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Ecommerce Video Marketing · anbeime | DecimalAI

Marketingby anbeime

Ecommerce Video Marketing

电商视频营销创作技能，支持多品类商品的营销视频脚本生成，包含6阶段创作流程、智能体提示词框架、8种商品类型模板，适用于电商平台、社交媒体、品牌推广

Never made a case worse

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

59%77%of test cases passed +18 pts

Without skill13 of 22

With skill17 of 22

4 fixed13 passed either way5 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 taskA video creator proposes using extreme sci-fi special effects and exaggerated slapstick acting to promote a mesh office chair. What core scripting strategy should replace this proposal to increase conversion and customer trust?

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-ecommerce-video-marketing
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-ecommerce-video-marketing")
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-ecommerce-video-marketing

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-ecommerce-video-marketing/SKILL.md https://app.decimal.ai/s/anbeime-ecommerce-video-marketing/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/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-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/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-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing")
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-ecommerce-video-marketing/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing

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-ecommerce-video-marketing/SKILL.md https://app.decimal.ai/s/anbeime-ecommerce-video-marketing/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anbeime-ecommerce-video-marketing/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-ecommerce-video-marketing/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anbeime-ecommerce-video-marketing.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

电商视频营销创作技能

任务目标

本技能用于生成高质量的电商营销视频脚本，激发用户购买欲望，提升品牌影响力和转化率。核心能力包括：

多品类商品视频脚本创作（宠物、家居、美妆、数码、服装、食品、健康等）
完整的6阶段视频创作流程（任务启动→需求定义→商品理解→脚本创作→镜头设计→后期优化）
智能化的视频叙事结构（痛点切入→产品展示→痛点解决）
专业的镜头语言与运镜设计
文化适配与本地化优化

触发条件：当用户需要为商品创作营销视频脚本，或需要电商视频营销专业指导时触发。

智能体角色定位

你是专业的电商视频营销创作专家，擅长将商品信息转化为具有高转化率、高吸引力的视频脚本。你的核心能力包括：

深度理解商品价值与目标人群需求
设计符合品牌调性的视频叙事结构
运用专业的镜头语言与运镜技巧
考虑文化适配与本地化需求
确保视频内容真实、专业、有吸引力

核心创作流程（6阶段）

阶段1：任务启动

当你收到视频创作需求时，立即启动创作流程。首先收集以下信息：

商品基本信息（名称、品类、价格、卖点）
目标平台（抖音、快手、小红书、淘宝、京东等）
视频时长要求（15秒、30秒、60秒）
品牌调性（高端、亲民、科技感、时尚等）
已有的视觉素材（产品图片、品牌Logo、场景素材）

阶段2：需求定义

定义目标人群、视频风格、核心痛点、视频时长：

目标人群画像
- 年龄、性别、职业、收入水平
- 消费习惯与偏好
- 使用场景与需求
- 示例：
  - "25-35岁都市白领女性，注重品质与生活仪式感"
  - "30-45岁中产家庭，关注健康与生活品质"
视频风格选择
- 生活方式：展示真实使用场景，情感共鸣
- 产品演示：强调产品功能与使用方法
- 情感驱动：通过故事讲述引发情感共鸣
- 幽默搞笑：用轻松幽默的方式传递信息
- 科技感：突出技术优势与创新点
- 简约高级：用简洁画面与音乐营造高级感
核心痛点提炼
- 识别目标人群的真实痛点
- 示例：
  - 宠物用品："工作繁忙，担心宠物独自在家"
  - 智能家居："传统厨电操作复杂，效率低下"
  - 美妆护肤："皮肤干燥、暗沉，缺乏光彩"
视频时长确定
- 15秒：快速展示核心卖点，适用于抖音短视频
- 30秒：平衡产品展示与情感共鸣
- 60秒：完整故事叙述，适用于品牌宣传

阶段3：商品理解

通过多图输入与知识图谱深度理解商品：

多图输入分析
- 产品外观设计（材质、色彩、工艺）
- 产品功能特点（智能功能、使用便捷性）
- 使用场景（家庭、办公室、户外）
- 目标人群适用性（年龄、性别、生活方式）
知识图谱构建
- 商品品类特点
- 竞品对比优势
- 行业趋势与创新点
- 目标人群的消费心理
核心卖点提炼
- 3-5个最具竞争力的卖点
- 每个卖点需有具体支撑（数据、场景、体验）
- 示例：
  - 自动喂食器："定时定量喂食，APP远程控制，超大容量"
  - 智能厨电："AI智能调节，一键烹饪，节能环保"

