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Post to WeChat Official Account · leoyeai | DecimalAI

Marketingby leoyeai

Post to WeChat Official Account

Posts content to WeChat Official Account (微信公众号) via API or Chrome CDP. Supports article posting (文章) with HTML, markdown, or plain text input, and image-text posting (贴图, formerly 图文) with multiple images. Markdown article workflows default to converting ordinary external links into bottom citations for WeChat-friendly output.

SkillScore

SkillScore55/100

Test results 55

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%55%of test cases passed +41 pts

Without skill3 of 22

With skill12 of 22

9 fixed3 passed either way10 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v117 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI want to publish `post.md` via API. My EXTEND.md sets `default_theme: simple`, but I run the command with `--theme modern`. What theme is used in the command?

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-baoyu-post-to-wechat
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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat

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-baoyu-post-to-wechat/SKILL.md https://app.decimal.ai/s/leoyeai-baoyu-post-to-wechat/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-baoyu-post-to-wechat/SKILL.md

This gets SKILL.md only. The skill's 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/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-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/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-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat")
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-baoyu-post-to-wechat/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-baoyu-post-to-wechat/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 13 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-baoyu-post-to-wechat

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-baoyu-post-to-wechat/SKILL.md https://app.decimal.ai/s/leoyeai-baoyu-post-to-wechat/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-baoyu-post-to-wechat/SKILL.md

This gets SKILL.md only. The skill's 13 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-baoyu-post-to-wechat/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-baoyu-post-to-wechat.json

This gets SKILL.md only. The skill's 13 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

Post to WeChat Official Account

Language

Match user's language: Respond in the same language the user uses. If user writes in Chinese, respond in Chinese. If user writes in English, respond in English.

Script Directory

Agent Execution: Determine this SKILL.md directory as {baseDir}, then use {baseDir}/scripts/<name>.ts. Resolve ${BUN_X} runtime: if bun installed → bun; if npx available → npx -y bun; else suggest installing bun.

Script	Purpose
`scripts/wechat-browser.ts`	Image-text posts (图文)
`scripts/wechat-article.ts`	Article posting via browser (文章)
`scripts/wechat-api.ts`	Article posting via API (文章)
`scripts/md-to-wechat.ts`	Markdown → WeChat-ready HTML with image placeholders
`scripts/check-permissions.ts`	Verify environment & permissions

Preferences (EXTEND.md)

Check EXTEND.md existence (priority order):

# macOS, Linux, WSL, Git Bash
test -f .baoyu-skills/baoyu-post-to-wechat/EXTEND.md && echo "project"
test -f "${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-post-to-wechat/EXTEND.md" && echo "xdg"
test -f "$HOME/.baoyu-skills/baoyu-post-to-wechat/EXTEND.md" && echo "user"

# PowerShell (Windows)
if (Test-Path .baoyu-skills/baoyu-post-to-wechat/EXTEND.md) { "project" }
$xdg = if ($env:XDG_CONFIG_HOME) { $env:XDG_CONFIG_HOME } else { "$HOME/.config" }
if (Test-Path "$xdg/baoyu-skills/baoyu-post-to-wechat/EXTEND.md") { "xdg" }
if (Test-Path "$HOME/.baoyu-skills/baoyu-post-to-wechat/EXTEND.md") { "user" }

┌────────────────────────────────────────────────────────┬───────────────────┐ │ Path │ Location │ ├────────────────────────────────────────────────────────┼───────────────────┤ │ .baoyu-skills/baoyu-post-to-wechat/EXTEND.md │ Project directory │ ├────────────────────────────────────────────────────────┼───────────────────┤ │ $HOME/.baoyu-skills/baoyu-post-to-wechat/EXTEND.md │ User home │ └────────────────────────────────────────────────────────┴───────────────────┘

