upbeat city pop" \
  --style "city pop" \
  --json

Wait for the CLI result, then download {slug}.mp3 to cwd.

Generate instrumental:

"Create an instrumental electronic track for a game intro"

Detect: instrumental capability ("instrumental")
Infer: prompt = "electronic track for a game intro"
Confirm summary → user confirms

bash
listenhub music instrumental \
  --prompt "electronic track for a game intro" \
  --json

Wait for the CLI result, then download {slug}.mp3 to cwd.

Soundtrack for a video:

"给这段 clip.mp4 配一段紧张的背景音乐"

Detect: soundtrack capability ("配乐"), input is a video
Validate: clip.mp4 exists (mp4/mov/avi/mkv/webm), under 10 MB
Infer: prompt = "紧张的背景音乐"
Confirm summary → user confirms

bash
listenhub music soundtrack \
  --video "clip.mp4" \
  --prompt "tense, suspenseful background score" \
  --json

Recognize lyrics (sync):

"帮我识别 song.mp3 里的歌词"

Detect: recognize capability ("识别歌词")
Validate: song.mp3 exists, under 10 MB
Run in foreground and show lyrics with timestamps

bash
listenhub music recognize --audio "song.mp3" --json

Split stems (sync):

"把 track.mp3 分轨"

Detect: stem capability ("分轨")
Ask: separation model (audio-separation-1 / audio-separation-2)
Run in foreground; in download mode, fetch the ZIP URL to cwd

bash
listenhub music stem --audio "track.mp3" --model "audio-separation-2" --json

Publisher	marswaveai
Repository	`skills`
Skill name	`music`
Stars	82
Forks	3
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

Music

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Use

When NOT to Use

Purpose

Hard Constraints

Step -1: CLI Auth Check

Step 0: Config Setup

Setup Flow (user-initiated reconfigure only)

Interaction Flow

Step 1: Capability

Step 2: Gather inputs (per capability)

Step 3: Confirm

Workflow

Async generation commands

Sync analysis commands

Task management

After Successful Generation

Resources

Composability

Examples

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Use

When NOT to Use

Purpose

Hard Constraints

Step -1: CLI Auth Check

Step 0: Config Setup

Setup Flow (user-initiated reconfigure only)

Interaction Flow

Step 1: Capability

Step 2: Gather inputs (per capability)

Step 3: Confirm

Workflow

Async generation commands

Sync analysis commands

Task management

After Successful Generation

Resources

Composability

Examples

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now