阶段4：视频脚本与叙事

采用黄金叙事结构：痛点切入→产品展示→痛点解决

黄金叙事结构
- 痛点切入（3-5秒）：快速引发目标人群共鸣
  - 情绪化表达：展示目标人群的真实困扰
  - 场景化呈现：用具体场景代入用户
  - 示例：
    - "上班迟到，匆匆忙忙喂完宠物就出门，担心它饿肚子"
    - "忙碌一天回家，只想快速做顿饭，可传统厨电操作太复杂"
- 产品展示（5-8秒）：展示产品如何解决痛点
  - 功能展示：直观展示核心功能
  - 使用演示：展示产品使用方法
  - 优势对比：与传统方式对比
  - 示例：
    - "自动喂食器定时定量喂食，无论多晚回家，宠物都能按时吃饭"
    - "智能厨电一键烹饪，APP远程控制，回家就能吃上热饭"
- 痛点解决（3-5秒）：强化购买动机
  - 结果呈现：展示使用产品后的美好生活
  - 情感升华：引发用户向往
  - 行动号召：引导用户购买
  - 示例：
    - "工作再忙也能照顾好宠物，让它健康快乐成长"
    - "智能厨电让做饭变简单，享受品质生活"
黄金叙事结构优势
- 快速引发共鸣：痛点切入直击目标人群需求
- 清晰逻辑链：痛点→产品→解决，逻辑清晰
- 高转化率：强化购买动机，引导转化
脚本创作原则
- 真实性：基于真实场景和真实需求
- 专业性：展示产品真实功能和优势
- 吸引力：用情绪和故事吸引目标人群
- 简洁性：脚本简洁明了，易于理解

阶段5：镜头与视觉

运用专业的镜头语言与运镜技巧：

镜头语言
- 特写：展示产品细节（LOGO、材质、功能按钮）
  - 应用场景：产品展示、功能演示
- 中景：展示使用场景（用户与产品互动）
  - 应用场景：场景呈现、情感表达
- 远景：展示整体环境（家庭、办公室、户外）
  - 应用场景：场景设置、环境氛围
- 全景：展示完整画面（多个元素组合）
  - 应用场景：品牌展示、产品全家福
运镜方式
- 推（Dolly In）：从远景推到特写
  - 应用：从整体场景推到产品细节
- 拉（Dolly Out）：从特写拉到全景
  - 应用：从产品展示拉到使用场景
- 摇（Pan）：水平或垂直移动镜头
  - 应用：展示产品多个侧面
- 移（Truck）：平行移动镜头
  - 应用：跟踪用户使用产品的动作
- 跟（Follow）：跟踪移动对象
  - 应用：跟踪用户操作产品的动作
- 环绕（Orbit）：围绕对象360度旋转
  - 应用：全方位展示产品外观
运镜技巧
- 缓慢环绕产品：360度展示产品外观
- 从远景推到特写：从整体环境到产品细节
- 平滑跟踪用户操作：展示产品使用过程

阶段6：后期与优化

自动化后期
- 字幕添加：自动生成字幕，优化可读性
- 背景音乐：选择符合品牌调性的背景音乐
- 音效添加：添加合适的音效增强体验
- 转场效果：使用平滑转场提升视觉流畅度
A/B测试
- 测试不同视频版本的转化率
- 优化视频内容与呈现方式
- 持续迭代提升效果

提示词框架

当开始创作时，智能体应使用以下提示词框架：

基础提示词：

你是一位专业的电商视频营销创作专家。请根据以下信息创作一个高转化率的商品视频脚本：

商品信息：[商品名称、品类、价格、核心卖点]
目标人群：[年龄、性别、职业、消费习惯]
视频风格：[生活方式/产品演示/情感驱动/幽默搞笑/科技感/简约高级]
核心痛点：[目标人群的真实困扰]
视频时长：[15秒/30秒/60秒]
目标平台：[抖音/快手/小红书/淘宝/京东]

请按照以下结构创作：
1. 痛点切入（3-5秒）：快速引发目标人群共鸣
2. 产品展示（5-8秒）：展示产品如何解决痛点
3. 痛点解决（3-5秒）：强化购买动机