┌───────────┬───────────────────────────────────────────────────────────────────────────┐ │ Result │ Action │ ├───────────┼───────────────────────────────────────────────────────────────────────────┤ │ Found │ Read, parse, apply settings │ ├───────────┼───────────────────────────────────────────────────────────────────────────┤ │ Not found │ Run first-time setup (references/config/first-time-setup.md) → Save → Continue │ └───────────┴───────────────────────────────────────────────────────────────────────────┘

First-time setup: references/config/first-time-setup.md

Minimum supported keys (case-insensitive, accept 1/0 or true/false):

Key	Default	Mapping
`default_author`	empty	Fallback for `author` when CLI/frontmatter not provided
`need_open_comment`	`1`	`articles[].need_open_comment` in `draft/add` request
`only_fans_can_comment`	`0`	`articles[].only_fans_can_comment` in `draft/add` request

Recommended EXTEND.md example:

default_theme: default
default_color: blue
default_publish_method: api
default_author: 宝玉
need_open_comment: 1
only_fans_can_comment: 0
chrome_profile_path: /path/to/chrome/profile

Theme options: default, grace, simple, modern

Color presets: blue, green, vermilion, yellow, purple, sky, rose, olive, black, gray, pink, red, orange (or hex value)

Value priority:

CLI arguments
Frontmatter
EXTEND.md (account-level → global-level)
Skill defaults

Multi-Account Support

EXTEND.md supports managing multiple WeChat Official Accounts. When accounts: block is present, each account can have its own credentials, Chrome profile, and default settings.

Compatibility rules:

Condition	Mode	Behavior
No `accounts` block	Single-account	Current behavior, unchanged
`accounts` with 1 entry	Single-account	Auto-select, no prompt
`accounts` with 2+ entries	Multi-account	Prompt to select before publishing
`accounts` with `default: true`	Multi-account	Pre-select default, user can switch

Multi-account EXTEND.md example:

default_theme: default
default_color: blue

accounts:
  - name: 宝玉的技术分享
    alias: baoyu
    default: true
    default_publish_method: api
    default_author: 宝玉
    need_open_comment: 1
    only_fans_can_comment: 0
    app_id: your_wechat_app_id
    app_secret: your_wechat_app_secret
  - name: AI工具集
    alias: ai-tools
    default_publish_method: browser
    default_author: AI工具集
    need_open_comment: 1
    only_fans_can_comment: 0

Per-account keys (can be set per-account or globally as fallback): default_publish_method, default_author, need_open_comment, only_fans_can_comment, app_id, app_secret, chrome_profile_path

Global-only keys (always shared across accounts): default_theme, default_color

Account Selection (Step 0.5)

Insert between Step 0 and Step 1 in the Article Posting Workflow:

if no accounts block:
    → single-account mode (current behavior)
elif accounts.length == 1:
    → auto-select the only account
elif --account <alias> CLI arg:
    → select matching account
elif one account has default: true:
    → pre-select, show: "Using account: <name> (--account to switch)"
else:
    → prompt user:
      "Multiple WeChat accounts configured:
       1) <name1> (<alias1>)
       2) <name2> (<alias2>)
       Select account [1-N]:"

Credential Resolution (API Method)

For a selected account with alias {alias}:

app_id / app_secret inline in EXTEND.md account block
Env var WECHAT_{ALIAS}_APP_ID / WECHAT_{ALIAS}_APP_SECRET (alias uppercased, hyphens → underscores)
.baoyu-skills/.env with prefixed key WECHAT_{ALIAS}_APP_ID
~/.baoyu-skills/.env with prefixed key
Fallback to unprefixed WECHAT_APP_ID / WECHAT_APP_SECRET

.env multi-account example:

# Account: baoyu
WECHAT_BAOYU_APP_ID=your_wechat_app_id
WECHAT_BAOYU_APP_SECRET=your_wechat_app_secret

# Account: ai-tools
WECHAT_AI_TOOLS_APP_ID=your_ai_tools_wechat_app_id
WECHAT_AI_TOOLS_APP_SECRET=your_ai_tools_wechat_app_secret

Chrome Profile (Browser Method)

Each account uses an isolated Chrome profile for independent login sessions:

