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Intelligent Content System · anbeime | DecimalAI

Marketingby anbeime

Intelligent Content System

智能内容创作与发布全流程系统。根据用户需求自动识别场景，编排调用网页采集、智能配图、小红书图文生成、热点内容创作、多平台发布等技能，实现从内容获取到发布的一站式自动化。支持场景： - 网页内容采集与再创作（采集→配图→发布） - 热点内容创作（热点采集→AI生成→多平台发布） - 原创内容优化（配图→小红书图文→发布） - 单一功能调用（采集/配图/图文/发布）支持平台：微信公众号、小红书、X/Twitter、B站

Never made a case worse

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%82%of test cases passed +64 pts

Without skill4 of 22

With skill18 of 22

14 fixed4 passed either way4 still failing0 broke

Measured on gemini-3.6-flash8/10/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task帮我把那个网页内容采集下来，加上配图后直接发布到微信公众号草稿箱。

Without the skillMissed

With the skillPassed

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

Use this skill

Your agent runs on

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

pip install "decimalai>=0.15"
decimalai skills add anbeime-intelligent-content-system
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-intelligent-content-system")
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-intelligent-content-system

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-intelligent-content-system/SKILL.md https://app.decimal.ai/s/anbeime-intelligent-content-system/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anbeime-intelligent-content-system/SKILL.md

This gets SKILL.md only. The skill's 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/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-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/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-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system")
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-intelligent-content-system/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anbeime-intelligent-content-system/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 2 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-intelligent-content-system

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-intelligent-content-system/SKILL.md https://app.decimal.ai/s/anbeime-intelligent-content-system/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anbeime-intelligent-content-system/SKILL.md

This gets SKILL.md only. The skill's 2 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-intelligent-content-system/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anbeime-intelligent-content-system.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/10/2026
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

智能内容创作与发布系统

任务目标

本技能用于：从内容获取到多平台发布的智能化全流程
能力包含：
- 自动识别用户需求场景
- 智能编排技能调用链
- 网页内容采集与转换
- 文章智能配图
- 小红书图文生成
- 热点内容创作
- 多平台自动发布
触发条件：用户提出内容创作或发布相关需求

核心特性

1. 智能场景识别

系统会自动分析用户输入，识别以下场景：

场景代码	场景名称	触发关键词	技能链
A1	采集并发布	"采集+发布"、"抓取+推送"	url-to-markdown → format → illustrator → publish
A2	采集并配图	"采集+配图"、"抓取+插图"	url-to-markdown → illustrator
A3	采集转小红书	"采集+小红书"、"网页转小红书"	url-to-markdown → xhs-images
B1	热点文章生成	"热点+文章"、"爆款+公众号"	wechat-hotspot-publisher
B2	热点图文生成	"热点+小红书"、"爆款+图文"	wechat-hotspot → xhs-images
B3	热点多平台发布	"热点+多平台"	wechat-hotspot → multi-publish
C1	文章配图	"配图"、"插图"、"加图"	article-illustrator
C2	转小红书图文	"转小红书"、"生成图文"	xhs-images
C3	多平台发布	"发布到"、"推送到"	format → publish
D1	仅采集网页	"采集"、"抓取"、"保存网页"	url-to-markdown
D2	仅配图	"配图"（单独）	article-illustrator
D3	仅小红书图文	"小红书图文"（单独）	xhs-images
D4	仅发布	"发布"（单独）	publish

详细识别规则见：references/scenario-detection.md

2. 自动参数收集

系统会根据场景自动收集必需参数：

URL：网页地址（用于采集场景）
关键词：热点话题关键词（用于热点场景）
文件路径：Markdown文件路径（用于优化场景）
平台列表：目标发布平台（微信/小红书/X/B站）
风格偏好：小红书图文风格（可选）
是否需要登录：网页是否需要登录（可选）

3. 进度实时反馈

执行过程中会实时反馈进度：

✓ 场景识别：A1 - 采集并发布
✓ 参数收集完成
→ 步骤1/4：采集网页内容...
✓ 步骤1/4：内容已采集（3500字）
→ 步骤2/4：格式优化...
✓ 步骤2/4：格式已优化
→ 步骤3/4：生成配图...
✓ 步骤3/4：已生成3张配图
→ 步骤4/4：发布到微信公众号...
✓ 步骤4/4：已推送到草稿箱

