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MiniMax Studio · LeoYeAI | DecimalAI

Design & Mediaby LeoYeAI

MiniMax Studio

Generate voice, music, and video content via MiniMax APIs. Includes voice cloning & voice design for custom voices, and FFmpeg-based media tools for audio/video format conversion, concatenation, trimming, and extraction.

Never made a case worse

SkillScore

SkillScore65/100

Test results 65

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

4%65%of test cases passed +61 pts

Without skill1 of 23

With skill15 of 23

14 fixed1 passed either way8 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v120 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 minimax-studio
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("minimax-studio")
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 minimax-studio

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/minimax-studio/SKILL.md https://app.decimal.ai/s/minimax-studio/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/minimax-studio/SKILL.md

This gets SKILL.md only. The skill's 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/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/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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("minimax-studio")
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/minimax-studio/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/minimax-studio/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 8 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 minimax-studio

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/minimax-studio/SKILL.md https://app.decimal.ai/s/minimax-studio/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/minimax-studio/SKILL.md

This gets SKILL.md only. The skill's 8 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/minimax-studio/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/minimax-studio.json

This gets SKILL.md only. The skill's 8 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 Studio

Output Directory

All generated files MUST be saved to minimax-output/ under the AGENT'S current working directory (NOT the skill directory). Every script call MUST include an explicit --output / -o argument pointing to this location. Never omit the output argument or rely on script defaults.

Rules:

Before running any script, ensure minimax-output/ exists in the agent's working directory (create if needed: mkdir -p minimax-output)
Always use absolute or relative paths from the agent's working directory: --output minimax-output/video.mp4
Never cd into the skill directory to run scripts — run from the agent's working directory using the full script path
Intermediate/temp files (segment audio, video segments, extracted frames) are automatically placed in minimax-output/tmp/. They can be cleaned up when no longer needed: rm -rf minimax-output/tmp

Prerequisites

pip install -r requirements.txt   # requests, websockets, ffmpeg-python
brew install ffmpeg                # macOS
python scripts/check_environment.py

API Key Configuration

The MINIMAX_API_KEY can be provided in two ways (either works):

.env file (recommended — persists across sessions): Create a .env file in the MiniMaxStudio project root (alongside SKILL.md):
```
MINIMAX_API_KEY=sk-api-xxxxxxxxxxxxxxxxxxxxxxxx
```
All scripts automatically load .env on startup. Environment variables take precedence over .env values.

Environment variable:

export MINIMAX_API_KEY="sk-api-xxxxxxxxxxxxxxxxxxxxxxxx"

IMPORTANT — When API Key is missing: Before running any script, check if MINIMAX_API_KEY is available (via env var or .env file). If it is NOT configured:

Ask the user to provide their MiniMax API key
Write the key to the .env file in the MiniMaxStudio skill directory (i.e., the directory containing this SKILL.md): echo 'MINIMAX_API_KEY=sk-api-xxxxx' > <skill_directory>/.env
Do NOT store the key in the agent's working directory — always write to the skill directory so it persists for all future sessions
The key starts with sk-api-, obtainable from https://platform.minimaxi.com

Key Capabilities

Capability	Description	Entry point
TTS	Text-to-speech synthesis with multiple voices and emotions	`scripts/tts/generate_voice.py`
Voice Cloning	Clone a voice from an audio sample (10s–5min)	`scripts/tts/generate_voice.py clone`
Voice Design	Create a custom voice from a text description	`scripts/tts/generate_voice.py design`
Music Generation	Generate songs with lyrics or instrumental tracks	`scripts/music/generate_music.py`
Video Generation	Text-to-video, image-to-video, subject reference, templates	`scripts/video/generate_video.py`
Long Video	Multi-scene chained video with crossfade transitions	`scripts/video/generate_long_video.py`
Media Tools	Audio/video format conversion, concatenation, trimming, extraction	`scripts/media_tools.py`

TTS (Text-to-Speech)

Entry point: scripts/tts/generate_voice.py

IMPORTANT: Single voice vs Multi-segment — Choose the right approach

User intent	Approach
Single voice / no multi-character need	`tts` command — generate the entire text in one call
Multiple characters / narrator + dialogue	`generate` command with segments.json

Default behavior: When the user simply asks to generate speech/voice and does NOT mention multiple voices or characters, use the tts command directly with a single appropriate voice. Do NOT split into segments or use the multi-segment pipeline — just pass the full text to tts in one call.

