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MiniMax Voice Maker · LeoYeAI | DecimalAI

Design & Mediaby LeoYeAI

MiniMax Voice Maker

Enables voice synthesis, voice cloning, voice design, and audio post-processing using MiniMax Voice API and FFmpeg.

SkillScore

SkillScore45/100

Test results 45

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

5%45%of test cases passed +41 pts

Without skill1 of 22

With skill10 of 22

9 fixed1 passed either way12 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v115 comparable

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-voice-maker
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-voice-maker")
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-voice-maker

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-voice-maker/SKILL.md https://app.decimal.ai/s/mm-voice-maker/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/mm-voice-maker/SKILL.md

This gets SKILL.md only. The skill's 9 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-voice-maker

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-voice-maker")
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-voice-maker/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-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/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-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker")
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-voice-maker/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/mm-voice-maker/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 9 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-voice-maker

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-voice-maker/SKILL.md https://app.decimal.ai/s/mm-voice-maker/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/mm-voice-maker/SKILL.md

This gets SKILL.md only. The skill's 9 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-voice-maker/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/mm-voice-maker.json

This gets SKILL.md only. The skill's 9 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 Voice Maker

Professional text-to-speech skill with emotion detection, voice cloning, and audio processing capabilities powered by MiniMax Voice API and FFmpeg.

Capabilities

Area	Features
TTS	Sync (HTTP/WebSocket), async (long text), streaming
Segment-based	Multi-voice, multi-emotion synthesis from segments.json, auto merge
Voice	Cloning (10s–5min), design (text prompt), management
Audio	Format conversion, merge, normalize, trim, remove silence (FFmpeg)

File structure:

mmVoice_Maker/
├── SKILL.md                       # This overview
├── mmvoice.py                     # CLI tool (recommended for Agents)
├── check_environment.py           # Environment verification
├── requirements.txt
├── scripts/                       # Entry: scripts/__init__.py
│   ├── utils.py                   # Config, data classes
│   ├── sync_tts.py                # HTTP/WebSocket TTS
│   ├── async_tts.py               # Long text TTS
│   ├── segment_tts.py             # Segment-based TTS (multi-voice, multi-emotion)
│   ├── voice_clone.py             # Voice cloning
│   ├── voice_design.py            # Voice design
│   ├── voice_management.py        # List/delete voices
│   └── audio_processing.py        # FFmpeg audio tools
└── reference/                     # Load as needed
    ├── cli-guide.md               # CLI usage guide
    ├── getting-started.md         # Setup and quick test
    ├── tts-guide.md               # Sync/async TTS workflows
    ├── voice-guide.md             # Clone/design/manage
    ├── audio-guide.md             # Audio processing
    ├── script-examples.md         # Runnable code snippets
    ├── troubleshooting.md         # Common issues
    ├── api_documentation.md       # Complete API reference
    └── voice_catalog.md           # Voice selection guide

Main Workflow Guideline (Text to Speech)

6-step workflow: [step1]. Verify environment

[step2-preparation]⚠️NOTE: Before processing the text, you must read voice-catalog.md for voice selection.

[step2]. Process text into script → <cwd>/audio/segments.json. Note: [Step2.4] is really important, you must check it twice before sending the script to the user.

[step2.5]. ⚠️ Generate preview for user confirmation (highly recommended for multi-voice content)

[step3]. Present plan to user for confirmation

[step4]. Validate segments.json

[step5]. Generate and merge audio → intermediate files in <cwd>/audio/tmp/, final output in <cwd>/audio/output.mp3

[step6]. ⚠️ CRITICAL: User confirms audio quality FIRST → THEN cleanup temp files (only after user is satisfied)

<cwd> is Claude's current working directory (not the skill directory). Audio files are saved relative to where Claude is running commands.

Step 1: Verify environment

python check_environment.py

Checks:

Python 3.8+
Required packages (requests, websockets)
FFmpeg installation
MINIMAX_VOICE_API_KEY environment variable

If API key is not set, ask user for keys and set it:

export MINIMAX_VOICE_API_KEY="your-api-key-here"

Step 2: Decision and Pre-processing

⚠️ MOST IMPORTANT PRINCIPLE: Gender Matching First

Before selecting voices, you MUST always match gender first. This is non-negotiable.

