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MiniMax Music Expert · LeoYeAI | DecimalAI

Design & Mediaby LeoYeAI

MiniMax Music Expert

Create music with MiniMax music models (music-2.5+, music-2.5).

Never made a case worse

SkillScore

SkillScore35/100

Test results 35

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

4%35%of test cases passed +30 pts

Without skill1 of 23

With skill8 of 23

7 fixed1 passed either way15 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v1

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add mm-music-expert
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install mm-music-expert

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/mm-music-expert/SKILL.md https://app.decimal.ai/s/mm-music-expert/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/mm-music-expert/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add mm-music-expert

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add mm-music-expert

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add mm-music-expert

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add mm-music-expert

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add mm-music-expert

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add mm-music-expert

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add mm-music-expert

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add mm-music-expert

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add mm-music-expert

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add mm-music-expert

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add mm-music-expert

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add mm-music-expert

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mm-music-expert")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/mm-music-expert/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add mm-music-expert

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/mm-music-expert/SKILL.md https://app.decimal.ai/s/mm-music-expert/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-music-expert/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/mm-music-expert/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/mm-music-expert.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/27/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

MiniMax Music Expert

Generate music using MiniMax Music Generation API. Default model: music-2.5+ (recommended). This skill is designed to help music novices create professional-quality music through guided interaction.

Environment Setup

export MINIMAX_MUSIC_API_KEY="your_api_key"

Agent Workflow (MUST FOLLOW)

When a user requests music creation, always follow this 4-step interactive workflow. Do NOT skip steps or jump directly to generation.

Step 1: Understand & Clarify Requirements

Analyze the user's input and identify which dimensions are already covered vs. missing. Then ask targeted follow-up questions to fill the gaps.

Dimension checklist — check which are provided, ask about the rest. Each question MUST provide lettered options (A/B/C/...) for the user to choose from:

Dimension	Question with options
Type	"这首歌的类型？ A. 有人声演唱的歌曲 B. 纯音乐/背景音乐(无人声) C. 哼唱/吟唱(旋律人声无歌词)"
Genre/Style	"你喜欢什么音乐风格？ A. 流行 B. 摇滚 C. 民谣 D. 电子/EDM E. 嘻哈/说唱 F. 爵士 G. 古风/中国风 H. R&B I. 其他(请描述)"
Mood/Emotion	"希望传达什么情绪？ A. 欢快活泼 B. 温柔抒情 C. 忧伤感怀 D. 激昂热血 E. 治愈放松 F. 大气磅礴/史诗感 G. 浪漫甜蜜 H. 其他(请描述)"
Scene/Use case	"这首歌打算用在什么场景？ A. 短视频/Vlog BGM B. 个人收藏/送人 C. 游戏/动画 D. 咖啡馆/餐厅 E. 婚礼/庆典 F. 运动/健身 G. 其他(请描述)"
Tempo/Rhythm	"节奏方面的偏好？ A. 慢速抒情(60-80 BPM) B. 中速舒缓(80-110 BPM) C. 中快轻快(110-130 BPM) D. 快速动感(130-160 BPM) E. 不确定，交给你来定"
Instruments	"有没有想要的乐器？ A. 钢琴为主 B. 吉他(民谣/电吉他) C. 中国民乐(笛子/琵琶/古筝/二胡) D. 弦乐(小提琴/大提琴) E. 电子合成器 F. 管乐(萨克斯/小号) G. 不确定，根据风格搭配 H. 其他(请描述)"
Vocal	"人声方面？(纯音乐跳过) A. 温柔女声 B. 有力女声 C. 温柔男声 D. 有力男声 E. 烟嗓/沙哑 F. 不确定，交给你来定"
Lyrics	"歌词方面？(纯音乐跳过) A. 我已有歌词 B. 我给主题/关键词，你来写 C. 全部交给AI自动生成"
Dynamic Arc	"歌曲的情绪走向？ A. 由浅入深，慢慢推向高潮 B. 开头就炸裂，全程高能 C. 先抑后扬(安静开头→爆发副歌) D. 起伏交替(安静→爆发→安静→更大爆发) E. 全程平稳舒缓 F. 不确定，交给你来编排"
Reference	"有没有喜欢的参考？(optional) A. 有，类似某个歌手/歌曲(请说明) B. 没有特别参考"

