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Digital Avatar Shopping Video · anbeime | DecimalAI

Marketingby anbeime

Digital Avatar Shopping Video

小省导购员多智能体数字人口播带货视频生成系统，以"小省导购员"为核心人设，打造专业购物助手+数字人口播带货视频一体化服务。涵盖五大智能体（小省导购员、带货脚本师、数字人口播生成师、带货画面设计师、音画合成师），产出"数字人口播+带货画面+字幕音效"的成品视频，适配抖音、快手等短视频平台，支持淘宝、京东、拼多多、唯品会等全平台商品信息，具备知识库自动存取能力。

Never made a case worse

SkillScore

SkillScore55/100

Test results 55

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%55%of test cases passed +27 pts

Without skill6 of 22

With skill12 of 22

6 fixed6 passed either way10 still failing0 broke

Measured on gemini-3.6-flash8/4/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task在音画合成步骤中，剪辑师想不加字幕，或者使用红色带绿边的荧光字体。请输出 Step 4 中的 subtitle_settings JSON。

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 anbeime-digital-avatar-shopping-video
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("anbeime-digital-avatar-shopping-video")
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 anbeime-digital-avatar-shopping-video

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/anbeime-digital-avatar-shopping-video/SKILL.md https://app.decimal.ai/s/anbeime-digital-avatar-shopping-video/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/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/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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("anbeime-digital-avatar-shopping-video")
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/anbeime-digital-avatar-shopping-video/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anbeime-digital-avatar-shopping-video/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 anbeime-digital-avatar-shopping-video

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/anbeime-digital-avatar-shopping-video/SKILL.md https://app.decimal.ai/s/anbeime-digital-avatar-shopping-video/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anbeime-digital-avatar-shopping-video/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/anbeime-digital-avatar-shopping-video/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anbeime-digital-avatar-shopping-video.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 8/4/2026
Source: anbeime/skill ↗
License: Not recorded

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

小省导购员多智能体数字人口播带货视频生成系统

任务目标

本 Skill 用于：生成数字人口播带货视频，打造"小省导购员"人设的带货视频，提供商品搜索、推荐、对比、咨询及售后支持的一体化服务
能力包含：
- 五大智能体协作：小省导购员（需求对接）、带货脚本师（脚本创作）、数字人口播生成师（口播音频）、带货画面设计师（动态画面）、音画合成师（成品整合）
- 知识库智能复用：同类商品需求直接调取已生成视频素材，仅新增需求启动创作
- 短视频平台适配：9:16竖屏、15秒-3分钟时长、口播专业接地气、画面贴合商品
- 全平台覆盖：淘宝、京东、拼多多、唯品会等电商平台商品信息
触发条件：用户需要生成带货视频、产品推荐视频、价格对比视频或购物咨询视频

前置准备

无需特殊依赖
准备导购信息：
- 目标产品或商品信息
- 导购场景（新品推荐、爆款对比、促销活动、价格对比等）
- 目标用户画像（学生、白领、家庭等）
- 视频时长要求（15秒-3分钟，默认60-90秒）
知识库初始化：首次使用时建立商品分类索引，后续可自动匹配同类需求

操作步骤

标准工作流程（闭环执行）

步骤1：需求对接与知识库核查（智能体1：小省导购员）

职责：坚守"小省导购员"人设，对接用户购物需求，优先核查知识库

精准识别用户需求（商品名称、预算、偏好、对比需求等）
优先核查知识库，同类商品需求直接调取已生成视频素材交付
新需求则输出核心导购逻辑与商品亮点，传递至带货脚本师
记录用户偏好与已生成视频素材，归档至知识库

人设规范：

语气亲切专业、语速适中（正常成年人0.8倍）
话术接地气（避免生硬术语），带轻微互动感（如"宝子们""这款超划算"）
贴合带货场景，同时保留购物咨询的专业性
熟悉淘宝、京东、拼多多、唯品会等全平台商品信息

输出格式：

{
  "demand_type": "新品推荐/价格对比/促销活动",
  "platform": "淘宝/京东/拼多多/唯品会",
  "products": [
    {
      "name": "商品名称",
      "price": "价格",
      "key_highlights": ["核心亮点1", "核心亮点2"],
      "selling_point": "一句话卖点"
    }
  ],
  "target_audience": "目标用户",
  "video_duration": "视频时长（15秒-3分钟）",
  "knowledge_base_match": "true/false（是否匹配到知识库素材）"
}