Golden Rule:

If a character is male → use male voice If a character is female → use female voice If a character is neutral/other → choose appropriate neutral voice

Why this matters:

Violating gender matching (e.g., male character with female voice) breaks immersion
Even if personality traits match, gender comes first
This is especially critical for classic literature, historical content, and professional narration

Examples:

Character	Wrong Voice	Correct Voice
唐三藏 (male monk)	`female-yujie` ❌	`Chinese (Mandarin)_Gentleman` ✅
林黛玉 (female)	`male-qn-badao` ❌	`female-shaonv` ✅
曹操 (male warlord)	`female-chengshu` ❌	`Chinese (Mandarin)_Unrestrained_Young_Man` ✅

Decision guide: Evaluate based on:

Does the user specify a model? → Use that model, or use the default one "speech-2.8"
Is multi-voice needed? → Different voice_id per speaker/character
For speech-2.8: emotion is auto-matched (leave emotion empty)
For older models: manually specify emotion tags

Use case scenarios:

Scenario	Description	Segments	Voice Selection
Single Voice	User needs one voice for the entire content. Segment only by length (≤1,000,000 chars per segment).	Split by length only	One voice_id for all segments
Multi-Voice	Multiple characters/speakers, each with different voice. Segment by speaker/role changes.	Split by logical unit (speaker, dialogue, etc.)	Different voice_id per role
Podcast/Interview	Host and guest speakers with distinct voices.	Split by speaker	Voice per host/guest
Audiobook/Fiction	Narrator and character voices.	Split by narration vs. dialogue	Voice per narrator/character
Documentary	Mostly narration with occasional quotes.	Keep as one segment	Single narrator voice
Report/Announcement	Formal content with consistent tone.	Keep as one segment	Professional voice

Processing Workflow (4 sub-steps):

Step 2.1: Text Segmentation and Role Analysis First, segment your text into logical units and identify the role/character for each segment.

Key principle (Important!): Split by logical unit, NOT simply by sentence

When to split (Important!):

Different speakers clearly marked
Narrator vs. character dialogue (in fiction/audiobooks/interview etc.)
In some scenarios (like audiobooks, multi-voice fiction etc.), where speaker's identity is important, split when narration and dialogue mix in the same sentence.

When NOT to split (Important!):

Third-person narration like "John said..." or "The reporter noted..."
Quoted speech in narration (in documentary/podcast/report etc.) should keep in narrator's voice
Keep in narrator's voice unless specific characterization is needed

Decision depends on use case:

Use case	Example	Split strategy
Single Voice	Long article, news piece, announcement	Split by length (≤1,000,000 chars), same voice for all
Podcast/Interview	"Host: Welcome to the show. Guest: Thank you for having me."	Split by speaker
Documentary narration	"The scientist explained, 'The results are promising.'"	Keep as one segment (narrator voice)
Audiobook/Fiction	"'Who's there?' she whispered."	Split: "'Who's there?'" should be in character voice, while "she whispered." should be in narrator's voice
Report	"According to the report, the economy is growing."	Keep as one segment

Example1: Single Voice (speech-2.8) For single-voice content (e.g., news, announcements, articles), segment only by length while maintaining the same voice:

[
  {"text": "First part of the article (under 1,000,000 chars)...", "role": "narrator", "voice_id": "female-shaonv", "emotion": ""},
  {"text": "Second part of the article (under 1,000,000 chars)...", "role": "narrator", "voice_id": "female-shaonv", "emotion": ""},
  {"text": "Third part of the article (under 1,000,000 chars)...", "role": "narrator", "voice_id": "female-shaonv", "emotion": ""}
]

Example2: Audiobook with characters (speech-2.8) In audiobooks (multi-voice fiction), split when narration and dialogue mix in the same sentence:

[
  {"text": "The detective entered the room.", "role": "narrator", "voice_id": "", "emotion": ""},
  {"text": "\"Who's there?\"", "role": "female_character", "voice_id": "", "emotion": ""},
  {"text": "she whispered.", "role": "narrator", "voice_id": "", "emotion": ""},
  {"text": "\"It's me,\"", "role": "male_character", "voice_id": "", "emotion": ""},
  {"text": "he replied calmly.", "role": "narrator", "voice_id": "", "emotion": ""}
]