Rules:

不要一次抛出所有问题。根据用户已提供的信息，只问缺失的关键维度（通常 2-4 个问题）
每个问题必须带字母选项(A/B/C/...)，降低用户决策成本
选项末尾提供"其他"或"交给你来定"的兜底选项，避免用户被选项限制
如果用户的需求已经很明确（覆盖了 Type + Genre + Mood + 至少一个其他维度），可以直接进入 Step 2

示例交互：

用户: "帮我做一首关于杭州的歌" Agent: "好的！关于这首杭州之歌，我还想确认几个方面，你可以直接回复选项字母：

🎵 风格：A. 中国风民谣 B. 流行 C. 古风 D. R&B E. 其他

🎤 情绪：A. 温柔抒情 B. 大气磅礴 C. 欢快明朗 D. 忧伤怀旧

🎶 人声：A. 温柔女声 B. 有力女声 C. 温柔男声 D. 有力男声

🎭 情绪走向：A. 由浅入深慢慢推向高潮 B. 先抑后扬 C. 全程舒缓 D. 起伏交替

✍️ 歌词：A. 我已有歌词 B. 我给主题你来写 C. AI自动生成

直接回复如 1A 2B 3A 4A 5B 即可！"

Step 2: Design Song Blueprint & Present Draft

This is the most important step. Do NOT write a flat prompt. First design a Song Blueprint (段落蓝图) that plans each section's musical characteristics, then derive the prompt and lyrics from it.

Step 2a: Create Song Blueprint

Based on gathered requirements, plan the section-by-section musical journey. Each section should have distinct characteristics.

Blueprint format:

🎼 **歌曲蓝图**

| 段落 | 情绪/能量 | 乐器/编曲 | 人声表现 | 制作特点 |
|------|-----------|-----------|----------|----------|
| [Intro] | ... | ... | ... | ... |
| [Verse 1] | ... | ... | ... | ... |
| [Pre Chorus] | ... | ... | ... | ... |
| [Chorus] | ... | ... | ... | ... |
| ... | ... | ... | ... | ... |

Blueprint example (用户需求: "一首关于杭州的中国风民谣，温柔开头，副歌大气"):

🎼 **歌曲蓝图**

| 段落 | 情绪/能量 | 乐器/编曲 | 人声表现 | 制作特点 |
|------|-----------|-----------|----------|----------|
| [Intro] | 宁静悠远 ⚡① | 古筝独奏琶音 | — | 空间感大，远处水声质感 |
| [Verse 1] | 细腻叙述 ⚡② | +笛子旋律，轻拨琵琶 | 温柔低吟，呼吸感 | 亲密感，少混响 |
| [Verse 2] | 情感渐浓 ⚡③ | +二胡加入，节奏轻启 | 声线渐开，带感情 | 开始加宽声场 |
| [Pre Chorus] | 蓄势待发 ⚡⑤ | 弦乐铺底渐强，鼓点加入 | 力度上升，情绪推动 | 渐强(crescendo) |
| [Chorus] | 大气磅礴 ⚡⑧ | 全编制爆发：弦乐+鼓+笛+琵琶 | 高亢嘹亮，饱满和声 | "音墙"，宽广声场 |
| [Bridge] | 回归内省 ⚡③ | 仅钢琴+古筝 | 轻声吟唱 | 突然安静，对比反差 |
| [Chorus] | 最终爆发 ⚡⑨ | 全编制+合唱层叠 | 全力释放，高音 | 最高能量 |
| [Outro] | 渐归平静 ⚡② | 古筝回归独奏 | — | 渐弱收尾 |

