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narrator-ai-cli — AI Video Narration CLI Skill · narratorai-studio | DecimalAI

Design & Mediaby narratorai-studio

narrator-ai-cli — AI Video Narration CLI Skill

AI 电影/短剧解说视频自动生成（AI 解说大师 CLI Skill）。当用户需要创建电影解说视频、短剧解说、影视二创、AI 配音旁白视频、film commentary、video narration、drama dubbing、movie narration 时触发。内置电影素材库、BGM、多语种配音、解说模板。通过 narrator-ai-cli 命令行实现：搜片→选模板→选 BGM→选配音→生成文案→合成视频的全流程自动化。CLI client for Narrator AI video narration API.

Never made a case worse

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%86%of test cases passed +59 pts

Without skill6 of 22

With skill19 of 22

13 fixed6 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/12/2026v120 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI received `{"task_id": "e3b0c44298fc1c149afbf4c8996fb924", "task_order_num": "fast_writing_clip_data_88888"}` from clip creation. Can I pass `e3b0c44298fc1c149afbf4c8996fb924` as the `order_num` argument to video-composing?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

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 narratorai-studio-narrator-ai-cli
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("narratorai-studio-narrator-ai-cli")
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 narratorai-studio-narrator-ai-cli

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/narratorai-studio-narrator-ai-cli/SKILL.md https://app.decimal.ai/s/narratorai-studio-narrator-ai-cli/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/narratorai-studio-narrator-ai-cli/SKILL.md

This gets SKILL.md only. The skill's 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/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/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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("narratorai-studio-narrator-ai-cli")
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/narratorai-studio-narrator-ai-cli/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/narratorai-studio-narrator-ai-cli/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 4 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 narratorai-studio-narrator-ai-cli

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/narratorai-studio-narrator-ai-cli/SKILL.md https://app.decimal.ai/s/narratorai-studio-narrator-ai-cli/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/narratorai-studio-narrator-ai-cli/SKILL.md

This gets SKILL.md only. The skill's 4 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/narratorai-studio-narrator-ai-cli/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/narratorai-studio-narrator-ai-cli.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/12/2026
Source: narratorai-studio/narrator-ai-cli-skill ↗
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

narrator-ai-cli — AI Video Narration CLI Skill

CLI client for Narrator AI video narration API. Designed for AI agents and developers.

CLI repo: https://github.com/NarratorAI-Studio/narrator-ai-cli
Resources preview (BGM / dubbing / templates): https://ceex7z9m67.feishu.cn/wiki/WLPnwBysairenFkZDbicZOfKnbc

Reference Index

This file covers decision flow, the common workflow, and pointers. Detailed lookups live in references/:

Topic	File
Resource selection (material / BGM / dubbing / templates) — list commands, response formats, field mapping	`references/resources.md`
Full workflow steps with parameter tables and JSON examples (Fast Path + Standard Path)	`references/workflows.md`
Magic Video — optional visual template step (catalog, params, language rules)	`references/magic-video.md`
Polling pattern, task types, file ops, user account, error codes	`references/operations.md`

Pipeline at a Glance

                    ┌─── Fast Path (原创文案, cheaper) ───┐
                    │   fast-writing → fast-clip-data     │
  Source material ──┤              ↓                      ├──→ video-composing ──→ (magic-video)
  (material list /  │   [video-composing keys off         │   final MP4 URL       optional visual
   search-movie /   │    fast-clip-data.task_order_num]   │                        template pass
   file upload)     └─────────────────────────────────────┘
                    ┌─── Standard Path (二创文案) ────────┐
                    │   popular-learning → generate-      │
                    │   writing → clip-data               │
                    │              ↓                      │
                    │   [video-composing keys off         │
                    │    generate-writing.task_order_num] │
                    └─────────────────────────────────────┘

Agent Rules (mandatory — apply across all steps)

Always:

Confirm before acting. Every resource (source, BGM, dubbing, template) and every magic-video submission requires explicit user approval. Never auto-select, never auto-submit.

Source data, never invent. Construct confirmed_movie_json from material list fields or task search-movie output. If neither yields it, ask the user — do not fabricate.

Honor the language chain. The dubbing voice's language defines the writing task language param AND every magic-video text param. All three must match. → references/magic-video.md § Language Awareness

Paginate material list to exhaustion, search programmatically. Fetch all pages until total is consumed, then grep -i or python3 -c on the JSON. Never trust truncated terminal display.