Example3: Documentary/podcast narration (speech-2.8) Quoted speech in narration stays in narrator's voice (no need to split):

[
  {
    "text": "The scientist explained, \"The results show significant improvement in all test groups.\"",
    "role": "narrator",
    "voice_id": "",
    "emotion": ""
  },
  {
    "text": "According to the latest report, the economy has grown by 3% this quarter.",
    "role": "narrator",
    "voice_id": "",
    "emotion": ""
  }
]

**Note:** In the preliminary `segments.json`:
- Fill in the `text` field with segment content
- Fill in the `role` field to identify the character (narrator, male_character, female_character, host, guest, etc.)
- Leave `voice_id` empty (to be filled in Step 2.2)
- Leave `emotion` empty for speech-2.8 models


**Step 2.2: Voice Selection**

After segmenting and labeling roles, analyze all detected characters in your text. Consult [voice_catalog.md](reference/voice_catalog.md) **Section 1 "How to Choose a Voice"** to match voices to characters.

**⚠️ CRITICAL: Follow the two-step selection process below**

**Path A — Professional domains (Story/Narration, News/Announcements, Documentary):**
If the content belongs to one of these three professional domains, prioritize selecting from the recommended voices in **voice_catalog.md Section 2.1** (filter by scenario + gender). These voices are specifically optimized for their professional use cases.

**Path B — All other scenarios:**
Select from **voice_catalog.md Section 2.2**, following this strict priority hierarchy:

1. **First: Match Gender** (non-negotiable) — Male characters MUST use male voices, female characters MUST use female voices
2. **Second: Match Language** — The voice MUST match the content language (Chinese content → Chinese voice, Korean content → Korean voice, English content → English voice, etc.). Never assign a voice from the wrong language.
3. **Third: Match Age** — Determine the age group (Children / Youth / Adult / Elderly / Professional) and select from the corresponding subsection in Section 2.2
4. **Fourth: Match Personality & Role** — Choose the best fit based on personality traits, tone, and character role

**Voice Selection Decision Tree:**

Is this a professional domain (Story/News/Documentary)? ├── YES → Select from voice_catalog Section 2.1 (filter by scenario + gender) └── NO → Select from voice_catalog Section 2.2: Step 1: Match Gender ├── Male character → Male voices only └── Female character → Female voices only Step 2: Match Age Group └── Children / Youth / Adult / Elderly / Professional Step 3: Match Language └── Filter to voices matching the content language Step 4: Match Personality & Role └── Choose best fit by tone, personality, character role


**Step 2.3: Emotions Segmentation** *(For non-2.8 series models only)*
For models other than speech-2.8 series, analyze emotions in your segments:
- For **long segments**, split further based on **emotional transitions**
- Add appropriate **emotion tags** to each segment
- Refer to Section 3 in [text-processing.md](reference/text-processing.md) for emotion tags and examples
- Skip this step for speech-2.8 models (emotion is auto-matched)

**Emotion Tags:**
- For speech-2.6 series (speech-2.6-hd and speech-2.6-turbo): happy, sad, angry, fearful, disgusted, surprised, calm, fluent, whisper
- For older models: happy, sad, angry, fearful, disgusted, surprised, calm (7 emotions)


**Step 2.4: Check and Post-processing**
Finally, review and optimize your script:
- Verify segment length limits (async TTS ≤1,000,000 characters)
- Clean up conversational text (remove speaker names if needed)
- Ensure consistency in voice and emotion tags
- **Critical check for multi-voice content**: For audiobooks, multi-voice fiction, or content where dialogue is presented from a first-person perspective, verify that narration and dialogue mixed in the same sentence are properly split.

  **When splitting IS needed (first-person dialogue in fiction/audiobooks):**
  
  Example: `"John asked, 'Where are you going?'"` should be split into:
  - Segment 1: `"John asked, "` - uses narrator voice (describes who is speaking)
  - Segment 2: `"Where are you going?"` - uses the character's voice (actual dialogue in first-person)

  This ensures proper voice differentiation: descriptive narration uses the narrator's voice, while the character's spoken words use the character's designated voice.