✅ 任务完成！

操作步骤

标准流程

用户描述需求
- 用户用自然语言描述需要完成的任务
- 示例："采集这篇文章并发布到微信公众号"
场景识别
- 系统分析用户输入，识别场景代码
- 确定需要调用的技能链
- 参考：references/scenario-detection.md
参数收集
- 系统提取用户输入中的参数（URL、关键词等）
- 如有缺失，询问用户补充
- 确认所有必需参数已收集
工作流执行
- 按照技能链顺序执行各个步骤
- 每步完成后反馈进度
- 将上一步输出作为下一步输入
- 参考：references/workflow-templates.md
结果汇总
- 展示最终结果
- 提供文件路径或发布链接
- 询问是否需要调整

可选分支

分支A：用户不确定需求

系统展示可用场景列表
用户选择或描述具体需求
系统根据选择执行对应工作流

分支B：参数不完整

系统列出缺失的参数
用户补充参数
继续执行工作流

分支C：执行失败

系统报告失败原因
提供解决建议
询问是否重试或调整参数

场景详解

场景A：网页内容采集与再创作

A1：采集并发布

用户需求："采集这篇文章并发布到微信公众号"

执行流程：

使用 baoyu-url-to-markdown 采集网页内容
使用 baoyu-format-markdown 优化格式
使用 article-illustrator 生成配图
使用 wechat-publisher 发布到微信草稿箱

输出：

优化后的Markdown文件
配图文件（imgs/目录）
微信草稿箱链接

A2：采集并配图

用户需求："采集这篇文章并配图"

执行流程：

使用 baoyu-url-to-markdown 采集网页内容
使用 article-illustrator 生成配图

输出：

带配图的Markdown文件
配图文件（imgs/目录）

A3：采集转小红书

用户需求："把这个网页转成小红书图文"

执行流程：

使用 baoyu-url-to-markdown 采集网页内容
使用 baoyu-xhs-images 生成小红书图文

输出：

小红书图文系列（1-10张）
分析文档和大纲

场景B：热点内容创作

B1：热点文章生成

用户需求："根据AI热点生成一篇公众号文章"

执行流程：

使用 wechat-hotspot-publisher 采集热点并生成文章
自动生成标题、封面、配图、标签
推送到微信公众号草稿箱

输出：

完整的HTML格式文章
微信草稿箱链接

B2：热点图文生成

用户需求："根据AI热点生成小红书图文"

执行流程：

使用 wechat-hotspot-publisher 采集热点并生成内容
使用 baoyu-xhs-images 转换为小红书图文

输出：

小红书图文系列（1-10张）
原始文章内容

B3：热点多平台发布

用户需求："根据AI热点生成内容，发布到微信和小红书"

执行流程：

使用 wechat-hotspot-publisher 采集热点并生成文章
使用 baoyu-xhs-images 转换为小红书图文
分别发布到各平台

输出：

微信公众号草稿箱链接
小红书图文系列
各平台发布状态

场景C：原创内容优化与发布

C1：文章配图

用户需求："给这篇文章配图"

执行流程：

使用 article-illustrator 分析文章并生成配图

输出：

带配图的Markdown文件
配图文件（imgs/目录）

C2：转小红书图文

用户需求："把这篇文章转成小红书图文"

执行流程：

使用 baoyu-xhs-images 生成小红书图文

输出：

小红书图文系列（1-10张）
分析文档和大纲

C3：多平台发布

用户需求："把这篇文章发布到微信和X"

执行流程：

使用 baoyu-format-markdown 优化格式
分别发布到各平台

输出：

各平台发布状态和链接

场景D：单一功能调用

D1：仅采集网页

用户需求："采集这个网页"

执行流程：

使用 baoyu-url-to-markdown 采集网页内容

输出：

Markdown文件

D2：仅配图

用户需求："给文章配图"

执行流程：

使用 article-illustrator 生成配图

输出：

带配图的Markdown文件
配图文件

D3：仅小红书图文

用户需求："生成小红书图文"

执行流程：

使用 baoyu-xhs-images 生成图文

输出：

小红书图文系列

D4：仅发布

用户需求："发布到微信"