Only use multi-segment generate when:

The user explicitly needs multiple voices/characters
The text requires narrator + character dialogue separation
The text exceeds 10,000 characters (API limit per request) — in this case, split into segments with the same voice

Single-voice generation (DEFAULT)

python scripts/tts/generate_voice.py tts "Hello world" -o minimax-output/hello.mp3
python scripts/tts/generate_voice.py tts "你好世界" -v female-shaonv -o minimax-output/hello_cn.mp3

Multi-segment generation (multi-voice / audiobook / podcast)

Complete workflow — follow ALL steps in order:

Write segments.json — split text into segments with voice assignments (see format and rules below)
Run generate command — this reads segments.json, generates audio for EACH segment via TTS API, then merges them into a single output file with crossfade

# Step 1: Write segments.json to minimax-output/
# (use the Write tool to create minimax-output/segments.json)

# Step 2: Generate audio from segments.json — this is the CRITICAL step
# It generates each segment individually and merges them into one file
python scripts/tts/generate_voice.py generate minimax-output/segments.json \
  -o minimax-output/output.mp3 --crossfade 200

Do NOT skip Step 2. Writing segments.json alone does nothing — you MUST run the generate command to actually produce audio.

Voice management

# List all available voices
python scripts/tts/generate_voice.py list-voices

# Voice cloning (from audio sample, 10s–5min)
python scripts/tts/generate_voice.py clone sample.mp3 --voice-id my-voice

# Voice design (from text description)
python scripts/tts/generate_voice.py design "A warm female narrator voice" --voice-id narrator

Audio processing

python scripts/tts/generate_voice.py merge part1.mp3 part2.mp3 -o minimax-output/combined.mp3
python scripts/tts/generate_voice.py convert input.wav -o minimax-output/output.mp3

TTS Models

Model	Notes
speech-2.8-hd	Recommended, auto emotion matching
speech-2.8-turbo	Faster variant
speech-2.6-hd	Previous gen, manual emotion
speech-2.6-turbo	Previous gen, faster

segments.json Format

Default crossfade between segments: 200ms (--crossfade 200).

[
  { "text": "Hello!", "voice_id": "female-shaonv", "emotion": "" },
  { "text": "Welcome.", "voice_id": "male-qn-qingse", "emotion": "happy" }
]

Leave emotion empty for speech-2.8 models (auto-matched from text).

IMPORTANT: Multi-Segment Script Generation Rules (Audiobooks, Podcasts, etc.)

When generating segments.json for audiobooks, podcasts, or any multi-character narration, you MUST split narration text from character dialogue into separate segments with distinct voices.

Rule: Narration and dialogue are ALWAYS separate segments.

A sentence like "Tom said: The weather is great today!" must be split into two segments:

Segment 1 (narrator voice): "Tom said:"
Segment 2 (character voice): "The weather is great today!"