  **When splitting is NOT needed (third-person quotes in podcast/documentary/news):**
  
  In podcasts, documentaries, or news reports, quoted speech is typically presented in third-person narrative style - the speaker's words are being reported, not performed. Keep these as one segment with the narrator's voice and remove the speaker's name at the beginning:
  
  - `"Welcome to our show." → narrator voice, remove the speaker's name (like "The host said:") at the beginning
  - `"According to experts, 'This technology represents a significant breakthrough.'" → keep as one segment (narrator voice)
  - `"Scientists noted, 'The experimental results exceeded our expectations.'" → keep as one segment (narrator voice)
- **If the split is missing**: Go back to Step 2.1 and ensure dialogue portions are separated from narration with appropriate role labels.

**Create segments.json:**
After completing all 4 sub-steps, save the final `segments.json` to `<cwd>/audio/segments.json`.


### Step 2.5: Generate Preview for User Confirmation (Highly Recommended)

**For multi-voice content (audiobooks, dramas, etc.), always generate a preview first.**

This saves time and prevents waste when voice selections need adjustment.

**How to generate a preview:**
1. Create a smaller segments file with 10-20 representative segments (include all characters)
2. Generate the preview audio
3. Ask user to listen and confirm voice choices

**Preview segments.json example:**
```json
[
  {"text": "Narration opening...", "role": "narrator", "voice_id": "...", "emotion": ""},
  {"text": "Male character speaks...", "role": "male_character", "voice_id": "...", "emotion": ""},
  {"text": "Female character speaks...", "role": "female_character", "voice_id": "...", "emotion": ""},
  {"text": "More dialogue...", "role": "...", "voice_id": "...", "emotion": ""}
]

Preview command:

python mmvoice.py generate segments_preview.json -o preview.mp3

When user confirms preview:

Use the same voice selections for the full segments.json
No need to re-select voices

Step 3: Present plan to user for confirmation

Before proceeding to validation and generation, present the segmentation plan to the user and wait for confirmation:

Present to the user:

Roles identified: List all characters/speakers in the text
Voice assignments: Show which voice_id is assigned to each role (include voice characteristics from voice_catalog.md)
Model being used: Explain why this model was selected
Language: Confirm the primary language of the content
Emotion approach: Auto-matched (speech-2.8) or manual tags (older models)

Example confirmation message:

I've analyzed the text and created a segmentation plan:

**Roles and Voices:**
- Narrator: male-qn-jingying (deep, authoritative, suitable for storytelling)
- Protagonist: female-shaonv (bright, energetic, youthful)
- Antagonist: male-qn-qingse (cool, menacing)

**Model:** speech-2.8-hd (recommended - automatic emotion matching)
**Language:** Chinese
**Segments:** 8 segments total

Please review and confirm:
1. ⚠️ **Gender Verification**: Do the voice genders match the character genders?
   - [Narrator: Male ✓] [Protagonist: Female ✓] [Antagonist: Male ✓]
2. ⚠️ **Language Verification**: Do the voice languages match the content language?
   - [All voices: Chinese ✓]
3. Are the voice assignments appropriate for each character (age, personality)?
4. Should any segments be combined or split differently?
5. Any other changes you'd like to make?

**After generation:**
- I'll generate a preview first for you to review
- Only after you confirm the audio quality will I clean up temporary files
- If not satisfied, I'll re-generate and we iterate until you're happy

Reply "confirm" to proceed, or let me know what to adjust.

Wait for user response:

If user confirms → Proceed to Step 4 (validate)
If user suggests changes → Update segments.json and present the plan again for confirmation

Step 4: Validate segments.json (model, emotion, voice_id validation)

Before generating audio, validate the segments file:

# Default: speech-2.8-hd (auto emotion matching)
python mmvoice.py validate <cwd>/audio/segments.json

# Specify model for context-specific validation
python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd

# Validate voice_ids against available voices (slower, requires API call)
python mmvoice.py validate <cwd>/audio/segments.json --validate-voices

# Combined options (recommended)
python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd --validate-voices