Source	Path
Account `chrome_profile_path` in EXTEND.md	Use as-is
Auto-generated from alias	`{shared_profile_parent}/wechat-{alias}/`
Single-account fallback	Shared default profile (current behavior)

CLI `--account` Argument

All publishing scripts accept --account <alias>:

${BUN_X} {baseDir}/scripts/wechat-api.ts <file> --theme default --account ai-tools
${BUN_X} {baseDir}/scripts/wechat-article.ts --markdown <file> --theme default --account baoyu
${BUN_X} {baseDir}/scripts/wechat-browser.ts --markdown <file> --images ./photos/ --account baoyu

Pre-flight Check (Optional)

Before first use, suggest running the environment check. User can skip if they prefer.

${BUN_X} {baseDir}/scripts/check-permissions.ts

Checks: Chrome, profile isolation, Bun, Accessibility, clipboard, paste keystroke, API credentials, Chrome conflicts.

If any check fails, provide fix guidance per item:

Check	Fix
Chrome	Install Chrome or set `WECHAT_BROWSER_CHROME_PATH` env var
Profile dir	Shared profile at `baoyu-skills/chrome-profile` (see CLAUDE.md Chrome Profile section)
Bun runtime	`brew install oven-sh/bun/bun` (macOS) or `npm install -g bun`
Accessibility (macOS)	System Settings → Privacy & Security → Accessibility → enable terminal app
Clipboard copy	Ensure Swift/AppKit available (macOS Xcode CLI tools: `xcode-select --install`)
Paste keystroke (macOS)	Same as Accessibility fix above
Paste keystroke (Linux)	Install `xdotool` (X11) or `ydotool` (Wayland)
API credentials	Follow guided setup in Step 2, or manually set in `.baoyu-skills/.env`

Image-Text Posting (图文)

For short posts with multiple images (up to 9):

${BUN_X} {baseDir}/scripts/wechat-browser.ts --markdown article.md --images ./images/
${BUN_X} {baseDir}/scripts/wechat-browser.ts --title "标题" --content "内容" --image img.png --submit

See references/image-text-posting.md for details.

Article Posting Workflow (文章)

Copy this checklist and check off items as you complete them:

Publishing Progress:
- [ ] Step 0: Load preferences (EXTEND.md)
- [ ] Step 0.5: Resolve account (multi-account only)
- [ ] Step 1: Determine input type
- [ ] Step 2: Select method and configure credentials
- [ ] Step 3: Resolve theme/color and validate metadata
- [ ] Step 4: Publish to WeChat
- [ ] Step 5: Report completion

Step 0: Load Preferences

Check and load EXTEND.md settings (see Preferences section above).

CRITICAL: If not found, complete first-time setup BEFORE any other steps or questions.

Resolve and store these defaults for later steps:

default_theme (default default)
default_color (omit if not set — theme default applies)
default_author
need_open_comment (default 1)
only_fans_can_comment (default 0)

Step 1: Determine Input Type

Input Type	Detection	Action
HTML file	Path ends with `.html`, file exists	Skip to Step 3
Markdown file	Path ends with `.md`, file exists	Continue to Step 2
Plain text	Not a file path, or file doesn't exist	Save to markdown, continue to Step 2

Plain Text Handling:

Generate slug from content (first 2-4 meaningful words, kebab-case)
Create directory and save file:

mkdir -p "$(pwd)/post-to-wechat/$(date +%Y-%m-%d)"
# Save content to: post-to-wechat/yyyy-MM-dd/[slug].md

Continue processing as markdown file

Slug Examples:

"Understanding AI Models" → understanding-ai-models
"人工智能的未来" → ai-future (translate to English for slug)

Step 2: Select Publishing Method and Configure

Ask publishing method (unless specified in EXTEND.md or CLI):

Method	Speed	Requirements
`api` (Recommended)	Fast	API credentials
`browser`	Slow	Chrome, login session

If API Selected - Check Credentials:

# macOS, Linux, WSL, Git Bash
test -f .baoyu-skills/.env && grep -q "WECHAT_APP_ID" .baoyu-skills/.env && echo "project"
test -f "$HOME/.baoyu-skills/.env" && grep -q "WECHAT_APP_ID" "$HOME/.baoyu-skills/.env" && echo "user"

# PowerShell (Windows)
if ((Test-Path .baoyu-skills/.env) -and (Select-String -Quiet -Pattern "WECHAT_APP_ID" .baoyu-skills/.env)) { "project" }
if ((Test-Path "$HOME/.baoyu-skills/.env") -and (Select-String -Quiet -Pattern "WECHAT_APP_ID" "$HOME/.baoyu-skills/.env")) { "user" }

If Credentials Missing - Guide Setup:

WeChat API credentials not found.

To obtain credentials:
1. Visit https://mp.weixin.qq.com
2. Go to: 开发 → 基本配置
3. Copy AppID and AppSecret

Where to save?
A) Project-level: .baoyu-skills/.env (this project only)
B) User-level: ~/.baoyu-skills/.env (all projects)

After location choice, prompt for values and write to .env:

WECHAT_APP_ID=<user_input>
WECHAT_APP_SECRET=<user_input>

Step 3: Resolve Theme/Color and Validate Metadata

Resolve theme (first match wins, do NOT ask user if resolved):
- CLI --theme argument
- EXTEND.md default_theme (loaded in Step 0)
- Fallback: default
Resolve color (first match wins):
- CLI --color argument
- EXTEND.md default_color (loaded in Step 0)
- Omit if not set (theme default applies)
Validate metadata from frontmatter (markdown) or HTML meta tags (HTML input):

Field	If Missing
Title	Prompt: "Enter title, or press Enter to auto-generate from content"
Summary	Prompt: "Enter summary, or press Enter to auto-generate (recommended for SEO)"
Author	Use fallback chain: CLI `--author` → frontmatter `author` → EXTEND.md `default_author`

Auto-Generation Logic:

Title: First H1/H2 heading, or first sentence
Summary: First paragraph, truncated to 120 characters

Cover Image Check (required for API article_type=news):
1. Use CLI --cover if provided.
2. Else use frontmatter (coverImage, featureImage, cover, image).
3. Else check article directory default path: imgs/cover.png.
4. Else fallback to first inline content image.
5. If still missing, stop and request a cover image before publishing.

Step 4: Publish to WeChat

CRITICAL: Publishing scripts handle markdown conversion internally. Do NOT pre-convert markdown to HTML — pass the original markdown file directly. This ensures the API method renders images as <img> tags (for API upload) while the browser method uses placeholders (for paste-and-replace workflow).

Markdown citation default:

For markdown input, ordinary external links are converted to bottom citations by default.
Use --no-cite only if the user explicitly wants to keep ordinary external links inline.
Existing HTML input is left as-is; no extra citation conversion is applied.

API method (accepts .md or .html):

${BUN_X} {baseDir}/scripts/wechat-api.ts <file> --theme <theme> [--color <color>] [--title <title>] [--summary <summary>] [--author <author>] [--cover <cover_path>] [--no-cite]

CRITICAL: Always include --theme parameter. Never omit it, even if using default. Only include --color if explicitly set by user or EXTEND.md.

draft/add payload rules:

Use endpoint: POST https://api.weixin.qq.com/cgi-bin/draft/add?access_token=ACCESS_TOKEN
article_type: news (default) or newspic
For news, include thumb_media_id (cover is required)
Always resolve and send:
- need_open_comment (default 1)
- only_fans_can_comment (default 0)
author resolution: CLI --author → frontmatter author → EXTEND.md default_author

If script parameters do not expose the two comment fields, still ensure final API request body includes resolved values.

Browser method (accepts --markdown or --html):

${BUN_X} {baseDir}/scripts/wechat-article.ts --markdown <markdown_file> --theme <theme> [--color <color>] [--no-cite]
${BUN_X} {baseDir}/scripts/wechat-article.ts --html <html_file>

Step 5: Completion Report

For API method, include draft management link:

WeChat Publishing Complete!