Poll with the canonical while loop at 5-second intervals. Never use a fixed-iteration for loop. → references/operations.md § Task Polling

Never:

Submit magic-video without showing the full request body (templates + every template_params value) and getting user confirmation. The cost is 30 pts/minute and irreversible.

Submit Chinese default values for magic-video text params when narration language is non-Chinese. The defaults are hardcoded Chinese and will appear as Chinese text in a non-Chinese video.

Submit .task_id (32-char hex) as order_num. Downstream tasks want .task_order_num (the prefixed string like generate_writing_xxxxx), not .task_id. Submitting the hex returns 10001 任务关联记录数据异常. The other look-alike — .results.order_info.order_num (script_xxxxx) — is also wrong; see references/operations.md § Task Query Response Shape.

Auto-switch paths after a failure. If a step fails, surface the error to the user and ask explicitly: retry the same path, switch to the other path, or abort. Never infer a path switch on the agent's own initiative.

Prerequisites

This skill assumes the narrator-ai-cli binary is installed and configured with a valid NARRATOR_APP_KEY. See README.md for install / setup. Agents can verify with narrator-ai-cli user balance.

Core Concepts

Concept	Description
file_id	32-char hex string for uploaded files. Via `file upload` or task results
task_id	32-char hex string returned on task creation. Poll with `task query`
task_order_num	Assigned after task creation. Used as `order_num` for downstream tasks
files[]	Output files in the completed task response (flat, top-level array). Each entry has `file_id`, `file_path`, `suffix`. Read `.files[0].file_id` for the next step's input
learning_model_id	Narration style model — from a pre-built template (90+) or `popular-learning` result
learning_srt	Reference SRT file_id. Mutually exclusive with `learning_model_id`

Conversation Initiation

⚠️ Agent behavior — first message of a session: Before asking the user for a movie title or workflow path, proactively orient them about what the skill offers. Most users assume they need to upload their own video + SRT and don't realize a pre-built material library ships with the skill. Skipping this step often results in unnecessary uploads or aborted sessions.

Required opening (adapt to the conversation language):

Lead with the pre-built material library. Mention upfront that ~100 ready-to-use movies are available with video + SRT already loaded — no upload needed in most cases.
Offer three concrete entry points (let the user pick one):
- "I have a specific movie in mind" → take the title, search materials first, fall back to task search-movie only if not found
- "Show me what's available" → run material list --json and present 5–8 titles spanning varied genres; offer to filter by genre on request
- "I'll upload my own video + SRT" → guide through file upload
Defer the Fast vs Standard path question until source material is confirmed. Asking both at once forces a decision the user has no context for yet.
Optionally share the visual resources preview link (BGM / dubbing / templates browsable visually): https://ceex7z9m67.feishu.cn/wiki/WLPnwBysairenFkZDbicZOfKnbc — but only if the user wants to browse, not as a wall of links upfront.

Example opening (Chinese conversation):

你好，欢迎使用 AI 解说大师。这个技能可以帮你生成电影/短剧解说视频。我这边内置了约 100 部电影素材（视频 + 字幕都是现成的），所以大多数情况你不需要自己上传任何文件。

你想怎么开始？

直接告诉我片名 — 我先查内置素材库，没有再去外部搜

让我列一些内置素材 — 你可以按类型挑（喜剧 / 动作 / 悬疑 / 科幻…）

自己上传视频 + 字幕 — 我引导你完成上传流程

After source material is confirmed, walk the user through the decision sequence below — one question per turn, in order. Do NOT collapse multiple decisions into one message; users cannot reason about target_mode before they've picked a path.

Decision sequence (each step waits for explicit user confirmation):

Source material — covered above.
Workflow path — Fast (原创文案) or Standard (二创文案). See "Two Workflow Paths" below.
target_mode — only ask if path = Fast. Choose mode 1 / 2 / 3 (see "Fast Path internal: target_mode" below). If path = Standard, skip this question entirely — Standard Path has no target_mode.
BGM → Dubbing voice → Narration template — see "Resource Selection Protocol".

⚠️ Anti-pattern (do NOT do this): Asking "① 解说模式 (纯解说/原声混剪) ② 制作路线 (快速/标准)" in the same message. 纯解说 and 原声混剪 are Fast Path internal modes (target_mode 1 vs 2). They do not exist in Standard Path. Asking them alongside the path choice forces the user to make decisions in the wrong order and conflates two layers of the decision tree.

Two Workflow Paths

Two end-to-end paths produce a finished narrated video. Choose with the user before starting.

