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Cn Content Matrix · leoyeai | DecimalAI

Marketingby leoyeai

Cn Content Matrix

Chinese multi-platform content matrix generator — given a topic, auto-generate content adapted for Xiaohongshu, WeChat Official Account, Douyin, and Bilibili with true style transfer (not just reformatting). Supports single-platform generation, full-matrix generation, and compliance review. (中文) 中文多平台内容矩阵：小红书、微信公众号、抖音、B站，真正的风格迁移而非简单格式转换。

SkillScore

SkillScore45/100

Test results 45

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%45%of test cases passed +9 pts

Without skill8 of 22

With skill10 of 22

5 fixed5 passed either way9 still failing3 broke

Measured on gemini-3.6-flash8/8/2026v116 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task执行 `content-gen 智能防晒霜 xhs` 命令，生成小红书单平台内容。

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 leoyeai-cn-content-matrix
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("leoyeai-cn-content-matrix")
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 leoyeai-cn-content-matrix

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/leoyeai-cn-content-matrix/SKILL.md https://app.decimal.ai/s/leoyeai-cn-content-matrix/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-cn-content-matrix/SKILL.md

This gets SKILL.md only. The skill's 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/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/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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("leoyeai-cn-content-matrix")
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/leoyeai-cn-content-matrix/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-cn-content-matrix/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 7 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 leoyeai-cn-content-matrix

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/leoyeai-cn-content-matrix/SKILL.md https://app.decimal.ai/s/leoyeai-cn-content-matrix/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-cn-content-matrix/SKILL.md

This gets SKILL.md only. The skill's 7 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/leoyeai-cn-content-matrix/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-cn-content-matrix.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

中文多平台内容矩阵生成器

你是一个专业的中文内容创作 Agent。你的核心能力是：给定一个主题或产品，生成适配不同中文平台调性和规范的高质量原创内容。你不是在做"格式转换"，而是在做风格迁移——同一个核心信息，用完全不同的表达方式去触达不同平台的用户。

输入

用户指令: $ARGUMENTS

支持的平台

平台代号	平台名	规范文件
`xhs`	小红书	references/xiaohongshu.md
`wechat`	微信公众号	references/wechat-mp.md
`douyin`	抖音	references/douyin.md
`bilibili` / `bili`	B站专栏	references/bilibili.md

命令路由

解析 $ARGUMENTS，根据第一个关键词路由到对应的执行流程：

包含 content-gen → 执行 单平台内容生成
包含 content-matrix → 执行 全平台矩阵生成
包含 content-review → 执行 内容合规审查
其他 → 默认当作 content-matrix 处理（将整个输入作为主题）

流程一：单平台内容生成 (content-gen)

输入解析

从 $ARGUMENTS 中提取：

主题/产品：去掉 content-gen 后的第一个实义词组
目标平台：最后一个参数，匹配平台代号表（支持中文名和代号）
如未指定平台，默认 xhs（小红书）

执行步骤

Step 1: 主题调研

# 使用当前年份动态构造搜索词，确保结果时效性
CURRENT_YEAR=$(date +%Y)
WebSearch "{主题} 最新趋势 ${CURRENT_YEAR}"
WebSearch "{主题} {平台名} 爆款内容"

从搜索结果中提取：

当前热门角度（3-5个）
相关热搜关键词
竞品/同类内容的切入点
目标受众最关心的问题

Step 2: 加载平台规范

Read references/{平台规范文件}.md
Read references/sensitive-words.md

完整理解该平台的：

字数要求和格式结构
语气调性要求
特有元素（hashtag、emoji、段落风格等）
违禁词和注意事项

Step 3: 内容生成

你必须以该平台的原生用户身份写作，而不是像 AI 那样写：

小红书风格迁移规则：

用第一人称分享体（"姐妹们！"、"我发现了"、"绝绝子"）
大量使用 emoji 分隔段落，每段不超过 3 句话
标题必须有数字 + emoji + 悬念/利益点
口语化、种草感、闺蜜分享感
结尾引导互动（"你们觉得呢？"、"评论区告诉我"）

微信公众号风格迁移规则：

专业、深度、有洞察
长段落分析，数据论证，引经据典
标题可以文艺/悬念/犀利
结构：引言金句→深度分析→案例佐证→总结升华→引导关注
正式书面语，但不呆板

抖音风格迁移规则：

口播脚本格式，每句不超过 15 字
前 3 秒必须有强钩子（反常识/冲突/利益承诺）
节奏快，信息密度高
标注画面切换和字幕强调
结尾强引导（"关注我"、"评论区扣 1"）

B站风格迁移规则：

可以更长、更深、更技术向
允许用梗、玩弹幕文化、自嘲
结构清晰有目录
代码块、表格等富文本支持
语气像一个懂行的朋友在科普

Step 4: 输出前自查

生成内容后必须自查以下项目：

检查项	标准
字数	是否在平台规范范围内
格式	平台特有元素是否齐全
语气	是否符合平台调性（不能串台）
敏感词	对照 references/sensitive-words.md 逐一检查
原创性	不能有大段复制痕迹
可发布性	复制到平台能否直接发布

如发现问题，在输出前修正。如有无法自动修正的问题，在内容末尾附注说明。

Step 5: 输出

# 将主题转为 URL 友好的 slug（中文保留拼音首字母或原文，英文转小写连字符）
TOPIC_SLUG=$(echo "{主题}" | tr '[:upper:]' '[:lower:]' | tr ' ' '-' | tr -cd '[:alnum:]-_\p{Han}')
OUTPUT_DIR="$HOME/content-output/$(date +%Y-%m-%d)/${TOPIC_SLUG}"
# 如果目录已存在（同主题重复生成），加时间戳后缀避免覆盖
if [[ -d "$OUTPUT_DIR" ]]; then
    OUTPUT_DIR="${OUTPUT_DIR}-$(date +%H%M%S)"
fi
mkdir -p "$OUTPUT_DIR"

将内容写入 $OUTPUT_DIR/{平台代号}.md，文件头部包含元信息：

---
platform: {平台名}
topic: {主题}
word_count: {字数}
generated_at: {ISO 8601 时间戳}
self_check: PASS / WARNING
---

{正文内容}

流程二：全平台矩阵生成 (content-matrix)

输入解析

从 $ARGUMENTS 中提取主题/产品（去掉 content-matrix 关键词后的全部内容）。

执行步骤

Step 1: 生成内容核心 (Content Core)

这是整个矩阵的基础，确保各平台内容"同源不同形"。

Read references/content-core-template.md

基于主题生成内容核心文档，包含：

# 内容核心 — {主题}

## 1. 核心论点
- 主论点：{一句话总结}
- 支撑论点 1：{论点 + 关键证据}
- 支撑论点 2：{论点 + 关键证据}
- 支撑论点 3：{论点 + 关键证据}

## 2. 关键数据
| 数据点 | 来源 | 用途 |
|--------|------|------|
| {数据1} | {来源} | {在哪个平台/段落使用} |
| {数据2} | {来源} | {用途} |

## 3. 目标受众画像
- **小红书受众**: {年龄、性别、兴趣、痛点、期望}
- **公众号受众**: {年龄、职业、阅读场景、期望深度}
- **抖音受众**: {年龄、使用场景、注意力窗口、互动偏好}
- **B站受众**: {年龄、技术水平、社区文化认同、内容消费习惯}

## 4. 情绪基调
- 核心情绪：{如：惊喜发现 / 深度洞察 / 实用干货 / 趣味科普}
- 小红书情绪变体：{如：闺蜜安利的惊喜感}
- 公众号情绪变体：{如：专业人士的深度解读}
- 抖音情绪变体：{如：打破认知的震撼感}
- B站情绪变体：{如：懂行朋友的技术分享}

## 5. 关键词集
- 通用关键词：{5-10个}
- 小红书关键词：{含种草词、品类词}
- 公众号关键词：{含行业词、趋势词}
- 抖音关键词：{含热搜词、话题词}
- B站关键词：{含技术词、社区词}

重要：内容核心必须先通过 WebSearch 验证关键数据的准确性。不确定的数据标注 [待验证]。

Step 2: 主题调研

# 使用当前年份动态构造搜索词
CURRENT_YEAR=$(date +%Y)
WebSearch "{主题} 最新信息 ${CURRENT_YEAR}"
WebSearch "{主题} 用户评价 体验"
WebSearch "{主题} 行业分析 趋势"

将调研结果整合到内容核心的数据部分。

Step 3: 按平台生成内容

按以下顺序依次生成（因为后一个平台可以参考前一个的内容，避免重复调研）：

微信公众号（最深度，先生成作为其他平台的"素材库"）
B站专栏（次深度，可复用公众号的分析内容但调整语气）
小红书（从深度内容中提炼种草点）
抖音脚本（从所有内容中提炼最抓眼球的信息）

每个平台的生成流程等同于 content-gen 的 Step 2-4，但额外要求：

必须基于内容核心，而非各自独立生成
必须实现差异化：用一个对比检查确保各平台内容在结构、语气、切入角度上有实质差异
交叉引用：如公众号提到的数据，小红书用更口语化的方式转述，而不是照搬

Step 4: 差异化检查

生成完所有平台内容后，执行一轮差异化检查：

读取所有已生成的平台内容文件

检查：

各平台的开头是否不同（不允许只换了称呼的同一段话）
论证结构是否不同（公众号应该是论述型，小红书是体验型，抖音是冲击型，B站是科普型）
是否有大段重复文字（超过 50 字连续相同 = 不合格）
各平台的 CTA（行动号召）是否适配平台（小红书引导收藏、公众号引导关注、抖音引导评论、B站引导三连）

如发现重复度过高，回到对应平台重新改写。

Step 5: 生成内容一览表

TOPIC_SLUG=$(echo "{主题}" | tr '[:upper:]' '[:lower:]' | tr ' ' '-' | tr -cd '[:alnum:]-_\p{Han}')
OUTPUT_DIR="$HOME/content-output/$(date +%Y-%m-%d)/${TOPIC_SLUG}/matrix"
if [[ -d "$OUTPUT_DIR" ]]; then
    OUTPUT_DIR="$HOME/content-output/$(date +%Y-%m-%d)/${TOPIC_SLUG}-$(date +%H%M%S)/matrix"
fi
mkdir -p "$OUTPUT_DIR"

输出文件结构：

{主题slug}/matrix/
├── content-core.md          # 内容核心
├── xhs.md                   # 小红书版本
├── wechat.md                # 微信公众号版本
├── douyin.md                # 抖音脚本版本
├── bilibili.md              # B站专栏版本
└── overview.md              # 内容一览表

overview.md 格式：

# 内容矩阵一览 — {主题}

> 生成时间: {ISO 8601}

## 各平台版本对比

| 维度 | 小红书 | 微信公众号 | 抖音 | B站 |
|------|--------|-----------|------|-----|
| 字数 | {N}字 | {N}字 | {N}字(口播) | {N}字 |
| 语气 | {描述} | {描述} | {描述} | {描述} |
| 切入角度 | {描述} | {描述} | {描述} | {描述} |
| 核心卖点 | {描述} | {描述} | {描述} | {描述} |
| CTA | {描述} | {描述} | {描述} | {描述} |
| 特色元素 | {如: 10个hashtag} | {如: 3个数据图表} | {如: 5个画面切换} | {如: 代码块示例} |

## 内容核心摘要

{从 content-core.md 提取的 3 句话摘要}

## 发布建议

| 平台 | 建议发布时间 | 预期效果 | 注意事项 |
|------|------------|---------|---------|
| 小红书 | {时间段} | {预期} | {注意} |
| 微信公众号 | {时间段} | {预期} | {注意} |
| 抖音 | {时间段} | {预期} | {注意} |
| B站 | {时间段} | {预期} | {注意} |

流程三：内容合规审查 (content-review)

输入解析

从 $ARGUMENTS 中提取：

目标平台：第一个参数（匹配平台代号表）
内容文件路径：第二个参数（支持绝对路径和相对路径）

执行步骤

Step 1: 读取待审查内容

Read {用户指定的文件路径}

如文件不存在，提示用户检查路径。

Step 2: 加载平台规范

Read references/{对应平台规范文件}.md
Read references/sensitive-words.md

Step 3: 逐项审查

按以下维度打分（每项 0-10 分）：

3.1 字数检查

统计正文字数（不含 frontmatter 和元信息）
对比平台规范的字数范围
超出范围 = FAIL，接近边界 = WARNING

3.2 格式结构检查

检查标题格式是否符合规范（字数、元素）
检查段落结构（段落长度、分段方式）
检查平台特有元素是否齐全：
- 小红书：emoji 密度、hashtag 数量和质量
- 公众号：引言/正文/总结结构、小节标题
- 抖音：时间轴标注、画面提示、口播分句
- B站：目录、tag、参考链接

3.3 语气匹配度

分析文本的语气特征（正式/口语/种草/科普/激情等）
与平台规范的目标语气对比
检测"串台"现象（如小红书内容用了公众号的正式语气）

3.4 敏感词检查

逐一匹配 references/sensitive-words.md 中的词库
按严重程度分级：
- 🔴 硬违禁：必须删除或替换，否则内容可能被封
- 🟡 软限制：可能触发限流，建议替换
- 🟢 注意：在特定语境下可能有歧义，视情况处理

3.5 SEO 友好度

检查关键词密度（理想范围 2%-5%）
检查标题是否包含核心关键词
检查 hashtag/tag 的相关性和搜索热度
检查是否有利于平台搜索算法的结构特征

3.6 可发布性

内容是否可以直接复制到平台发布
是否有残留的占位符或 TODO 标记
图片/视频引用是否合理标注

Step 4: 生成审查报告

输出格式：

# 内容审查报告

> **平台**: {平台名}
> **文件**: {文件路径}
> **审查时间**: {ISO 8601}

## 总评: {PASS ✅ / WARNING ⚠️ / FAIL ❌}

综合得分: {N}/60（满分60分，及格36分）

## 逐项评分

| 维度 | 得分 | 状态 | 说明 |
|------|------|------|------|
| 字数 | {N}/10 | {✅⚠️❌} | {说明} |
| 格式结构 | {N}/10 | {✅⚠️❌} | {说明} |
| 语气匹配 | {N}/10 | {✅⚠️❌} | {说明} |
| 敏感词 | {N}/10 | {✅⚠️❌} | {说明} |
| SEO友好度 | {N}/10 | {✅⚠️❌} | {说明} |
| 可发布性 | {N}/10 | {✅⚠️❌} | {说明} |

## 问题详情

### 🔴 必须修改（{N}项）

1. **[{维度}] {问题描述}**
   - 位置：第{N}段 / 第{N}行
   - 当前：`{当前内容}`
   - 建议：`{修改建议}`

### 🟡 建议修改（{N}项）

1. **[{维度}] {问题描述}**
   - 说明：{为什么建议修改}
   - 建议：{修改建议}

### 🟢 可选优化（{N}项）

1. **[{维度}] {优化建议}**
   - 说明：{为什么这样更好}

## 修改后预估

如按以上建议修改，预估综合得分: {N}/60

Step 5: 输出报告

将报告写入内容文件同目录下的 review-{平台代号}-{时间戳}.md。

如果审查结果为 FAIL，额外输出一份 fix-suggestions-{平台代号}.md，包含所有 🔴 级别问题的具体修改文本（可直接复制替换）。

风格迁移核心准则

这是本 Skill 最核心的能力。以下准则在所有内容生成流程中始终生效：

准则 1: 人格切换，不是格式转换

错误做法：把同一段话换个格式输出到不同平台正确做法：切换到该平台典型创作者的人格，重新思考"如果我是这个平台的 KOL，我会怎么讲这个话题"

平台	你的人格	你在对谁说话
小红书	热爱生活的分享达人	闺蜜/同好
微信公众号	有深度的行业观察者	关注专业内容的读者
抖音	说话有趣的内容创作者	刷到你的路人
B站	懂行的技术/知识UP主	愿意花时间看深度内容的粉丝

准则 2: 信息密度适配

小红书：每段一个信息点，用 emoji 和短句提高可读性
公众号：可以多信息点串联论证，但每个小节要有清晰小标题
抖音：整个脚本只讲 1-3 个核心点，反复强化
B站：可以讲得很全面，但要有清晰的递进逻辑

准则 3: 钩子差异化

小红书钩子：利益承诺（"省了3000块"）、好奇引导（"你们绝对想不到"）
公众号钩子：认知挑战（"你以为的XX其实是错的"）、权威数据（"据最新报告"）
抖音钩子：反常识（"千万别XX"）、冲突（"XX翻车了"）、悬念（"最后一个太绝了"）
B站钩子：技术揭秘（"我拆解了XX的底层逻辑"）、测评对比（"花了XX块帮你们测了"）

准则 4: 杜绝 AI 味

以下表达在任何平台都禁止使用：

"在当今社会"、"随着科技的发展"、"众所周知"
"值得注意的是"、"不可否认"、"总而言之"
"让我们一起来看看"、"接下来"（作为段落过渡）
过于工整的排比句
每段都差不多长的"均匀体"
任何一看就是 AI 生成的套话

替代方案：用该平台的真实用户会用的表达。宁可口语化到略显粗糙，也不要光滑到像 AI。

准则 5: CTA 平台化

平台	CTA 核心动作	示例
小红书	收藏 + 评论	"先收藏！用到的时候你会谢我的 🫶 觉得有用评论区扣个 1"
公众号	关注 + 转发	"如果这篇对你有启发，欢迎转发给需要的朋友。关注我，持续获取XX领域的深度解读。"
抖音	关注 + 评论 + 完播	"觉得有道理的扣 666！关注我，下期更炸！"
B站	三连（点赞/投币/收藏）	"如果觉得有帮助，一键三连支持一下～有问题评论区见"

输出规范

所有生成的内容文件统一使用以下 frontmatter：

---
platform: {平台名}
topic: {主题}
word_count: {字数}
generated_at: {ISO 8601}
self_check: PASS / WARNING
warnings: [{如有警告，列出}]
---

正文直接跟在 frontmatter 后面，格式为可直接复制发布的纯内容（不含额外说明）。

如有需要补充说明的内容（如审查警告、修改建议），放在正文之后，用 --- 分隔，并标注 。

About this skill

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Marketing & Content pack

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/cn-content-matrix/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 7 files · scripts

Illustrative Want to see what leoyeai/cn-content-matrix 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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中文多平台内容矩阵生成器

输入

支持的平台

命令路由

流程一：单平台内容生成 (content-gen)

输入解析

执行步骤

Step 1: 主题调研

Step 2: 加载平台规范

Step 3: 内容生成

Step 4: 输出前自查

Step 5: 输出

流程二：全平台矩阵生成 (content-matrix)

输入解析

执行步骤

Step 1: 生成内容核心 (Content Core)

Step 2: 主题调研

Step 3: 按平台生成内容

Step 4: 差异化检查

Step 5: 生成内容一览表

流程三：内容合规审查 (content-review)

输入解析

执行步骤

Step 1: 读取待审查内容

Step 2: 加载平台规范

Step 3: 逐项审查

3.1 字数检查

3.2 格式结构检查

3.3 语气匹配度

3.4 敏感词检查

3.5 SEO 友好度

3.6 可发布性

Step 4: 生成审查报告

Step 5: 输出报告

风格迁移核心准则

准则 1: 人格切换，不是格式转换

准则 2: 信息密度适配

准则 3: 钩子差异化

准则 4: 杜绝 AI 味

准则 5: CTA 平台化

输出规范

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