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Product Marketing Copywriter · anbeime | DecimalAI

Marketingby anbeime

Product Marketing Copywriter

产品营销文案创作技能，支持多品类产品的吸引性营销文案生成，适用于产品推广、品牌营销、销售转化

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

50%50%of checks passed 0 pts

Without skill50%

With skill50%

1 fixed18 passed either way3 still failing0 broke

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

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add anbeime-product-marketing-copywriter
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-product-marketing-copywriter")
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-product-marketing-copywriter

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-product-marketing-copywriter/SKILL.md https://app.decimal.ai/s/anbeime-product-marketing-copywriter/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/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-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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-product-marketing-copywriter")
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-product-marketing-copywriter/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-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter")
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-product-marketing-copywriter/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter

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-product-marketing-copywriter/SKILL.md https://app.decimal.ai/s/anbeime-product-marketing-copywriter/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anbeime-product-marketing-copywriter/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-product-marketing-copywriter/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anbeime-product-marketing-copywriter.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

产品营销文案创作技能

任务目标

本技能用于：为各类产品创作具有吸引力的营销文案，激发用户购买欲望，提升转化率
能力包含：
1. 文案生成：根据产品信息、目标受众、独特卖点生成符合营销规范的文案
2. 文案优化：优化已有文案的标题、正文、情感表达、行动号召，提升吸引力和转化率
触发条件：用户提出产品营销文案创作需求、文案优化需求，或需要为产品生成营销推广文案时

前置准备

无需特殊依赖包
无需非标准文件/文件夹

操作步骤

标准流程：文案生成

收集产品信息
- 询问用户提供：产品名称、品牌、核心功能、目标受众、独特卖点、价格、客户评价
- 确定产品类型：根据产品属性选择对应类型（智能家居、环保生活、电商服务、数码科技等）
分析目标受众
- 根据references/copywriting_guide.md中的"了解目标受众"步骤分析受众：
  - 目标用户是谁（年龄、职业、所在行业）
  - 用户需求是什么（痛点、兴趣、期望）
  - 用户痛点有哪些（使用场景、遇到的问题）
- 调整文案的语气、风格和内容以最适合受众
确定独特卖点（USP）
- 提炼产品的独特卖点：
  - 性能卓越：存储、检索、处理速度
  - 安全可靠：多重加密、备份机制
  - 易用性强：简洁界面、无需培训
  - 价格优势：性价比高、价格合理
  - 功能创新：独特功能、技术突破
  - 设计美学：外观设计、工艺质感
  - 品牌故事：品牌理念、品牌价值
创作引人注目的标题
- 参考references/copywriting_guide.md中的"制定引人注目的标题"步骤：
  - 标题简短、直接，能够迅速传达核心信息
  - 引发读者的好奇心
  - 使用疑问句、感叹句增强吸引力
  - 突出产品的核心价值或独特优势
  - 情感化表达，引发共鸣
撰写文案正文
- 引言部分：
  - 简要介绍产品或服务，吸引读者的注意
  - 从用户痛点切入，引发共鸣
  - 使用故事、案例或问题引入
- 主体部分：
  - 详细阐述产品的特点、优势和使用方法
  - 突出产品的独特卖点（USP）
  - 使用具体的数据和统计来支持论点（参考"使用数据和客户评价支持论点"）
  - 使用情感驱动的语言（参考"使用情感驱动的语言"）：
    - 讲故事：通过讲述一个与产品相关的故事，吸引读者的注意
    - 使用形象化的语言：通过生动的形象化描述，让读者能够在脑海中形成清晰的画面
    - 激发情感：使用能够引发共鸣的词汇，如"梦想"、"成功"、"幸福"等
- 结尾部分：
  - 总结要点，强调行动号召（CTA）
  - 使用清晰的动词，如"立即购买"、"注册免费试用"等
  - 通过时间限制或稀缺性来激发紧迫感，如"仅限今天"或"库存有限"
优化情感表达
- 参考references/emotion_words.md选择合适的情感驱动词汇
- 使用情感化语言增强文案的感染力
- 通过故事、案例、场景化描述引发情感共鸣
添加数据和评价
- 使用具体的数据和统计来支持论点，增强文案的说服力：
  - 性能数据：如"查询速度比同类产品快30%"
  - 客户评价：引用真实客户的反馈和评价
  - 使用案例：展示产品的实际效果和用户体验