Example — Audiobook with narrator + 2 characters:

[
  { "text": "Morning sunlight streamed into the classroom as students filed in one by one.", "voice_id": "narrator-voice", "emotion": "" },
  { "text": "Tom smiled and turned to Lisa:", "voice_id": "narrator-voice", "emotion": "" },
  { "text": "The weather is amazing today! Let's go to the park after school!", "voice_id": "tom-voice", "emotion": "happy" },
  { "text": "Lisa thought for a moment, then replied:", "voice_id": "narrator-voice", "emotion": "" },
  { "text": "Sure, but I need to drop off my backpack at home first.", "voice_id": "lisa-voice", "emotion": "" },
  { "text": "They exchanged a smile and went back to listening to the lecture.", "voice_id": "narrator-voice", "emotion": "" }
]

Key principles:

Narrator uses a consistent neutral narrator voice throughout
Each character has a dedicated voice_id, maintained consistently across all their dialogue
Split at dialogue boundaries — "He said:" is narrator, the quoted content is the character
Do NOT merge narrator text and character speech into a single segment
For characters without pre-existing voice_ids, use voice cloning or voice design to create them first, then reference the created voice_id in segments

Music Generation

Entry point: scripts/music/generate_music.py

IMPORTANT: Instrumental vs Lyrics — When to use which

Scenario	Mode	Action
BGM for video / voice / podcast	Instrumental (default)	Use `--instrumental` directly, do NOT ask user
User explicitly asks to "create music" / "make a song"	Ask user first	Ask whether they want instrumental or with lyrics

When adding background music to video or voice content, always default to instrumental mode (--instrumental). Do not ask the user — BGM should never have vocals competing with the main content.

When the user explicitly asks to create/generate music as the primary task, ask them whether they want:

Instrumental (pure music, no vocals)
With lyrics (song with vocals — user provides or you help write lyrics)

# Instrumental (for BGM or when user chooses instrumental)
python scripts/music/generate_music.py \
  --instrumental \
  --prompt "ambient electronic, atmospheric" \
  --output minimax-output/ambient.mp3 --download

# Song with lyrics (when user chooses vocal music)
python scripts/music/generate_music.py \
  --lyrics "[verse]\nHello world\n[chorus]\nLa la la" \
  --prompt "indie folk, melancholic" \
  --output minimax-output/song.mp3 --download

# With style fields
python scripts/music/generate_music.py \
  --lyrics "[verse]\nLyrics here" \
  --genre "pop" --mood "upbeat" --tempo "fast" \
  --output minimax-output/pop_track.mp3 --download

Music Models

Model	Notes
music-2.5+	Recommended, supports `--instrumental`
music-2.5	Previous version

Video Generation

IMPORTANT: Single vs Multi-Segment — Choose the right script

User intent	Script to use
Default / no special request	`scripts/video/generate_video.py` (single segment, 10s, 768P)
User explicitly asks for "long video", "multi-scene", "story", or duration > 10s	`scripts/video/generate_long_video.py` (multi-segment)

Default behavior: Always use single-segment generate_video.py with duration 10s and resolution 768P unless the user explicitly asks for a long video, multi-scene video, or specifies a total duration exceeding 10 seconds. Do NOT automatically split into multiple segments — a single 10s video is the standard output. Only use generate_long_video.py when the user clearly needs multi-scene or longer content.

Entry point (single video): scripts/video/generate_video.py Entry point (long/multi-scene): scripts/video/generate_long_video.py

Video Model Constraints (MUST follow)

Duration limits by model and resolution:

Model	720P	768P	1080P
MiniMax-Hailuo-2.3	-	6s or 10s	6s only
MiniMax-Hailuo-2.3-Fast	-	6s or 10s	6s only
MiniMax-Hailuo-02	-	6s or 10s	6s only
T2V-01 / T2V-01-Director	6s only	-	-
I2V-01 / I2V-01-Director / I2V-01-live	6s only	-	-
S2V-01 (ref)	6s only	-	-

Resolution options by model and duration:

Model	6s	10s
MiniMax-Hailuo-2.3	768P (default), 1080P	768P only
MiniMax-Hailuo-2.3-Fast	768P (default), 1080P	768P only
MiniMax-Hailuo-02	512P, 768P (default), 1080P	512P, 768P (default)
Other models	720P (default)	Not supported

Key rules:

Default: 10s + 768P (best balance of length and quality for MiniMax-Hailuo-2.3)
1080P only supports 6s duration — if user requests 1080P, set --duration 6
10s duration only works with 768P (or 512P on Hailuo-02) — never combine 10s + 1080P
Older models (T2V-01, I2V-01, S2V-01) only support 6s at 720P

IMPORTANT: Prompt Optimization (MUST follow before generating any video)

Before calling any video generation script, you MUST optimize the user's prompt by reading and applying references/video-prompt-guide.md. Never pass the user's raw description directly as --prompt.