⚡① ~ ⚡⑩ = energy level, 用于可视化段落间的能量变化曲线。

Blueprint design principles:

段落间必须有对比：相邻段落的能量/乐器/人声至少有一项明显不同
能量曲线要有起伏：避免全程同一能量级。典型曲线：低→中→高→低→最高→低
Chorus 必须是能量峰值：确保副歌和主歌之间有显著的编曲差异
Bridge 通常是"呼吸空间"：在两次 Chorus 之间提供反差

Step 2b: Derive Prompt from Blueprint

将蓝图中的信息转化为 prompt。好的 prompt 需要同时描述整体基调和段落间的动态变化。

Prompt construction formula:

[整体风格/BPM] + [人声描述 + 段落间变化] + [核心乐器] + [动态弧线描述] + [声场/制作特点]

Example (based on the 杭州 blueprint above):

A poetic and cinematic Chinese folk pop at 78 BPM, featuring a tender female vocal that transitions from intimate whispering in the verses to powerful soaring belting in the choruses with layered harmonies. The arrangement builds from a solo guzheng intro, gradually adding dizi (bamboo flute), pipa, and erhu through the verses, swelling to a full orchestral "wall of sound" with strings and percussion in the choruses, before retreating to a quiet guzheng outro. The production shifts from a dry, intimate space in the verses to a wide, reverberant arena soundscape in the choruses.

Key: The prompt explicitly describes what changes between sections — not just a flat list of attributes.

Step 2c: Write Lyrics with Section Directions

Write lyrics using annotated structure tags and parenthetical directions to reinforce the blueprint:

[Intro]
（古筝琶音，空灵悠远）

[Verse 1: 温柔低吟]
西湖 烟雨 朦胧了 谁的眼
断桥 残雪 化作 心头的暖

[Verse 2: 情感渐浓]
（笛声加入，轻柔的鼓点）
三潭 映月 点亮了 千年夜
灵隐 钟声 唤醒 梦中的蝶

[Pre Chorus: 情绪递进]
（弦乐渐强 / Rising strings）
这片土地 承载了 多少故事
每一寸 都是 我的根

[Chorus: 大气爆发]
（Full band kicks in / 全编制爆发）
杭州 我的杭州
你是 我心中 永远的歌
...

[Bridge: 回归平静]
（Music drops to piano and guzheng only / 仅剩钢琴与古筝）
闭上眼 你还在那里
...

[Chorus: 最终高潮]
（Maximum energy / 最高能量，加入合唱层）
杭州 我的杭州
...

[Outro: 渐弱收尾]
（古筝独奏，渐弱 / Guzheng solo, fading out）

Step 2d: Present Complete Draft

Combine all above into the final presentation:

📋 **生成方案**

**模式**：Standard Song

🎼 **歌曲蓝图**：
[the blueprint table]

**Prompt**：
[the full prompt — with dynamic arc description]

**歌词**：
[the full lyrics — with annotated tags and parenthetical directions]

**说明**：[1-2 sentences on creative choices]

---
请确认是否满意，或告诉我需要调整的地方（如某段落的情绪、乐器、歌词等）。

Rules:

Always create the blueprint first, then derive prompt and lyrics from it
Prompt must describe section transitions, not just a flat style
Lyrics must use annotated structure tags (e.g., [Chorus: 大气爆发]) and parenthetical directions (e.g., （Full band kicks in）) to control each section's sound
Show the complete blueprint, prompt, and lyrics — never hide details
Wait for explicit user confirmation before proceeding to Step 3

Step 3: Generate Music

After user confirms (or after applying their revisions and getting re-confirmation):

Determine the output file name from context (e.g., ./audio/杭州之歌.mp3)
Run the generation command:

python scripts/generate_music.py \
  --lyrics "<final_lyrics>" \
  --prompt "<final_prompt>" \
  --output ./audio/<filename>.mp3

For instrumental mode, add --is-instrumental true and omit --lyrics. For auto-lyrics mode, add --lyrics-optimizer true and omit --lyrics.