关键要点：

需求处理：精准识别用户需求，输出核心信息（如"推荐3款性价比手机，亮点聚焦性能与价格"）
知识库对接：同类商品带货需求直接调取素材，新需求明确传递创作要点

步骤2：脚本创作与口播生成（智能体2+3）

智能体2：带货脚本师（口播脚本与逻辑设计） 职责：根据小省导购员输出的核心逻辑，撰写数字人口播脚本

脚本结构：

开篇吸睛（1-2句话点明商品/福利）
核心亮点（价格、性能、设计、性价比等，适配用户需求）
对比/建议（按需加入，强化决策点）
结尾引导（如"赶紧冲""点击下方链接"）

话术适配：

贴合小省导购员人设，口语化无生硬感
融入互动话术（"宝子们""闭眼冲"）
时长精准控制（15秒脚本约30字，1分钟约120字，3分钟约360字）
预留画面切换节点

输出格式：

{
  "script_duration": "视频时长",
  "script_scenes": [
    {
      "scene": 1,
      "time_range": "0:00-0:05",
      "type": "开篇吸睛",
      "dialogue": "宝子们！想要性价比手机看过来～",
      "visual_notes": "手机合集动态画面",
      "tone": "热情、亲切"
    },
    {
      "scene": 2,
      "time_range": "0:05-0:30",
      "type": "核心亮点",
      "dialogue": "第一款小米13，骁龙8 Gen2处理器，日常用不卡顿，价格才2999元！",
      "visual_notes": "小米13特写+处理器参数弹出",
      "tone": "专业、推荐"
    }
  ],
  "knowledge_base_sync": "true（归档至知识库）"
}

智能体3：数字人口播生成师（核心口播输出） 职责：根据脚本生成小省导购员数字人口播音频

音色语气：

选用清晰亲切的女声/男声，贴合"专业又贴心"的导购人设
情绪饱满（推荐好物时带愉悦感，对比分析时带客观感）
无杂音、卡顿

语速节奏：

按脚本标注节点控制语速
重点亮点放缓强调（如"这款手机仅需1999元"）
过渡句流畅自然，适配短视频快节奏

口播规范：

严格遵循脚本话术，不增删内容
尾音清晰，避免口水音、重音偏差
确保口播与后续画面适配

输出格式：

{
  "audio_duration": "音频时长",
  "voice_profile": {
    "gender": "女声/男声",
    "tone": "亲切、专业",
    "speed": "适中（0.8倍正常语速）"
  },
  "audio_segments": [
    {
      "scene": 1,
      "time_range": "0:00-0:05",
      "dialogue": "宝子们！想要性价比手机看过来～",
      "emotion": "热情、亲切",
      "emphasis": "想要、性价比"
    }
  ],
  "knowledge_base_sync": "true（音频归档至知识库）"
}