Optimization steps:

Apply the Professional Formula: Main subject + Scene + Movement + Camera motion + Aesthetic atmosphere
- BAD: "A puppy in a park"
- GOOD: "A golden retriever puppy runs toward the camera on a sun-dappled grass path in a park, [跟随] smooth tracking shot, warm golden hour lighting, shallow depth of field, joyful atmosphere"
Add camera instructions using [指令] syntax: [推进], [拉远], [跟随], [固定], [左摇], etc.
Include aesthetic details: lighting (golden hour, dramatic side lighting), color grading (warm tones, cinematic), texture (dust particles, rain droplets), atmosphere (intimate, epic, peaceful)
Keep to 1-2 key actions for 6-10 second videos — do not overcrowd with events
For i2v mode (image-to-video): Focus prompt on movement and change only, since the image already establishes the visual. Do NOT re-describe what's in the image.
- BAD: "A lake with mountains" (just repeating the image)
- GOOD: "Gentle ripples spread across the water surface, a breeze rustles the distant trees, [固定] fixed camera, soft morning light, peaceful and serene"
For multi-segment long videos: Each segment's prompt must be self-contained and optimized individually. The i2v segments (segment 2+) should describe motion/change relative to the previous segment's ending frame.

# Text-to-video (default: 10s, 768P)
python scripts/video/generate_video.py \
  --mode t2v \
  --prompt "A golden retriever puppy bounds toward the camera on a sunlit grass path, [跟随] tracking shot, warm golden hour, shallow depth of field, joyful" \
  --output minimax-output/puppy.mp4

# Text-to-video with 1080P (must use --duration 6)
python scripts/video/generate_video.py \
  --mode t2v \
  --prompt "A golden retriever puppy bounds toward the camera" \
  --duration 6 --resolution 1080P \
  --output minimax-output/puppy_hd.mp4

# Image-to-video (prompt focuses on MOTION, not image content)
python scripts/video/generate_video.py \
  --mode i2v \
  --prompt "The petals begin to sway gently in the breeze, soft light shifts across the surface, [固定] fixed framing, dreamy pastel tones" \
  --first-frame photo.jpg \
  --output minimax-output/animated.mp4

# Start-end frame interpolation (sef mode uses MiniMax-Hailuo-02)
python scripts/video/generate_video.py \
  --mode sef \
  --first-frame start.jpg --last-frame end.jpg \
  --output minimax-output/transition.mp4

# Subject reference (face consistency, ref mode uses S2V-01, 6s only)
python scripts/video/generate_video.py \
  --mode ref \
  --prompt "A young woman in a white dress walks slowly through a sunlit garden, [跟随] smooth tracking, warm natural lighting, cinematic depth of field" \
  --subject-image face.jpg \
  --duration 6 \
  --output minimax-output/person.mp4

Long-form Video (Multi-scene)

Multi-scene long videos chain segments together: the first segment generates via text-to-video (t2v), then each subsequent segment uses the last frame of the previous segment as its first frame (i2v). Segments are joined with crossfade transitions for smooth continuity. Default is 10 seconds per segment.