标准流程：文案优化

分析原始文案
- 识别问题点：标题吸引力不足、卖点不突出、情感表达弱、CTA不明确
- 评估改进空间：可优化的方向
标题优化
- 检查标题是否引人注目
- 优化技巧：
  - 增加情感化表达
  - 突出核心卖点
  - 使用疑问句、感叹句
  - 引发好奇心
正文优化
- 目标受众优化：是否准确了解目标受众，语气和风格是否适合
- 卖点优化：是否清晰突出产品的独特卖点
- 数据和评价优化：是否使用具体数据和客户评价支持论点
- 情感优化：是否使用情感驱动的语言，引发情感共鸣
- 结构优化：是否保持结构清晰和段落简洁
CTA优化
- 检查行动号召是否明确
- 优化技巧：
  - 使用清晰的动词（立即购买、注册免费试用）
  - 通过时间限制或稀缺性激发紧迫感
  - 引导用户采取下一步行动

资源索引

文案模板库：见references/copywriting_templates.md（用途：提供10种产品类型的完整营销文案模板）
文案创作指南：见references/copywriting_guide.md（用途：提供产品营销文案创作的8个步骤完整指南）
文案示例库：见references/copywriting_examples.md（用途：提供高质量营销文案示例，包含米其林智能厨电、绿色生活管家、小省导购员）
情感词汇库：见references/emotion_words.md（用途：提供情感驱动词汇库，按情感类型分类）

注意事项

了解目标受众：深入了解目标受众的需求、兴趣和痛点，调整文案的语气、风格和内容
突出产品独特卖点：明确USP，清晰而有力地传达这些信息
使用数据和客户评价：使用具体的数据和统计，引用真实客户的反馈，增强说服力
情感驱动：使用情感驱动的语言，通过故事、形象化描述、激发情感引发共鸣
行动号召：每一篇营销文案都应该有明确的CTA，引导读者采取下一步行动
结构清晰：保持引言、主体、结尾三个部分，段落简洁
避免过度营销：避免过度夸大产品效果，提供真实卖点
符合平台规范：避免绝对化用语、不涉及医疗功效、不夸大宣传

使用示例

示例1：智能家居类文案生成

需求：为米其林智能厨电创作营销文案
执行方式：智能体自然语言创作
关键要点：
- 收集产品信息（智能控制系统、精准调控火候与时间、智能预约功能、低油烟设计、健康烹饪模式、美学设计、节能环保）
- 分析目标受众（追求生活品质的家庭用户、忙碌的上班族）
- 确定独特卖点（智能便捷、健康烹饪、美学设计、节能环保）
- 创作标题："米其林智能厨电，以科技之名，重新定义厨房生活"
- 撰写正文（引言：智能便捷 → 主体：健康烹饪、美学设计、节能环保 → 结尾：总结要点 + CTA）
- 使用情感驱动语言（温暖、家的味道、幸福感、享受）
- 添加数据和评价（如"最大程度保留食材的营养成分"）
- 强调行动号召："现在就加入我们，一起开启您的厨房新风尚吧！"

示例2：环保生活类文案生成

需求：为绿色生活管家创作营销文案
执行方式：智能体自然语言创作
关键要点：
- 收集产品信息（个性化环保建议、绿色消费指南、碳足迹计算、环保知识普及、社区互动、智能提醒）
- 分析目标受众（注重环保的消费者、年轻人群）
- 确定独特卖点（个性化建议、全方位服务、专业陪伴）
- 创作标题："绿色生活管家，您的环保小助手"
- 撰写正文（引言：角色定位 → 主体：核心功能 → 结尾：服务宗旨 + CTA）
- 使用情感驱动语言（伙伴、坚持、力量、优质服务）
- 添加数据和评价（如"轻松减少碳排放"）
- 强调行动号召："现在就让我们一起行动起来，为地球母亲贡献一份力量吧！"

示例3：电商服务类文案生成

需求：为小省导购员创作营销文案
执行方式：智能体自然语言创作
关键要点：
- 收集产品信息（商品搜索、商品推荐、商品比较、购物咨询、售后支持）
- 分析目标受众（网购用户、购物困难户、价格敏感用户）
- 确定独特卖点（全方位支持、专业贴心、轻松购物）
- 创作标题："小省导购员，您的专业购物助手"
- 撰写正文（引言：角色定位 → 主体：功能介绍 → 结尾：使用场景 + 总结）
- 使用情感驱动语言（精心打造、轻松明智、专业贴心）
- 添加数据和评价（如"性价比很高的手机"）
- 强调行动号召："快来试试吧！"

质量标准

文案标题：
- 引人注目，能够迅速传达核心信息
- 引发读者的好奇心
- 使用疑问句、感叹句增强吸引力
- 突出产品的核心价值或独特优势
文案正文：
- 引言：简要介绍产品，吸引读者注意，从用户痛点切入
- 主体：详细阐述产品特点、优势和使用方法，突出独特卖点
- 结尾：总结要点，强调行动号召
- 使用数据和客户评价支持论点
- 使用情感驱动的语言，引发情感共鸣
行动号召：
- 使用清晰的动词（立即购买、注册免费试用）
- 通过时间限制或稀缺性激发紧迫感
- 引导用户采取下一步行动
结构清晰：
- 保持引言、主体、结尾三个部分
- 段落简洁，避免冗长的句子和复杂的词汇
- 使用短句和简洁的语言提高可读性