	Fast Path (原创文案, recommended)	Standard Path (二创文案)
Pipeline	material → fast-writing → fast-clip-data → video-composing → magic-video*	material → popular-learning** → generate-writing → clip-data → video-composing → magic-video*
Cost / speed	Faster, cheaper	Higher quality narration
When to use	Default unless user wants adapted-style narration	When user wants narration learned from a reference style

* magic-video is optional; only on explicit user request. ** popular-learning is skippable when using a pre-built template (recommended).

⚠️ Path is a standalone decision — ask the user "Fast or Standard?" by itself, in its own message. Do not auto-select. Do not bundle it with target_mode or any other follow-up question.

⚠️ Path choice is per-movie, evaluated fresh each time. If the user switched paths for a previous movie in the same session (e.g. from Fast to Standard due to a failure), that choice has no bearing on the current movie. Always ask the path question anew for each new movie — do not carry over or infer the prior session's path.

Fast Path internal: `target_mode` (ask only after path=Fast is confirmed)

Skip this section entirely if the user picked Standard Path — target_mode only exists inside fast-writing.

Mode	Use when	Required input
`"1"` 热门影视 (纯解说)	Known movie, narration from plot only	`confirmed_movie_json`; no `episodes_data`
`"2"` 原声混剪 (Original Mix)	Known movie + you have its SRT	`confirmed_movie_json` + `episodes_data[{srt_oss_key, num}]`
`"3"` 冷门/新剧 (New Drama)	Obscure/new content	`episodes_data[{srt_oss_key, num}]`; `confirmed_movie_json` optional

Resource Selection Protocol

Before any task, gather these resources in this order, with explicit user confirmation at each step:

Source files (video + SRT) — from material list or via file upload
BGM — from bgm list
Dubbing voice — from dubbing list
Narration style template — from task narration-styles

Detailed list commands, response shapes, and field mappings live in references/resources.md.

⚠️ Universal rules — apply at every resource step:

Pre-filter by context. Use the per-resource filter flag where supported: bgm list --search, dubbing list --lang, task narration-styles --genre. material list does NOT accept these flags — paginate the JSON and search programmatically with grep -i / python3 -c.

Default presentation: 5–8 options with the resource ID and key descriptive fields.

If the user has no preference: present 3 recommendations with a one-line reason for each. Still wait for confirmation.

Confirm one resource at a time. Do not advance until the current one is confirmed.

⚠️ Dubbing → writing language mismatch check: if the user pre-specified a language value that conflicts with the chosen voice, surface the mismatch and ask before proceeding. (The general language-chain rule lives in Agent Rules above.)

Fast Path — High-Level Flow

Detailed parameter tables, all target_mode cases, and full JSON examples live in references/workflows.md.

Step 0 — Find source material & determine target_mode:

List materials: narrator-ai-cli material list --json --page 1 --size 100. Search programmatically with grep -i or python3 -c on the JSON output — do NOT rely on the terminal display (may be truncated). Paginate (--page 2, etc.) until exhausted if total > 100.
Found in materials → ask user: pure narration (target_mode=1) or original mix (target_mode=2)? Construct confirmed_movie_json from material fields (mapping in references/resources.md).
Not found, known title → task search-movie "<name>" --json → target_mode=1 (or target_mode=2 if user uploads SRT). May take 60+ seconds (Gradio backend, results cached 24h).
Obscure / new content → target_mode=3 with user's uploaded SRT. confirmed_movie_json optional.

Step 1 — fast-writing: pass learning_model_id, target_mode, playlet_name, confirmed_movie_json and/or episodes_data, model (pricing: 纯解说文案 flash 5pts/1k-chars or pro 15pts/1k-chars; 原片混剪解说文案 flash 12pts/1k-chars or pro 40pts/1k-chars). Save task_id from the creation response, then poll until top-level .status=2 and save .files[0].file_id from the completed task.

Step 2 — fast-clip-data: pass task_id + file_id from Step 1, plus bgm, dubbing, dubbing_type, and episodes_data with video_oss_key / srt_oss_key / negative_oss_key. Poll until top-level .status=2; read top-level .task_order_num from the response.

Step 3 — video-composing: pass order_num: <.task_order_num from Step 2> only. Poll → .results.tasks[0].video_url is the finished MP4.

Step 4 (optional) — magic-video: only on explicit user request. See references/magic-video.md.

Standard Path — High-Level Flow

Detailed parameter tables and JSON examples live in references/workflows.md.