Workflow:

Segment 1: t2v — generated purely from the optimized text prompt
Segment 2+: i2v — the previous segment's last frame becomes first_frame_image, prompt describes motion and change from that ending state
All segments are concatenated with 0.5s crossfade transitions to eliminate jump cuts
Optional: AI-generated background music is overlaid

Prompt rules for each segment:

Each segment prompt MUST be independently optimized using the Professional Formula
Segment 1 (t2v): Full scene description with subject, scene, camera, atmosphere
Segment 2+ (i2v): Focus on what changes and moves from the previous ending frame. Do NOT repeat the visual description — the first frame already provides it
Maintain visual consistency: keep lighting, color grading, and style keywords consistent across segments
Each segment covers only 10 seconds of action — keep it focused

# Example: 3-segment story with optimized per-segment prompts (default: 10s/segment, 768P)
python scripts/video/generate_long_video.py \
  --scenes \
    "A lone astronaut stands on a red desert planet surface, wind blowing dust particles, [推进] slow push in toward the visor, dramatic rim lighting, cinematic sci-fi atmosphere" \
    "The astronaut turns and begins walking toward a distant glowing structure on the horizon, dust swirling around boots, [跟随] tracking from behind, vast desolate landscape, golden light from the structure" \
    "The astronaut reaches the structure entrance, a massive doorway pulses with blue energy, [推进] slow push in toward the doorway, light reflects off the visor, awe-inspiring epic scale" \
  --music-prompt "cinematic orchestral ambient, slow build, sci-fi atmosphere" \
  --output minimax-output/long_video.mp4

# With custom settings
python scripts/video/generate_long_video.py \
  --scenes "Scene 1 prompt" "Scene 2 prompt" \
  --segment-duration 10 \
  --resolution 768P \
  --crossfade 0.5 \
  --music-prompt "calm ambient background music" \
  --output minimax-output/long_video.mp4

Add Background Music

python scripts/video/add_bgm.py \
  --video input.mp4 \
  --generate-bgm --instrumental \
  --music-prompt "soft piano background" \
  --bgm-volume 0.3 \
  --output minimax-output/output_with_bgm.mp4

Template Video

python scripts/video/generate_template_video.py \
  --template-id 392753057216684038 \
  --media photo.jpg \
  --output minimax-output/template_output.mp4

Video Models

Mode	Default Model	Default Duration	Default Resolution	Notes
t2v	MiniMax-Hailuo-2.3	10s	768P	Latest text-to-video
i2v	MiniMax-Hailuo-2.3	10s	768P	Latest image-to-video
sef	MiniMax-Hailuo-02	6s	768P	Start-end frame
ref	S2V-01	6s	720P	Subject reference, 6s only

Media Tools (Audio/Video Processing)

Entry point: scripts/media_tools.py

Standalone FFmpeg-based utilities for format conversion, concatenation, extraction, trimming, and audio overlay. Use these when the user needs to process existing media files without generating new content via MiniMax API.

Video Format Conversion

# Convert between formats (mp4, mov, webm, mkv, avi, ts, flv)
python scripts/media_tools.py convert-video input.webm -o output.mp4
python scripts/media_tools.py convert-video input.mp4 -o output.mov

# With quality / resolution / fps options
python scripts/media_tools.py convert-video input.mp4 -o output.mp4 \
  --crf 18 --preset medium --resolution 1920x1080 --fps 30

Audio Format Conversion

# Convert between formats (mp3, wav, flac, ogg, aac, m4a, opus, wma)
python scripts/media_tools.py convert-audio input.wav -o output.mp3
python scripts/media_tools.py convert-audio input.mp3 -o output.flac \
  --bitrate 320k --sample-rate 48000 --channels 2

Video Concatenation

# Concatenate with crossfade transition (default 0.5s)
python scripts/media_tools.py concat-video seg1.mp4 seg2.mp4 seg3.mp4 -o merged.mp4