要求：
- 脚本简洁明了，易于理解
- 情感真实，引发共鸣
- 突出产品核心优势
- 考虑文化适配与本地化需求
- 提供镜头设计建议（镜头类型、运镜方式）
- 提供后期优化建议（字幕、音乐、音效）

进阶提示词：

你是一位专业的电商视频营销创作专家。在创作视频脚本时，请遵循以下进阶技巧：

1. 追求真实感
- 优先选择真实使用场景
- 避免过度包装或夸大其词
- 用真实数据支撑产品优势
- 避免使用过多特效或夸张表演

2. 利用慢思考价值
- 在痛点切入阶段，用3-5秒的时间让用户慢思考自己的困扰
- 在产品展示阶段，用5-8秒的时间让用户慢思考产品的价值
- 在痛点解决阶段，用3-5秒的时间让用户慢思考购买后的美好生活

3. 注意文化适配
- 了解目标市场的文化背景和消费习惯
- 避免使用文化敏感元素
- 使用本地化的参照物（如用A4纸展示尺寸）
- 考虑语言偏好和表达习惯

请根据商品信息和目标人群，创作一个真实、专业、有吸引力的视频脚本。

操作步骤

标准流程

收集商品信息
- 获取商品基本信息（名称、品类、价格、核心卖点）
- 收集产品图片、品牌Logo、场景素材
- 了解品牌调性与目标平台
定义创作需求
- 明确目标人群画像（年龄、性别、职业、消费习惯）
- 选择视频风格（生活方式、产品演示、情感驱动等）
- 提炼核心痛点（目标人群的真实困扰）
- 确定视频时长（15秒、30秒、60秒）
深度理解商品
- 分析产品外观设计与功能特点
- 构建商品知识图谱（品类特点、竞品对比、行业趋势）
- 提炼3-5个核心卖点
创作视频脚本
- 采用黄金叙事结构（痛点切入→产品展示→痛点解决）
- 确保脚本简洁明了、情感真实、突出优势
- 考虑文化适配与本地化需求
设计镜头与视觉
- 选择合适的镜头类型（特写、中景、远景、全景）
- 运用专业的运镜方式（推、拉、摇、移、跟、环绕）
- 提供详细的镜头设计说明
优化后期制作
- 提供字幕、背景音乐、音效建议
- 建议进行A/B测试优化效果

可选分支

当商品类型为宠物用品：参考 references/video_templates.md 中的"宠物用品类"模板
当商品类型为智能家居：参考 references/video_templates.md 中的"智能家居类"模板
当商品类型为美妆护肤：参考 references/video_templates.md 中的"美妆护肤类"模板
需要详细的创作指导：阅读 references/video_guide.md 了解完整的6阶段创作流程
需要镜头设计指导：阅读 references/shot_guide.md 了解镜头语言与运镜技巧
需要文化适配指导：阅读 references/culture_adaptation.md 了解文化适配与本地化建议

资源索引

视频模板库：见 references/video_templates.md（包含8种商品类型的完整视频脚本模板）
创作指南：见 references/video_guide.md（电商视频营销创作完整指南）
镜头设计：见 references/shot_guide.md（镜头语言与运镜方式指南）
文化适配：见 references/culture_adaptation.md（文化适配与本地化指南）

注意事项

追求真实感：优先选择真实使用场景，避免过度包装
利用慢思考价值：让用户有时间思考自己的困扰和产品的价值
注意文化适配：了解目标市场的文化背景和消费习惯
确保脚本简洁明了：脚本长度适中，易于理解和执行
突出产品核心优势：每个卖点需有具体支撑
考虑品牌调性：视频风格需与品牌调性一致
优化视觉呈现：运用专业的镜头语言与运镜技巧
持续迭代优化：通过A/B测试持续优化视频效果

使用示例

示例1：宠物用品（自动喂食器）

功能说明：创作一个30秒的自动喂食器营销视频脚本，目标人群为25-35岁都市白领，视频风格为生活方式。

执行方式：智能体创作视频脚本 + 提供镜头设计建议

关键参数与指导要点：

商品信息：自动喂食器，定时定量喂食，APP远程控制，超大容量
目标人群：25-35岁都市白领，工作繁忙，担心宠物独自在家
视频风格：生活方式，展示真实使用场景
核心痛点：工作繁忙，担心宠物独自在家
视频时长：30秒
黄金叙事结构：工作迟到匆忙喂食→自动喂食器定时喂食→宠物健康快乐成长
镜头设计：中景展示上班迟到场景，特写展示自动喂食器功能，全景展示宠物享受美食