执行流程：

使用对应平台的发布技能

输出：

发布状态和链接

资源索引

核心文档

场景识别规则 - 详细的场景识别算法和规则
工作流模板 - 各场景的完整工作流定义
技能整合说明 - 子技能的调用方式和参数说明
平台配置说明 - 各平台的配置要求和注意事项
快速参考卡 - 常用场景和命令速查
故障排查指南 - 常见问题和解决方案

使用示例

场景A1示例 - 采集并发布完整示例
场景B1示例 - 热点文章生成完整示例
场景C2示例 - 转小红书图文完整示例

子技能文档

baoyu-url-to-markdown
article-illustrator
baoyu-xhs-images
wechat-hotspot-publisher
content-creation-publisher

前置准备

环境依赖

# 检查Node.js
node --version

# 检查Chrome
chrome --version

# 检查Python
python --version

账号准备

微信公众号（可选）
- 已注册的公众号账号
- 或配置API凭证
X/Twitter账号（可选）
- 已注册的X账号
- 浏览器中保持登录状态
小红书账号（可选）
- 已注册的小红书账号
B站账号（可选）
- 已注册的B站账号

配置文件（可选）

# 创建配置文件
~/.baoyu-skills/.env

# 配置内容（可选）
WECHAT_APP_ID=your_app_id
WECHAT_APP_SECRET=your_app_secret

使用示例

示例1：采集文章并发布到微信

用户："采集这篇文章并发布到微信公众号"
URL: https://example.com/article

系统识别：场景A1
执行流程：
✓ 场景识别：A1 - 采集并发布
✓ 参数收集：URL已获取
→ 步骤1/4：采集网页内容...
✓ 步骤1/4：内容已采集（3500字）
→ 步骤2/4：格式优化...
✓ 步骤2/4：格式已优化
→ 步骤3/4：生成配图...
✓ 步骤3/4：已生成3张配图
→ 步骤4/4：发布到微信公众号...
✓ 步骤4/4：已推送到草稿箱

✅ 任务完成！
文件：url-to-markdown/example.com/article.md
配图：imgs/illustration-*.png（3张）
微信：已推送到草稿箱，请登录后台查看

示例2：热点内容创作并多平台发布

用户："根据AI热点生成一篇文章，发布到微信和小红书"

系统识别：场景B3
执行流程：
✓ 场景识别：B3 - 热点多平台发布
→ 收集参数：请提供关键词
用户："AI工具"
✓ 参数收集：关键词已获取
→ 步骤1/3：采集热点并生成文章...
✓ 步骤1/3：已生成文章《AI工具的未来：10个必知趋势》
→ 步骤2/3：转换为小红书图文...
✓ 步骤2/3：已生成5张小红书图文
→ 步骤3/3：多平台发布...
✓ 步骤3/3：发布完成

✅ 任务完成！
微信：已推送到草稿箱
小红书：图文已生成（5张），请手动发布
文件：xhs-images/ai-tools/01-cover.png 至 05-ending.png

示例3：文章配图

用户："给这篇文章配图"
文件：article.md

系统识别：场景C1
执行流程：
✓ 场景识别：C1 - 文章配图
✓ 参数收集：文件路径已获取
→ 步骤1/1：分析文章并生成配图...
✓ 步骤1/1：已生成4张配图

✅ 任务完成！
文件：article.md（已更新）
配图：imgs/illustration-*.png（4张）
配图位置：第2、4、6、8段后

示例4：网页转小红书

用户："把这个网页转成小红书图文"
URL: https://example.com/tutorial

系统识别：场景A3
执行流程：
✓ 场景识别：A3 - 采集转小红书
✓ 参数收集：URL已获取
→ 步骤1/2：采集网页内容...
✓ 步骤1/2：内容已采集（2800字）
→ 步骤2/2：生成小红书图文...
✓ 步骤2/2：已生成6张小红书图文

✅ 任务完成！
文件：xhs-images/tutorial/01-cover.png 至 06-ending.png
风格：notion（知识卡片风格）
布局：dense（高信息密度）