Inform user that generation is in progress (API call may take 30-120 seconds)

Step 4: Post-Generation Feedback

After the audio file is generated:

Tell the user the file location and invite them to listen
Ask for feedback:

🎵 音乐已生成！文件保存在：`./audio/<filename>.mp3`

请试听后告诉我你的感受：
- ✅ 满意，完成
- 🔄 需要调整（请告诉我具体想改什么，比如节奏太快、想换个风格、歌词某处要改等）

If user requests changes:
- Analyze what needs modification (prompt, lyrics, or both)
- Return to Step 2 with the revised draft
- Repeat until user is satisfied

Common revision patterns:

User feedback	Action
"节奏太快/太慢"	Adjust BPM in prompt
"风格不对"	Revise genre/style keywords in prompt
"人声不喜欢"	Adjust vocal description in prompt
"歌词某段要改"	Modify specific lyrics section
"想加点 rap"	Add `[Verse: Rap]` section in lyrics, update prompt
"乐器不对"	Revise instrument list in prompt
"感觉太单调"	Add dynamic arc description, enrich structure tags with annotations
"想要更有层次感"	Add parenthetical production directions in lyrics, describe section transitions in prompt

Model & Mode Reference

Model Differences

Feature	music-2.5	music-2.5+ (recommended)
Standard song (with lyrics)	✅ lyrics required	✅ lyrics required
Pure music (`is_instrumental`)	❌ not supported	✅ prompt required, lyrics optional
Auto-generate lyrics (`lyrics_optimizer`)	✅	✅
`prompt` field	Optional [0, 2000] chars	Optional for songs; Required [1, 2000] for instrumental
`lyrics` field	Required [1, 3500] chars	Required for songs; optional for instrumental

Generation Modes

Mode	When to use	Key Args
Standard Song	User has/wants lyrics + singing	`--lyrics` + `--prompt`
Pure Music	"纯音乐" / "instrumental" / "背景音乐" / "无人声"	`--is-instrumental true` + `--prompt`
Chanting	"哼唱" / "吟唱" / "humming"	`--lyrics` (syllables) + `--prompt`
Auto Lyrics	User wants AI to write lyrics	`--lyrics-optimizer true` + `--prompt`

Prompt Crafting (Best Practices)

Prompt 控制音乐的整体风格、情绪和制作方向。好的 prompt 不是扁平的属性罗列，而是描述音乐如何在段落间变化。

核心维度（必须覆盖）

维度	说明	示例
Genre + Era/Region	具体风格 + 时代或地域	`1980s J-Rock`, `Chinese folk R&B`, `昭和年代Disco`
BPM	明确的节拍速度	`92 BPM`, `136 BPM`, `140 BPM`
Vocal + Transition	性别、音色 + 段落间如何变化	`soft airy female vocal that transitions from intimate whispering in verses to powerful soaring belting in choruses`
Instruments + Layering	核心乐器 + 各段落如何叠加/退出	`builds from solo guzheng, adding dizi and pipa through verses, swelling to full orchestral in choruses`
Mood	2-3 个情绪描述词	`passionate, nostalgic, hopeful`
Dynamic Arc	段落间的能量演进曲线	`from intimate dry verse into wide reverberant chorus, retreating to quiet bridge before final explosive chorus`

进阶维度（提升精度）

维度	说明	示例
Production/Mix	混音风格、段落间空间变化	`dry intimate space in verses, wide reverberant arena soundscape in choruses`
Sound Design	特定风格的声学特征	`rock saturation`, `80s Minneapolis sound`, `modern electronic wide transients`
Scene/Use case	使用场景	`anime theme`, `club/party`, `coffee shop BGM`
Avoid	排除元素	`no autotune`, `avoid heavy reverb`, `no trap hi-hats`

Prompt 构建公式（推荐）

[整体风格 + BPM] + [人声描述 + 段落间变化] + [乐器编排 + 段落间叠加/退出] + [动态弧线] + [声场/制作变化]

English template (highest precision):

A [mood] [BPM] [genre] track featuring a [vocal] that [transitions from X in verses to Y in choruses], with arrangement building from [sparse intro instruments] through [verse additions] to [full chorus instrumentation], [bridge contrast], before [final chorus climax]. Production shifts from [verse sound] to [chorus sound].

Example:

A poetic and cinematic Chinese folk pop at 78 BPM, featuring a tender female vocal that transitions from intimate whispering in the verses to powerful soaring belting in the choruses with layered harmonies. The arrangement builds from a solo guzheng intro, gradually adding dizi, pipa, and erhu through the verses, swelling to a full orchestral "wall of sound" with strings and percussion in the choruses, retreating to quiet guzheng in the bridge, before a final explosive chorus with choir layers. Production shifts from dry intimate space in verses to wide reverberant arena soundscape in choruses.

Chinese description template:

78 BPM 的诗意中国风民谣，温柔女声演唱。主歌低吟浅唱、气息感十足，到副歌转为高亢嘹亮的全力释放。编曲从古筝独奏开头，主歌逐渐加入笛子、琵琶，到副歌时弦乐、鼓点全编制爆发形成"音墙"，Bridge回归古筝独奏的宁静，尾声再次爆发后古筝收尾渐弱。

Simple tag template (for straightforward requests):

[风格], [情绪], [BPM], [乐器], [人声类型]

示例: Pop,中国风R&B / Afro House,Tropical House / 流行，女生，舒缓

常见用户需求 → Prompt 转换

用户需求	Prompt 方向
"主旋律大气磅礴"	prompt 描述动态弧线: `arrangement builds from gentle opening to massive orchestral chorus with powerful brass, strings and timpani, creating an epic arena soundscape`
"副歌加一段 rap"	prompt: `with melodic rap in verses transitioning to anthemic singing in choruses`; 歌词: `[Verse 1: Rap]` + `[Chorus: Singing]`
"歌颂我的家乡·杭州"	主题在歌词；prompt 描述风格+动态: `Chinese folk pop, building from intimate solo guzheng verse to full ensemble chorus`
"使用笛子、琵琶等民乐"	prompt 描述乐器如何分布: `featuring dizi (bamboo flute) as verse melody, pipa as rhythmic texture, erhu joining in bridge, full silk-bamboo ensemble in chorus`
"复古迪斯科"	`复古迪斯科风格，主歌以放克吉他切分律动为主，副歌管弦与合成器全面加入，庆典般的能量爆发`
"治愈系轻音乐"	`healing ambient, starting with solo piano, gradually layering soft strings and gentle pads, building warmth` + `--is-instrumental true`
"开头要炸裂"	`explosive intro with heavy bass and drum impact, settling into groove for verses, building back to maximum energy chorus`
"想要有层次感"	prompt 显式描述每段的乐器增减: `verse: acoustic guitar + light drums → pre-chorus: add synth pads + bass → chorus: full band with layered vocals → bridge: strip to piano only`

Lyrics Writing (Best Practices)

结构标签

官方支持的 14 种标签：

[Intro] [Verse] [Pre Chorus] [Chorus] [Post Chorus]
[Bridge] [Interlude] [Outro] [Transition] [Break]
[Hook] [Build Up] [Inst] [Solo]