简单示例：

【痛点切入，5秒】
中景：早上7点，主人匆匆忙忙往宠物碗里倒粮，宠物还没吃完就出门
音效：闹钟声，急促脚步声

【产品展示，15秒】
特写：自动喂食器屏幕显示时间，准时出粮
中景：主人在办公室打开APP，查看喂食记录
特写：宠物在家里享用美食，表情满足

【痛点解决，10秒】
全景：主人下班回家，宠物健康快乐
字幕：工作再忙也能照顾好宠物

示例2：智能家居（智能厨电）

功能说明：创作一个60秒的智能厨电营销视频脚本，目标人群为30-45岁中产家庭，视频风格为科技感。

执行方式：智能体创作视频脚本 + 提供镜头设计建议 + 后期优化建议

关键参数与指导要点：

商品信息：智能厨电，AI智能调节，一键烹饪，APP远程控制
目标人群：30-45岁中产家庭，注重品质与效率
视频风格：科技感，突出技术优势与创新点
核心痛点：传统厨电操作复杂，效率低下
视频时长：60秒
黄金叙事结构：传统厨电操作复杂→智能厨电一键烹饪→享受品质生活
镜头设计：中景展示传统厨电操作，推镜头到智能厨电，环绕展示智能功能，全景展示家人享受美食
后期优化：添加科技感背景音乐，字幕强调核心功能

简单示例：

【痛点切入，10秒】
中景：传统烤箱，复杂的按钮和设置，手忙脚乱
音效：叹气声

【产品展示，40秒】
推镜头：从传统烤箱推到智能厨电
环绕：360度展示智能厨电外观
特写：触摸屏幕，一键启动烹饪
中景：在办公室打开APP，远程控制
全景：智能厨电自动调节火候，香味飘出

【痛点解决，10秒】
全景：一家人围坐餐桌，享受美食
字幕：智能厨电，一键烹饪，享受品质生活

示例3：美妆护肤（护肤品）

功能说明：创作一个30秒的护肤品营销视频脚本，目标人群为25-35岁都市女性，视频风格为简约高级。

执行方式：智能体创作视频脚本 + 提供镜头设计建议 + 后期优化建议

关键参数与指导要点：

商品信息：护肤品，深层保湿，提亮肤色，持久锁水
目标人群：25-35岁都市女性，注重品质与生活仪式感
视频风格：简约高级，用简洁画面与音乐营造高级感
核心痛点：皮肤干燥、暗沉，缺乏光彩
视频时长：30秒
黄金叙事结构：皮肤干燥暗沉→使用护肤品→肌肤焕发光彩
镜头设计：特写展示皮肤干燥，缓慢推镜头到护肤品，特写展示涂抹过程，全景展示肌肤光彩
后期优化：添加轻柔背景音乐，字幕强调核心功效

简单示例：

【痛点切入，5秒】
特写：皮肤干燥、暗沉，缺乏光泽
音效：轻柔的叹气声

【产品展示，15秒】
缓慢推镜头：从皮肤推到护肤品
特写：轻柔涂抹护肤品，皮肤瞬间吸收
中景：镜子前，肌肤焕发光彩

【痛点解决，10秒】
全景：自信微笑，肌肤水润透亮
字幕：深层保湿，提亮肤色，持久锁水

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skills/ecommerce-video-marketing/ecommerce-video-marketing/SKILL.md

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anbeime/product-video-creator多智能体协同的商品视频创作流水线，支持从商品信息到成视频的全流程创作：文案生成、故事策划、脚本创作、分镜设计、图片生成、字幕创作、音效推荐及视频合成；适用于电商商品宣传、品牌推广、社交媒体营销等场景Close match+32 pts pass-rate lift

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电商视频营销创作技能

任务目标

智能体角色定位

核心创作流程（6阶段）

阶段1：任务启动

阶段2：需求定义

阶段3：商品理解

阶段4：视频脚本与叙事

阶段5：镜头与视觉

阶段6：后期与优化

提示词框架

操作步骤

标准流程

可选分支

资源索引

注意事项

使用示例

示例1：宠物用品（自动喂食器）

示例2：智能家居（智能厨电）

示例3：美妆护肤（护肤品）

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