Step 0 — Source material: same material/upload flow as Fast Path. Use video_file_id as video_oss_key and negative_oss_key, and srt_file_id as srt_oss_key in episodes_data.

Step 1 — popular-learning (skip if using a pre-built template): pass video_srt_path, narrator_type, model_version. Poll until top-level .status=2, then parse .results.tasks[0].task_result JSON → agent_unique_code is the learning_model_id. Or use a pre-built template id from task narration-styles --json directly.

Step 2 — generate-writing: pass learning_model_id, playlet_name, playlet_num, episodes_data, plus three additional required fields — target_platform (e.g. "douyin"), vendor_requirements ("" if none), and target_character_name ("" if not applicable). Omitting any of these returns 10001 ... Field required. Full param table in references/workflows.md. Save task_id from the creation response.

Step 3 — clip-data: pass order_num (= top-level .task_order_num from Step 2's polled task record, e.g. generate_writing_xxxxx), plus bgm, dubbing, dubbing_type. ⚠️ Different from Fast Path's fast-clip-data, which takes task_id — clip-data takes order_num instead. Poll until top-level .status=2 (required prerequisite for Step 4) — but do not use clip-data's own task_order_num for video-composing; Step 4 keys off generate-writing's instead.

Step 4 — video-composing: pass order_num + bgm + dubbing + dubbing_type (all four required — re-pass the BGM/voice values from Step 3; the API does not inherit them, and submitting only order_num returns 10001 查询解说工程任务结果失败). ⚠️ Standard Path keys off generate-writing's task_order_num (generate_writing_xxxxx), NOT clip-data's. clip-data must reach top-level .status=2 first as a prerequisite, but its own task_order_num (generate_clip_data_xxxxx) returns 10001 任务关联记录信息缺失 when submitted. This is opposite to Fast Path (where fast-clip-data is the right anchor) — see Important Notes #4. Poll → .results.tasks[0].video_url is the finished MP4.

Step 5 (optional) — magic-video: only on explicit user request. See references/magic-video.md.

Standalone Tasks

# Voice clone — input audio_file_id, returns voice_id
narrator-ai-cli task create voice-clone --json -d '{"audio_file_id": "<file_id>"}'

# Text to speech — input voice_id + audio_text
narrator-ai-cli task create tts --json -d '{"voice_id": "<voice_id>", "audio_text": "Text to speak"}'

Both accept optional clone_model (default: pro).

Important Notes

confirmed_movie_json is required for target_mode=1 and 2, optional for 3. Construct from material fields when found in pre-built materials; use search-movie otherwise.
file_id always comes from file list or material list. Never guess.
search-movie may take 60+ seconds (Gradio backend, results cached 24h).
video-composing.order_num is path-asymmetric — which upstream task's task_order_num to use differs by path (the field-name rule — use task_order_num, not the hex order_num — is in Agent Rules above):
- Fast Path → use fast-clip-data's task_order_num (format: fast_writing_clip_data_xxxxx).
- Standard Path → use generate-writing's task_order_num (format: generate_writing_xxxxx). The clip-data step's own task_order_num (generate_clip_data_xxxxx) returns 10001 任务关联记录信息缺失. clip-data must still complete first as a prerequisite — but its order is not what video-composing keys off.
Prefer pre-built templates over popular-learning. List with task narration-styles --json; preview at the resources URL above.
Use -d @file.json for large request bodies to avoid shell quoting issues.
Use task verify before expensive tasks to catch missing/invalid materials early; task budget to estimate point cost.

Data & Privacy

API endpoint: All requests go to https://openapi.jieshuo.cn. No third-party services.
File upload: presigned URL → OSS PUT → callback. Files are bound to your account, not public.
Credentials: NARRATOR_APP_KEY stored at ~/.narrator-ai/config.yaml. Keep private; do not commit.
Scope: this skill only orchestrates the CLI; it does not access files outside what you explicitly pass as input.

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narrator-ai-cli — AI Video Narration CLI Skill

Reference Index

Pipeline at a Glance

Agent Rules (mandatory — apply across all steps)

Prerequisites

Core Concepts

Conversation Initiation

Two Workflow Paths

Fast Path internal: target_mode (ask only after path=Fast is confirmed)

Resource Selection Protocol

Fast Path — High-Level Flow

Standard Path — High-Level Flow

Standalone Tasks

Important Notes

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Fast Path internal: `target_mode` (ask only after path=Fast is confirmed)