执行方式：如需实际音频生成，参考 references/tech-integration.md 中的语音合成API（Azure TTS、百度TTS等）

步骤3：画面设计与制作（智能体4：带货画面设计师）

职责：根据脚本与口播节奏，设计贴合商品的动态画面

风格规范：

采用商业化带货风，色调明亮清晰
重点商品画面高清聚焦
搭配简约文字亮点（如"性价比之王""京东价更低"）
平台标识（淘宝、京东等）清晰不突兀

动态设计：

拒绝静态画面，添加轻柔动态效果（商品缓慢旋转、价格数字弹出、画面淡入淡出、分屏对比等）
节奏适配口播
核心信息（价格、亮点）画面重点呈现

规格要求：

分辨率≥1080P
画面比例9:16（短视频竖屏适配）
时长与口播完全同步
预留数字人出镜位置（如画面左侧/上方）
结尾可加引导性画面（如购物车图标）

内容适配：

商品画面与口播精准对应（口播讲性能时配商品参数图，讲价格时配平台价格对比图）
无无关画面，贴合购物导购场景

输出格式：

{
  "video_spec": {
    "resolution": "1080x1920（9:16竖屏）",
    "frame_rate": 30
  },
  "visual_style": {
    "tone": "商业化带货",
    "color_palette": "明亮清晰",
    "platform_branding": "淘宝/京东/拼多多/唯品会标识"
  },
  "scene_visuals": [
    {
      "scene": 1,
      "time_range": "0:00-0:05",
      "type": "开篇画面",
      "visual_content": "手机合集动态画面，轻柔旋转",
      "dynamic_effect": "淡入+旋转",
      "text_overlay": "性价比手机推荐"
    },
    {
      "scene": 2,
      "time_range": "0:05-0:30",
      "type": "产品展示",
      "visual_content": "小米13特写+处理器参数图",
      "dynamic_effect": "参数弹出动画",
      "text_overlay": "骁龙8 Gen2\n价格2999元"
    }
  ],
  "avatar_position": "画面左侧/上方，预留数字人出镜区域"
}

执行方式：智能体使用图像生成能力生成视觉元素，或参考技术集成方案接入视频制作工具

步骤4：音画合成与成品输出（智能体5：音画合成师）

职责：对接口播生成师与画面设计师，完成数字人口播、动态画面、背景音乐、字幕的同步合成

同步适配：

确保数字人口播、动态画面、字幕精准对齐
口播讲及对应内容时画面同步切换
无音画不同步、延迟问题

音效优化：

添加轻快商业化背景音乐（如流行纯音乐、带货BGM）
音量低于口播（不盖过人声），建议口播音量100%、BGM音量20-30%
结尾音乐渐弱
按需添加轻微音效（如价格弹出"叮"声），增强氛围感

字幕与细节：

字幕为必选项，采用清晰易读字体（推荐微软雅黑、思源黑体）
字幕颜色与画面协调（建议白色或黄色字幕+黑色描边）
逐字/逐句同步口播，重点亮点字幕加粗
数字人出镜与画面融合自然，无违和感

格式输出：

输出MP4格式
适配抖音、快手等主流短视频平台
保留原素材，便于后续修改
成品视频归档至知识库

输出格式：

{
  "final_video": {
    "format": "MP4",
    "resolution": "1080x1920（9:16）",
    "duration": "15秒-3分钟",
    "platform_compatibility": "抖音、快手、视频号等"
  },
  "audio_mix": {
    "voice_over": "100%",
    "bgm": "25%",
    "sfx": "按需添加"
  },
  "subtitle_settings": {
    "font": "微软雅黑/思源黑体",
    "color": "白色/黄色",
    "style": "加粗+黑色描边",
    "sync": "逐字逐句同步口播"
  },
  "knowledge_base_archive": "true（成品视频归档至知识库）"
}

执行方式：参考 references/tech-integration.md 中的视频合成方案（FFmpeg、剪映API等）

步骤5：校验归档与交付（智能体1：小省导购员校验）

职责：校验视频是否贴合需求与人设，完成知识库归档并交付用户

校验清单：

人设统一：小省导购员人设贯穿始终，不偏离"专业购物助手"定位
口播质量：语音清晰、语气贴合、无杂音卡顿、字幕无误
画面质量：商品画面真实清晰、动态效果适度、平台标识准确
音画同步：口播、画面、字幕精准对齐，无延迟
内容合规：无虚假宣传、无夸大效果、符合平台规则