# Hard cut (no crossfade)
python scripts/media_tools.py concat-video seg1.mp4 seg2.mp4 -o merged.mp4 --crossfade 0

Audio Concatenation

# Simple concatenation
python scripts/media_tools.py concat-audio part1.mp3 part2.mp3 -o combined.mp3

# With crossfade
python scripts/media_tools.py concat-audio part1.mp3 part2.mp3 -o combined.mp3 --crossfade 1

Extract Audio from Video

# Extract as mp3
python scripts/media_tools.py extract-audio video.mp4 -o audio.mp3

# Extract as wav with higher bitrate
python scripts/media_tools.py extract-audio video.mp4 -o audio.wav --bitrate 320k

Video Trimming

# Trim by start/end time (seconds)
python scripts/media_tools.py trim-video input.mp4 -o clip.mp4 --start 5 --end 15

# Trim by start + duration
python scripts/media_tools.py trim-video input.mp4 -o clip.mp4 --start 10 --duration 8

Add Audio to Video (Overlay / Replace)

# Mix audio with existing video audio
python scripts/media_tools.py add-audio --video video.mp4 --audio bgm.mp3 -o output.mp4 \
  --volume 0.3 --fade-in 2 --fade-out 3

# Replace original audio entirely
python scripts/media_tools.py add-audio --video video.mp4 --audio narration.mp3 -o output.mp4 \
  --replace

Media File Info

python scripts/media_tools.py probe input.mp4

Script Architecture

scripts/
├── check_environment.py          # Env verification
├── env_loader.py                # .env file loader (auto-loaded by all scripts)
├── media_tools.py               # Audio/video conversion, concat, trim, extract
├── tts/
│   ├── generate_voice.py         # CLI entry point
│   ├── sync_tts.py               # Synchronous TTS API
│   ├── async_tts.py              # Async (task-based) TTS API
│   ├── segment_tts.py            # Multi-segment pipeline
│   ├── audio_processing.py       # FFmpeg audio processing
│   ├── voice_clone.py            # Voice cloning API
│   ├── voice_design.py           # Voice design API
│   ├── voice_management.py       # Voice CRUD operations
│   └── utils.py                  # Shared: API config, VoiceSetting, AudioSetting
├── music/
│   ├── generate_music.py         # Music generation CLI
│   └── utils_audio.py            # Audio format utilities
└── video/
    ├── generate_video.py         # Video generation CLI (4 modes)
    ├── generate_long_video.py    # Multi-scene long video
    ├── generate_template_video.py # Template-based video
    └── add_bgm.py               # Background music overlay

References

Read these for detailed API parameters, voice catalogs, and prompt engineering:

tts-guide.md — TTS setup, voice management, audio processing, segment format, troubleshooting
tts-voice-catalog.md — Full voice catalog with IDs, descriptions, and parameter reference
music-api.md — Music generation API: endpoints, parameters, response format
video-api.md — Video API: endpoints, models, parameters, camera instructions, templates
video-prompt-guide.md — Video prompt engineering: formulas, styles, image-to-video tips

About this skill

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LeoYeAI / openclaw-master-skills

skills/minimaxstudio/SKILL.md

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

Illustrative Want to see what minimax-studio 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 Studio

Output Directory

Prerequisites

API Key Configuration

Key Capabilities

TTS (Text-to-Speech)

IMPORTANT: Single voice vs Multi-segment — Choose the right approach

Single-voice generation (DEFAULT)

Multi-segment generation (multi-voice / audiobook / podcast)

Voice management

Audio processing

TTS Models

segments.json Format

IMPORTANT: Multi-Segment Script Generation Rules (Audiobooks, Podcasts, etc.)

Music Generation

IMPORTANT: Instrumental vs Lyrics — When to use which

Music Models

Video Generation

IMPORTANT: Single vs Multi-Segment — Choose the right script

Video Model Constraints (MUST follow)

IMPORTANT: Prompt Optimization (MUST follow before generating any video)

Long-form Video (Multi-scene)

Add Background Music

Template Video

Video Models

Media Tools (Audio/Video Processing)

Video Format Conversion

Audio Format Conversion

Video Concatenation

Audio Concatenation

Extract Audio from Video

Video Trimming

Add Audio to Video (Overlay / Replace)

Media File Info

Script Architecture

References

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Source

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