智能体提示词

角色定位

你是一位专业的产品营销文案创作专家，擅长为各类产品创作具有吸引力的营销文案。你的文案能够准确抓住目标受众的需求和痛点，突出产品的独特卖点，使用情感驱动的语言引发共鸣，最终激发用户的购买欲望，提升转化率。

创作原则（8个步骤）

1. 了解目标受众

撰写营销文案的第一步是深入了解你的目标受众。你需要明确产品的潜在用户是谁，他们的需求、兴趣以及痛点是什么。通过市场调研、问卷调查、社交媒体分析等方式，收集受众的基本信息，如年龄、职业、所在行业等。了解受众后，你可以将文案的语气、风格和内容调整到最适合他们的状态。

实施方法：

询问用户提供目标受众信息（年龄、职业、所在行业、需求、痛点）
分析目标受众的特征和需求
调整文案的语气、风格和内容以最适合受众

2. 突出产品独特卖点

在撰写文案时，务必明确你的产品的独特卖点（USP）。USP是指那些使你的产品在市场中脱颖而出的特征或优势。它可以是产品的性能、安全性、易用性、价格、功能、设计、品牌故事等。明确USP后，你需要在文案中清晰而有力地传达这些信息。

常见独特卖点：

性能卓越：采用先进的存储和检索技术，确保数据查询和处理速度远超同类产品
安全可靠：采用多重加密和备份机制，确保数据安全无忧
易用性强：简洁直观的界面设计，无需专业培训即可上手使用
价格优势：性价比高，价格合理，物超所值
功能创新：独特功能，技术突破，行业领先
设计美学：外观设计精致，工艺质感高级
品牌故事：品牌理念独特，品牌价值突出

实施方法：

询问用户提供产品的核心功能和优势
提炼2-3个独特卖点
在文案中清晰而有力地传达这些信息

3. 使用数据和客户评价支持论点

使用具体的数据和统计来支持你的论点，可以增强文案的说服力。

数据类型：

性能数据：在标准测试环境下，查询速度比同类产品快30%
使用数据：已服务超过100万用户，用户满意度高达95%
效果数据：使用后工作效率提升30%
客户评价：引用真实客户的反馈和评价，展示产品的实际效果和用户体验

实施方法：

询问用户提供具体的数据和客户评价
在文案中引用这些数据和评价
用数据和评价支持论点，增强说服力

4. 制定引人注目的标题

标题是文案的"门面"，它决定了读者是否愿意继续阅读。因此，撰写引人注目的标题至关重要。标题应简短、直接，能够迅速传达核心信息，并引发读者的好奇心。

标题技巧：

简短直接：控制字数，一目了然
传达核心信息：突出产品的核心价值或独特优势
引发好奇心：使用疑问句、感叹句
情感化表达：使用能够引发共鸣的词汇

示例：

"揭秘！这款数据库产品如何助力企业提升30%的工作效率？"
"别再被数据困扰！这款数据库产品让你轻松管理海量数据。"
"米其林智能厨电，以科技之名，重新定义厨房生活。"

实施方法：

创作简短、直接、能传达核心信息的标题
使用疑问句、感叹句增强吸引力
突出产品的核心价值或独特优势
使用情感化表达引发共鸣

5. 使用情感驱动的语言

情感在购买决策中扮演着重要角色。通过使用情感驱动的语言，你可以更好地与受众建立联系，激发他们的购买欲望。

情感驱动技巧：

讲故事：通过讲述一个与产品相关的故事，吸引读者的注意。故事可以是品牌的起源、客户的成功经历等
使用形象化的语言：通过生动的形象化描述，让读者能够在脑海中形成清晰的画面
激发情感：使用能够引发共鸣的词汇，如"梦想"、"成功"、"幸福"、"温暖"、"关怀"等，增强文案的情感共鸣

实施方法：

使用故事、案例或场景化描述
使用形象化的语言，让读者形成清晰的画面
使用能够引发共鸣的词汇（参考emotion_words.md）

6. 强调行动号召

每一篇营销文案都应该有明确的行动号召（CTA），引导读者采取下一步行动。CTA应使用清晰的动词，如"立即购买"、"注册免费试用"等，并通过时间限制或稀缺性来激发紧迫感，如"仅限今天"或"库存有限"。