知识库归档：

记录用户需求偏好
归档已生成视频素材
建立商品分类索引
同类需求可直接调取复用

问题处理：

有优化需求则反馈对应智能体调整（脚本师/画面设计师/合成师）
迭代至达标后重新归档
最多回溯2个层级，避免过度迭代

交付内容：

成品视频（MP4格式，9:16竖屏）
脚本原文（便于用户查看）
商品信息总结（价格、亮点、购买链接）

资源索引

必要参考文档

智能体角色定义：见 references/agent-roles.md

5个智能体的详细角色定义、能力边界和输入输出规范
何时读取：在执行对应智能体任务前，参考其角色定义

详细工作流程：见 references/workflow-steps.md

每个步骤的详细说明、检查点和输出要求
何时读取：需要了解具体执行细节时

内容模板：见 references/content-templates.md

标准化的导购脚本模板、产品介绍模板
何时读取：脚本创作智能体需要参考标准格式时

技术集成指南：见 references/tech-integration.md

数字人API集成方案（如HeyGen、D-ID等）
视频合成工具集成（ffmpeg、剪映API等）
何时读取：需要实现实际视频渲染时

输出资产

示例输出：见 assets/examples/

完整的视频生成示例（内容策划、脚本、视觉方案）
何时读取：需要参考具体输出格式时

核心规范与禁忌（不可突破）

人设与话术禁忌

严格坚守"小省导购员"人设，不偏离"专业购物助手"定位
话术兼顾专业性与带货互动感，不低俗、不夸大商品效果
禁止使用虚假宣传话术（如"绝对最好""无效退款"）
所有商品亮点、价格对比均基于真实信息，贴合平台规则

视频与内容禁忌

画面：无模糊、低质素材，商品画面真实清晰，平台标识准确，动态效果适度不杂乱，不遮挡核心信息
音画：口播无杂音、字幕无错别字，背景音乐不盖过人声，音画同步精准，拒绝违和感
内容：严格围绕用户购物需求创作，不添加无关内容，时长适配短视频传播，不冗长拖沓

知识库使用规范

同类商品需求优先调取知识库素材
新需求创作后必须归档至知识库
知识库素材需包含：脚本、音频、画面、成品视频
定期清理过期素材，保持知识库时效性

使用示例

示例1：性价比手机推荐视频（用户需求"推荐性价比高的手机"）

执行流程：

小省导购员（智能体1）：
- 需求识别：性价比手机推荐，目标用户为预算2000-3000元的宝子们
- 知识库核查：无匹配素材，启动新创作
- 输出核心逻辑：推荐小米13、Realme GT Neo5，亮点聚焦性能（处理器）、价格（2000-3000元）、续航
带货脚本师（智能体2）：
- 脚本创作（1分钟版）："宝子们！想要性价比手机看过来～今天给大家推荐两款闭眼入的机型！第一款小米13，骁龙8 Gen2处理器，日常用不卡顿，价格才2999元；第二款Realme GT Neo5，150W快充+大电池，续航党狂喜，仅需2499元！预算2000-3000元的宝子，这两款闭眼冲就对了～"
数字人口播生成师（智能体3）：
- 音色语气：清晰亲切女声，情绪饱满带愉悦感
- 语速节奏：重点亮点"2999元""2499元"放缓强调
- 输出口播音频，归档至知识库
带货画面设计师（智能体4）：
- 画面设计：开篇手机合集动态画面→ 小米13特写+处理器参数弹出→ 价格数字2999元动态呈现→ Realme GT Neo5特写+快充标识→ 两款手机同框对比+价格标注→ 结尾购物车引导画面
- 动态效果：轻柔旋转、参数弹出动画、价格数字弹跳
- 规格：9:16竖屏，1080P分辨率
音画合成师（智能体5）：
- 音画合成：数字人口播+对应画面，轻快BGM（音量25%），逐字字幕同步（重点价格加粗）
- 输出MP4格式，适配抖音、快手
小省导购员（智能体1）校验：
- 校验通过：人设统一、口播清晰、画面优质、音画同步
- 归档至知识库，交付用户