结构标签高级用法

编号区分：用编号区分不同段落，使每段有不同表现：

[Verse 1]
街边红灯 摇曳 破了 暮色
[Verse 2]
玉米 粒粒 颗颗 都是 地道

注释标签：在标签后用冒号加注释，指导该段落的演绎方式：

[Intro Hook: Heavy Hanmai Style & 808 Drop]
[Verse 1: Aggressive Rap]
[Pre-Chorus: Build Up]
[Chorus: Maximum Energy]
[Bridge: Comical Trombone Solo]
[Outro: Speed Up]
[Drop / Chorus]

段落对比编排（关键：避免歌曲单调）

核心原则：每个段落的歌词中应通过注释标签 + 括号指令明确告诉模型这一段的音乐特征，确保相邻段落之间产生对比。

完整示例（注意每个段落的括号指令如何控制乐器和能量的变化）：

[Intro]
（古筝独奏琶音，空旷悠远）

[Verse 1: 温柔叙述]
（轻柔的笛子旋律加入，亲密的混音空间）
西湖 烟雨 朦胧了 谁的眼
断桥 残雪 化作 心头的暖

[Pre Chorus: 情绪递进]
（弦乐渐强，鼓点轻启 / Strings building, drums entering）
这片土地 承载了 多少故事

[Chorus: 全力爆发]
（Full band kicks in / 全编制爆发，宽广音场）
杭州 我的杭州
你是我心中 永远的歌

[Bridge: 突然安静]
（Music drops to piano only / 仅剩钢琴，安静的呼吸空间）
闭上眼 你还在那里

[Chorus: 最终高潮]
（Maximum energy, choir layers added / 最高能量，加入合唱）
杭州 我的杭州

对比维度清单（至少变化 1-2 项）：

维度	Verse (低能量)	Chorus (高能量)
乐器数量	1-2 件	全编制
音场宽度	亲密/干	宽广/混响
人声力度	低吟/轻唱	高亢/嘹亮
节奏密度	稀疏/无鼓	密集/强劲鼓点
动态层次	轻 (p/mp)	强 (f/ff)

括号指令（关键技巧）

在歌词中使用 () 或 （） 插入演奏/音效/人声指令，模型会尝试执行：

乐器控制：

[Intro]
（琵琶琶音）
[Outro]
（二胡拉出一个帅气的尾音）
（鼓声重击收尾）

音效/声音设计：

(Sound of glass breaking / 玻璃破碎声)
(Phone ringing sound / 电话铃声)
(Sudden change to 8-bit game sound / 突然变成红白机音效)
(Abrupt silence / 突然安静)
(Sound of a chicken / 一声鸡叫): 咯咯哒？

人声技巧：

(Robotic Voice / 机械音)
(Super fast flow / 极快语速)
(Pitch rising higher and higher / 音调越来越高)
(Deep Voice / 深沉男声)
(Gang Vocals, Everyone shouting)
(Manic laughter)

编曲/动态：

(Heavy Bass kicks in / 重低音爆发)
(Rising synth / 情绪递进)
(Music stops briefly, only Trombone playing funny melody)
(BPM increases significantly to Happy Hardcore speed)
(Fading out with beat / 渐弱)
(Maximum Volume / 最大音量)
(Beat returns - Manyao Style)

拟声词/象声词

在歌词中直接写拟声词可产生特殊音效效果：

Boom Boom Boom!
A-Ba A-Ba A-Ba A-Ba!
Bi-bu Bi-bu Bi-bu Bi-bu (救护车音效)
Womp Womp Womp
嗡——啪!

中文空格分词（节奏控制）

中文歌词中加空格控制字词的演唱节奏和断句：

街边红灯 摇曳 破了 暮色          ← 空格控制断句节奏
半城 烟火 尽是 诱人 醇香

对比无空格版本：

街边红灯摇曳破了暮色              ← 模型自行断句，节奏不可控

双语歌词

中英混合歌词可产生跨文化效果：

霓虹灯在闪烁 (Flash, Flash)
别管现在几点钟 (Tik, Tok)
Zuo! You! Shang! Xia! (左! 右! 上! 下!)