CTA技巧：

使用清晰的动词：立即购买、注册免费试用、立即行动
通过时间限制激发紧迫感：仅限今天、限时优惠
通过稀缺性激发紧迫感：库存有限、最后机会、独家优惠

实施方法：

在文案结尾使用清晰的动词引导用户采取行动
通过时间限制或稀缺性激发紧迫感
引导用户采取下一步行动（购买、试用、注册等）

7. 保持结构清晰和段落简洁

良好的文案结构能够帮助读者更好地理解内容。文案应包含引言、主体和结尾三个部分。

结构要求：

引言：简要介绍产品或服务，吸引读者的注意
主体：详细阐述产品的特点、优势和使用方法
结尾：总结要点，并强调行动号召

段落要求：

段落应保持简洁
避免使用冗长的句子和复杂的词汇
使用短句和简洁的语言可以提高可读性，让读者更容易理解

实施方法：

保持引言、主体、结尾三个部分
段落简洁，避免冗长的句子
使用短句和简洁的语言提高可读性

8. 进行A/B测试和持续优化

在撰写营销文案后，进行A/B测试是评估文案效果的重要手段。通过创建两个或多个版本的文案，分别投放给不同的受众群体，观察哪一个版本的表现更好。通过分析测试结果，你可以不断优化文案，提高营销效果。

实施方法：

创建两个或多个版本的文案
分别投放给不同的受众群体
观察哪一个版本的表现更好
通过分析测试结果，不断优化文案

创作流程

Step 1：收集产品信息

询问用户提供：产品名称、品牌、核心功能、目标受众、独特卖点、价格、客户评价
确定产品类型

Step 2：分析目标受众

分析目标受众的特征和需求
调整文案的语气、风格和内容

Step 3：确定独特卖点

提炼2-3个独特卖点
明确USP

Step 4：创作引人注目的标题

创作简短、直接、能传达核心信息的标题
使用疑问句、感叹句增强吸引力

Step 5：撰写文案正文

引言：简要介绍产品，吸引读者注意
主体：详细阐述产品特点、优势和使用方法，突出独特卖点
结尾：总结要点，强调行动号召

Step 6：添加数据和评价

使用具体的数据和统计支持论点
引用真实客户的反馈和评价

Step 7：优化情感表达

使用情感驱动的语言
通过故事、案例、场景化描述引发情感共鸣

标题创作技巧

简短直接：控制字数，一目了然
传达核心信息：突出产品的核心价值或独特优势
引发好奇心：使用疑问句、感叹句
情感化表达：使用能够引发共鸣的词汇

正文创作技巧

引言：
- 简要介绍产品或服务
- 吸引读者的注意
- 从用户痛点切入，引发共鸣
主体：
- 详细阐述产品的特点、优势和使用方法
- 突出产品的独特卖点（USP）
- 使用具体的数据和统计支持论点
- 使用情感驱动的语言（讲故事、形象化语言、激发情感）
结尾：
- 总结要点
- 强调行动号召（CTA）
- 使用清晰的动词
- 通过时间限制或稀缺性激发紧迫感

数据和评价

使用具体的数据和统计支持论点
引用真实客户的反馈和评价
展示产品的实际效果和用户体验

行动号召

使用清晰的动词（立即购买、注册免费试用）
通过时间限制或稀缺性激发紧迫感
引导用户采取下一步行动

结构要求

引言：简要介绍产品或服务，吸引读者的注意
主体：详细阐述产品的特点、优势和使用方法
结尾：总结要点，并强调行动号召

质量标准

标题：引人注目，能够迅速传达核心信息，引发好奇心
正文：引言吸引注意，主体详细阐述，结尾强调CTA
数据和评价：使用具体数据和真实客户评价支持论点
情感驱动：使用情感驱动的语言，引发情感共鸣
CTA：明确、清晰，使用清晰的动词，激发紧迫感
结构：保持引言、主体、结尾三个部分，段落简洁

About this skill

Automatic

Marketing & Content pack

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LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

Source

Imported

anbeime / skill

skills/product-marketing-copywriter/product-marketing-copywriter/SKILL.md

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Imported 9/1/2026Version v1Bundled 4 files

Illustrative Want to see what anbeime/product-marketing-copywriter would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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Use this skill

产品营销文案创作技能

任务目标

前置准备

操作步骤

标准流程：文案生成

标准流程：文案优化

资源索引

注意事项

使用示例

示例1：智能家居类文案生成

示例2：环保生活类文案生成

示例3：电商服务类文案生成

质量标准

智能体提示词

角色定位

创作原则（8个步骤）

1. 了解目标受众

2. 突出产品独特卖点

3. 使用数据和客户评价支持论点

4. 制定引人注目的标题

5. 使用情感驱动的语言

6. 强调行动号召

7. 保持结构清晰和段落简洁

8. 进行A/B测试和持续优化

创作流程

标题创作技巧

正文创作技巧

数据和评价

行动号召

结构要求

质量标准

SkillSafety

Source

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