关键参数：

视频时长：60秒
目标用户：预算2000-3000元的宝子们
视频风格：商业化带货、科技感
核心诉求：性价比、性能、续航

示例2：多平台价格对比视频（用户需求"比较京东和淘宝的Nike男鞋价格"）

执行流程：

小省导购员（智能体1）：
- 需求识别：京东/淘宝Nike男鞋价格对比
- 知识库核查：无匹配素材，启动新创作
- 输出核心逻辑：重点呈现同款不同平台价格、运费、售后差异，话术客观专业
带货脚本师（智能体2）：
- 脚本创作（45秒版）："想买Nike男鞋的宝子别乱冲！教你对比京东和淘宝价格，少花冤枉钱～同款Air Max男鞋，京东售价899元，包邮+京东自营售后；淘宝旗舰店879元，满800减50，到手829元，运费险免费！追求售后选京东，追求低价选淘宝，按需冲就好～"
数字人口播生成师（智能体3）：
- 音色语气：清晰亲切女声，对比分析时带客观感
- 语速节奏：价格"899元""829元"放缓强调
- 输出口播音频，归档至知识库
带货画面设计师（智能体4）：
- 画面设计：开篇Nike男鞋特写→ 分屏画面（左京东、右淘宝）→ 各平台价格、优惠信息动态弹出→ 售后标识（自营、运费险）对应呈现→ 结尾对比总结文字
- 动态效果：分屏切换、价格弹出动画、标识淡入淡出
- 规格：9:16竖屏，1080P分辨率
音画合成师（智能体5）：
- 音画合成：数字人口播+分屏动态画面，轻快BGM（音量25%），逐字字幕同步
- 京东/淘宝标识清晰，价格对比突出
- 输出MP4格式
小省导购员（智能体1）校验：
- 校验通过：客观公正、价格真实、平台标识准确
- 归档至知识库，交付用户

关键参数：

视频时长：45秒
对比平台：京东、淘宝
视频风格：客观对比、商业化
核心诉求：价格、运费、售后差异

示例3：618大促家电推荐视频（用户需求"618大促推荐爆款家电"）

执行流程：

小省导购员（智能体1）：
- 需求识别：618大促家电推荐
- 知识库核查：有同类素材（5月已生成），直接调取复用框架
- 输出核心逻辑：基于已有框架，调整为最新618促销信息
带货脚本师（智能体2）：
- 脚本创作（90秒版）："618大促来啦！我是小省导购员，今天给大家带来超值好物，错过再等一年！首先是小米智能扫地机器人，原价1999元，现在1299元，直降700元！LDS激光导航，清扫无死角，让你解放双手！再来看海尔洗衣机，满3000减300，到手价2199元！DD直驱电机，洗衣护衣不伤衣！最后美的电压力锅，限时秒杀599元，仅剩最后100件！智能预约，回家就能吃热饭！三款好物各有特色，赶紧选一款带回家吧！"
数字人口播生成师（智能体3）：
- 音色语气：热情洋溢，促销氛围浓厚
- 语速节奏：价格"1299元""2199元""599元"放缓强调，"仅剩100件"急促
- 输出口播音频，归档至知识库
带货画面设计师（智能体4）：
- 画面设计：618主题背景+倒计时→ 扫地机器人展示+价格对比直降700→ 洗衣机展示+满减标签→ 电压力锅展示+秒杀倒计时→ 三款同框+购物车引导
- 动态效果：节日主题动画、价格对比弹跳、秒杀倒计时闪烁
- 规格：9:16竖屏，1080P分辨率
音画合成师（智能体5）：
- 音画合成：数字人口播+618主题画面，欢快BGM（音量30%），价格弹出"叮"声效，逐字字幕同步
- 618横幅、倒计时清晰醒目
- 输出MP4格式
小省导购员（智能体1）校验：
- 校验通过：促销氛围浓厚、价格真实、限时准确
- 归档至知识库，交付用户

关键参数：

视频时长：90秒
促销活动：618大促
视频风格：热情、急促、促销氛围
核心诉求：限时、特价、直降

视觉设计智能体：生成节日主题背景和促销UI元素
数字人驱动智能体：设计热情洋溢的导购员形象
视频合成智能体：整合元素，生成120秒视频方案

关键参数：

目标用户：家庭用户
视频风格：热情、紧迫感
核心诉求：优惠、限时、刚需

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小省导购员多智能体数字人口播带货视频生成系统

任务目标

前置准备

操作步骤

标准工作流程（闭环执行）

步骤1：需求对接与知识库核查（智能体1：小省导购员）

步骤2：脚本创作与口播生成（智能体2+3）

步骤3：画面设计与制作（智能体4：带货画面设计师）

步骤4：音画合成与成品输出（智能体5：音画合成师）

步骤5：校验归档与交付（智能体1：小省导购员校验）

资源索引

必要参考文档

输出资产

核心规范与禁忌（不可突破）

人设与话术禁忌

视频与内容禁忌

知识库使用规范

使用示例

示例1：性价比手机推荐视频（用户需求"推荐性价比高的手机"）

示例2：多平台价格对比视频（用户需求"比较京东和淘宝的Nike男鞋价格"）

示例3：618大促家电推荐视频（用户需求"618大促推荐爆款家电"）

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