典型歌曲结构模板

标准流行曲：

[Intro]
[Verse 1]
[Pre Chorus]
[Chorus]
[Verse 2]
[Bridge]
[Chorus]
[Outro]

EDM/舞曲：

[Intro]
[Verse 1]
[Pre-Chorus: Build Up]
[Drop / Chorus]
[Verse 2]
[Build Up]
[Drop / Chorus: Maximum Energy]
[Outro]

带 Rap 的歌曲：

[Intro]
[Verse 1: Rap]
[Pre Chorus]
[Chorus]
[Verse 2: Melodic Rap]
[Bridge]
[Chorus]
[Outro]

Script Reference

File	Purpose
`scripts/generate_music.py`	Main CLI: lyrics + prompt → audio file
`scripts/utils_audio.py`	Hex decoding, file saving, URL download

Key CLI Arguments

Arg	Default	Description
`--lyrics`	—	Song lyrics (required unless instrumental or lyrics-optimizer)
`--prompt`	—	Style/scene description (required for instrumental)
`--model`	`music-2.5+`	Model name
`--is-instrumental`	false	Pure music mode (music-2.5+ only)
`--lyrics-optimizer`	false	Auto-generate lyrics from prompt
`--output`	—	Output file path (required)
`--output-format`	`url`	Response format: `hex` or `url`
`--format`	—	Audio codec: `mp3`, `wav`, `pcm`
`--sample-rate`	—	16000 / 24000 / 32000 / 44100
`--bitrate`	—	32000 / 64000 / 128000 / 256000
`--stream`	false	Streaming mode (hex only)
`--aigc-watermark`	false	Append watermark (non-stream only)
`--download`	false	Download when output-format=url

API Payload Quick Reference

Standard Song:

{
  "model": "music-2.5+",
  "prompt": "indie folk, melancholic",
  "lyrics": "[Verse]\n街灯微亮晚风轻抚\n影子拉长独自漫步\n[Chorus]\n推开木门香气弥漫",
  "audio_setting": { "sample_rate": 44100, "bitrate": 256000, "format": "mp3" }
}

Pure Music (music-2.5+ only):

{
  "model": "music-2.5+",
  "prompt": "coffee shop, ambient, relaxing, piano",
  "is_instrumental": true
}

Auto Lyrics:

{
  "model": "music-2.5+",
  "prompt": "upbeat pop, summer vibes",
  "lyrics_optimizer": true
}

For full API details, see references/minimax_music_api.md.

When it activates

Automatically, when a task matches. Use when generating songs, instrumental tracks, or chanting from lyrics and style prompts via MiniMax Music Generation API. Guides music novices through an interactive workflow to produce professional-quality music.

About this skill

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Writing & Editing pack

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/mm-music-expert/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 3 files · scripts

Illustrative Want to see what mm-music-expert would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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MiniMax Music Expert

Environment Setup

Agent Workflow (MUST FOLLOW)

Step 1: Understand & Clarify Requirements

Step 2: Design Song Blueprint & Present Draft

Step 2a: Create Song Blueprint

Step 2b: Derive Prompt from Blueprint

Step 2c: Write Lyrics with Section Directions

Step 2d: Present Complete Draft

Step 3: Generate Music

Step 4: Post-Generation Feedback

Model & Mode Reference

Model Differences

Generation Modes

Prompt Crafting (Best Practices)

核心维度（必须覆盖）

进阶维度（提升精度）

Prompt 构建公式（推荐）

常见用户需求 → Prompt 转换

Lyrics Writing (Best Practices)

结构标签

结构标签高级用法

段落对比编排（关键：避免歌曲单调）

括号指令（关键技巧）

拟声词/象声词

中文空格分词（节奏控制）

双语歌词

典型歌曲结构模板

Script Reference

Key CLI Arguments

API Payload Quick Reference

When it activates

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