# Use `--verbose` to see segment details
python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd --validate-voices --verbose

Emotion Validation checks:

Model	Emotion Validation
speech-2.8-hd/turbo	Emotion can be empty (auto emotion matching)
speech-2.6-hd/turbo	All 9 emotions supported
Older models	happy, sad, angry, fearful, disgusted, surprised, calm (7 emotions)

Voice ID validation: With --validate-voices:

Calls API once to get all available voices
Validates each voice_id against the list
Shows errors for invalid voice_ids (blocks validation)

Step 5: Generate and merge audio

Generate audio for all segments and merge into final output.

File placement (default behavior if user doesn't specify):

<cwd>/                      # Claude's current working directory
└── audio/                  # Created automatically
    ├── tmp/                # Intermediate segment files
    │   ├── segment_0000.mp3
    │   ├── segment_0001.mp3
    │   └── ...
    └── <custom_audio_name>.mp3             # Final merged audio, name can be customized

Where <cwd> is Claude's current working directory (where commands are executed).

If -o is not specified, output goes to <cwd>/audio/output.mp3
Intermediate files go to <cwd>/audio/tmp/
After user confirms the final audio, ask whether to delete <cwd>/audio/tmp/

Basic usage:

# Default: speech-2.8-hd, output to <cwd>/audio/output.mp3
python mmvoice.py generate <cwd>/audio/segments.json

# Specify output path
python mmvoice.py generate <cwd>/audio/segments.json -o <cwd>/audio/<custom_audio_name>.mp3

# Specify model if needed
python mmvoice.py generate <cwd>/audio/segments.json --model speech-2.6-hd

Skip existing segments (for rate limit retries):

# Only generate segments that don't exist yet - skips already-generated files
python mmvoice.py generate <cwd>/audio/segments.json --skip-existing

Error handling:

If a segment fails, the script reports which segment and why
Use --continue-on-error to generate remaining segments despite failures
Use --skip-existing to skip already successfully generated segments (recommended for retries after rate limit)
The script automatically uses fallback merging if FFmpeg filter_complex fails

Step 6: Confirm and cleanup

⚠️ CRITICAL: Never delete temp files until user confirms!

After generation completes, you MUST follow this exact sequence:

Step 6.1: Report generation result to user

✓ Audio saved to: <output_path>
  Generated: X/Y segments
  Intermediate files in: <cwd>/audio/tmp/

Step 6.2: Ask user to confirm audio quality Ask the user to listen to the audio and confirm:

Is the audio quality satisfactory?
Are all voices appropriate?
Any adjustments needed?

Step 6.3: Wait for user response

Step 6.4: Only after user confirms, offer cleanup

After confirming audio quality, temporary files can be deleted with:
rm -rf <cwd>/audio/tmp/

NEVER execute rm -rf on temp files without explicit user confirmation!

If user is NOT satisfied:

Do NOT delete temp files
Discuss what needs to be adjusted
Re-generate affected segments if needed
Ask for confirmation again

Other Usage

Use the following when the task involves voice creation, single-voice TTS (sync/async), or audio processing instead of the main segment-based workflow. Each subsection gives CLI commands, script paths, and the reference doc to open for details.

Voice creation (clone / design / list)

Purpose: Create custom voices from audio (clone) or from a text description (design); list system and custom voices.

CLI (entry point: mmvoice.py):

python mmvoice.py clone AUDIO_FILE --voice-id VOICE_ID   # Clone from 10s–5min audio
python mmvoice.py design "DESCRIPTION" --voice-id ID      # Design from text
python mmvoice.py list-voices                             # List all voices

Scripts: scripts/voice_clone.py (clone), scripts/voice_design.py (design), scripts/voice_management.py (list/manage).
Documentation: reference/voice-guide.md — cloning (quick + high-quality + step-by-step), design workflow, management.

Text-to-speech (sync / async)

Purpose: Single-voice TTS: sync for short text (≤10k chars), async for long text (up to 1M chars); optional streaming.

CLI:

python mmvoice.py tts "TEXT" -o OUTPUT.mp3 [-v VOICE_ID] [--model MODEL]

Scripts: scripts/sync_tts.py (HTTP/WebSocket sync), scripts/async_tts.py (async task + poll).
Documentation: reference/tts-guide.md — sync TTS, async TTS, streaming, segment-based production.

Audio processing (merge / convert / normalize)

Purpose: Merge files (with optional crossfade), convert format, normalize loudness, trim.

CLI:

python mmvoice.py merge FILE1 [FILE2 ...] -o OUTPUT [--crossfade MS]
python mmvoice.py convert INPUT -o OUTPUT [--format FORMAT]

Script: scripts/audio_processing.py (merge, convert, normalize, trim).
Documentation: reference/audio-guide.md — format conversion, merging (filter_complex + concat demuxer fallback), normalization, trimming, optimization.

Segment-based TTS (main workflow)

CLI: validate and generate as in Steps 4–5 above.
Script: scripts/segment_tts.py.
Documentation: reference/cli-guide.md, reference/api_documentation.md.

Reference documents (on-demand)

Open these when you need concrete usage, parameters, or troubleshooting. Paths are relative to the skill root.

Document	Content for the Agent
reference/cli-guide.md	All CLI commands (`validate`, `generate`, `tts`, `clone`, `design`, `list-voices`, `merge`, `convert`, `check-env`) with options and examples. Use for correct CLI invocation.
reference/getting-started.md	Environment setup (venv, `pip install`, FFmpeg), `MINIMAX_VOICE_API_KEY`, basic synthesis test. Use for first-time setup or “env not working”.
reference/tts-guide.md	Sync TTS (short text), async TTS (long text), streaming TTS, multi-segment production. Use for sync/async/streaming logic and parameters.
reference/voice-guide.md	Voice cloning (quick, high-quality with prompt audio, step-by-step), voice design, voice management. Use for custom voice creation flows.
reference/audio-guide.md	Format conversion, merging (including crossfade and fallback), normalization, trimming, optimization. Use for merge/convert/normalize behavior and options.
reference/script-examples.md	Copy-paste runnable examples for sync TTS, async TTS, segment-based TTS, audio processing, voice clone/design/management. Use for quick Python snippets.
reference/troubleshooting.md	Environment (API key, FFmpeg), API errors, segment-based TTS, audio, voice. Use when an error message or unexpected behavior appears.
reference/api_documentation.md	Full API reference: config, sync/async TTS, emotion parameter, segment-based TTS, voice clone/design/management, audio processing, common parameters, error handling. Use for exact function signatures and parameter details.
reference/voice_catalog.md	System voices list (male/female/beta), selection guide, voice parameters, custom voices, voice IDs. Use to choose or look up `voice_id`.

Important notes

Requirements

Python: 3.8 or higher
API Key: MINIMAX_VOICE_API_KEY environment variable must be set
FFmpeg: Required for audio processing (merge, convert, normalize)
- Install: brew install ffmpeg (macOS) or sudo apt install ffmpeg (Ubuntu)

Limits and constraints

Text length: Sync TTS ≤10,000 chars; async TTS ≤1,000,000 chars
Voice cloning: Audio must be 10s–5min duration, ≤20MB, formats: mp3/wav/m4a
Voice expiration: Custom voices (cloned/designed) expire after 7 days if not used with TTS

Special features

Pause insertion: Use <#x#> in text where x = pause duration in seconds (0.01–99.99)
- Example: "Hello<#1.5#>world" creates 1.5s pause between words
Supported emotions: happy, sad, angry, fearful, disgusted, surprised, calm, fluent, whisper
- speech-2.8: automatic matching; speech-2.6: all 9; older models: first 7

Troubleshooting

Run python check_environment.py to diagnose setup issues
See troubleshooting.md for common problems and solutions
Check getting-started.md for detailed setup instructions

When it activates

Automatically, when a task matches. Use when converting text to speech, creating custom voices, or processing/merging audio.

About this skill

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0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/mm-voice-maker/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 9 files · scripts

Illustrative Want to see what mm-voice-maker 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 Voice Maker

Capabilities

File structure:

Main Workflow Guideline (Text to Speech)

Step 1: Verify environment

Step 2: Decision and Pre-processing

Step 3: Present plan to user for confirmation

Step 4: Validate segments.json (model, emotion, voice_id validation)

Step 5: Generate and merge audio

Step 6: Confirm and cleanup

Other Usage

Voice creation (clone / design / list)

Text-to-speech (sync / async)

Audio processing (merge / convert / normalize)

Segment-based TTS (main workflow)

Reference documents (on-demand)

Important notes

Requirements

Limits and constraints

Special features

Troubleshooting

When it activates

SkillSafety

Source

Other skills for the same job

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