Input: [type] - [path]
Method: API
Theme: [theme name] [color if set]

Article:
• Title: [title]
• Summary: [summary]
• Images: [N] inline images
• Comments: [open/closed], [fans-only/all users]

Result:
✓ Draft saved to WeChat Official Account
• media_id: [media_id]

Next Steps:
→ Manage drafts: https://mp.weixin.qq.com (登录后进入「内容管理」→「草稿箱」)

Files created:
[• post-to-wechat/yyyy-MM-dd/slug.md (if plain text)]
[• slug.html (converted)]

For Browser method:

WeChat Publishing Complete!

Input: [type] - [path]
Method: Browser
Theme: [theme name] [color if set]

Article:
• Title: [title]
• Summary: [summary]
• Images: [N] inline images

Result:
✓ Draft saved to WeChat Official Account

Files created:
[• post-to-wechat/yyyy-MM-dd/slug.md (if plain text)]
[• slug.html (converted)]

Detailed References

Topic	Reference
Image-text parameters, auto-compression	references/image-text-posting.md
Article themes, image handling	references/article-posting.md

Feature Comparison

Feature	Image-Text	Article (API)	Article (Browser)
Plain text input	✗	✓	✓
HTML input	✗	✓	✓
Markdown input	Title/content	✓	✓
Multiple images	✓ (up to 9)	✓ (inline)	✓ (inline)
Themes	✗	✓	✓
Auto-generate metadata	✗	✓	✓
Default cover fallback (`imgs/cover.png`)	✗	✓	✗
Comment control (`need_open_comment`, `only_fans_can_comment`)	✗	✓	✗
Requires Chrome	✓	✗	✓
Requires API credentials	✗	✓	✗
Speed	Medium	Fast	Slow

Prerequisites

For API method:

WeChat Official Account API credentials
Guided setup in Step 2, or manually set in .baoyu-skills/.env

For Browser method:

Google Chrome
First run: log in to WeChat Official Account (session preserved)

Config File Locations (priority order):

Environment variables
<cwd>/.baoyu-skills/.env
~/.baoyu-skills/.env

Troubleshooting

Issue	Solution
Missing API credentials	Follow guided setup in Step 2
Access token error	Check if API credentials are valid and not expired
Not logged in (browser)	First run opens browser - scan QR to log in
Chrome not found	Set `WECHAT_BROWSER_CHROME_PATH` env var
Title/summary missing	Use auto-generation or provide manually
No cover image	Add frontmatter cover or place `imgs/cover.png` in article directory
Wrong comment defaults	Check `EXTEND.md` keys `need_open_comment` and `only_fans_can_comment`
Paste fails	Check system clipboard permissions

Extension Support

Custom configurations via EXTEND.md. See Preferences section for paths and supported options.

When it activates

Automatically, when a task matches. Use when user mentions "发布公众号", "post to wechat", "微信公众号", or "贴图/图文/文章".

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 — 8/2/2026

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

LeoYeAI / openclaw-master-skills

skills/baoyu-post-to-wechat-2/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 13 files · scripts

Illustrative Want to see what leoyeai/baoyu-post-to-wechat 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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Related skills

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SkillScore

Safety

What it changes

Track record

Use this skill

Post to WeChat Official Account

Language

Script Directory

Preferences (EXTEND.md)

Multi-Account Support

Account Selection (Step 0.5)

Credential Resolution (API Method)

Chrome Profile (Browser Method)

CLI --account Argument

Pre-flight Check (Optional)

Image-Text Posting (图文)

Article Posting Workflow (文章)

Step 0: Load Preferences

Step 1: Determine Input Type

Step 2: Select Publishing Method and Configure

Step 3: Resolve Theme/Color and Validate Metadata

Step 4: Publish to WeChat

Step 5: Completion Report

Detailed References

Feature Comparison

Prerequisites

Troubleshooting

Extension Support

When it activates

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry

CLI `--account` Argument