注意事项

1. 场景识别

系统会尽量准确识别场景，但可能需要用户确认
如果识别不准确，用户可以明确指定场景
复杂需求可能需要拆分为多个场景

2. 参数收集

系统会自动提取用户输入中的参数
缺失参数会主动询问用户
用户可以在描述需求时一次性提供所有参数

3. 执行效率

完整工作流可能需要3-10分钟
系统会实时反馈进度
可以随时中断执行

4. 平台限制

微信公众号发布需要账号登录
小红书图文需要手动上传
X/Twitter发布需要浏览器登录状态

5. 内容质量

建议发布前人工审核内容
确保符合平台规范
注意版权和原创性

6. 技术限制

部分网站可能有反爬虫机制
网络环境可能影响采集和发布
Chrome依赖可能影响部分功能

高级功能

自定义工作流

用户可以自定义技能调用顺序：

用户："采集这篇文章，先配图，再转小红书，最后发布到微信"

系统识别：自定义工作流
执行流程：
1. baoyu-url-to-markdown（采集）
2. article-illustrator（配图）
3. baoyu-xhs-images（转小红书）
4. wechat-publisher（发布）

批量处理

支持批量处理多个内容：

用户："采集这5篇文章并发布到微信"
URLs: [url1, url2, url3, url4, url5]

系统识别：批量场景A1
执行流程：
对每个URL执行场景A1工作流

定时发布

支持设置定时发布：

用户："采集这篇文章，明天上午10点发布到微信"

系统识别：场景A1 + 定时发布
执行流程：
1. 立即执行采集和配图
2. 设置定时任务，明天10点发布

故障排查

问题1：场景识别错误

症状：系统识别的场景与用户意图不符

解决方案：

用户明确指定场景代码（如"执行场景A1"）
用户更详细地描述需求
查看场景识别规则文档

问题2：参数收集失败

症状：系统无法提取必需参数

解决方案：

用户在描述中明确提供参数
回答系统的参数询问
检查参数格式是否正确

问题3：技能调用失败

症状：某个技能执行失败

解决方案：

查看错误信息
检查环境依赖是否满足
查看对应技能的故障排查文档
尝试单独调用该技能测试

问题4：发布失败

症状：内容无法发布到平台

解决方案：

检查账号登录状态
确认内容符合平台规范
检查网络连接
查看平台配置说明文档

问题5：执行超时

症状：工作流执行时间过长

解决方案：

检查网络连接
简化工作流（减少步骤）
分批处理内容
增加超时时间设置

更新日志

v1.0.0（2026-02-09）

✅ 创建智能内容创作与发布系统
✅ 整合5个核心技能
✅ 实现14个场景支持
✅ 支持4个发布平台
✅ 提供智能场景识别
✅ 实现自动参数收集
✅ 提供实时进度反馈

🚀 一句话完成从内容获取到多平台发布的全流程！

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

anbeime / skill

skills/intelligent-content-system/SKILL.md

View on GitHub ↗

Imported 9/1/2026Version v1Bundled 2 files

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

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

anbeime/content-creation-publisher内容创作与发布全流程技能，整合网页采集、Markdown格式化、智能配图、多平台发布（微信公众号、X/Twitter）功能，实现从内容获取到发布的一站式解决方案Close match+55 pts pass-rate lift

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From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

智能内容创作与发布系统

任务目标

核心特性

1. 智能场景识别

2. 自动参数收集

3. 进度实时反馈

操作步骤

标准流程

可选分支

分支A：用户不确定需求

分支B：参数不完整

分支C：执行失败

场景详解

场景A：网页内容采集与再创作

A1：采集并发布

A2：采集并配图

A3：采集转小红书

场景B：热点内容创作

B1：热点文章生成

B2：热点图文生成

B3：热点多平台发布

场景C：原创内容优化与发布

C1：文章配图

C2：转小红书图文

C3：多平台发布

场景D：单一功能调用

D1：仅采集网页

D2：仅配图

D3：仅小红书图文

D4：仅发布

资源索引

核心文档

使用示例

子技能文档

前置准备

环境依赖

账号准备

配置文件（可选）

使用示例

示例1：采集文章并发布到微信

示例2：热点内容创作并多平台发布

示例3：文章配图

示例4：网页转小红书

注意事项

1. 场景识别

2. 参数收集

3. 执行效率

4. 平台限制

5. 内容质量

6. 技术限制

高级功能

自定义工作流

批量处理

定时发布

故障排查

问题1：场景识别错误

问题2：参数收集失败

问题3：技能调用失败

问题4：发布失败

问题5：执行超时

更新日志

v1.0.0（2026-02-09）

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry