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Ding Skills · leoyeai | DecimalAI

Productivityby leoyeai

Ding Skills

钉钉操作助手（DingTalk）。当用户通过钉钉对话、或提到以下任何场景时必须使用此技能：查人（查下某某、搜一下某人、找一下谁谁）、查部门、查手机号、查工号、约会议（预约会议、创建会议、安排会议、开个会）、发消息（给某人发消息、群里发个通知）、查审批（我的审批、待审批、审批状态）、发起审批、同意/拒绝审批、查日程、创建日程、查员工数、查离职、开视频会议、钉钉知识库（查钉钉知识库、在钉钉创建文档、搜索钉钉文档、覆写钉钉文档）。注意：当用户从钉钉渠道发送消息时，"知识库"默认指钉钉知识库，不是飞书知识库。

Never made a case worse

SkillScore

SkillScore88/100

Test results 88

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

16%88%of test cases passed +72 pts

Without skill4 of 25

With skill22 of 25

18 fixed4 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v122 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskA manager with userId 'mgr001' needs to approve an OA approval instance 'inst_999' with a comment 'Approved'. Which command should be executed?

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-ding-skills
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-ding-skills")
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-ding-skills

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-ding-skills/SKILL.md https://app.decimal.ai/s/leoyeai-ding-skills/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-ding-skills/SKILL.md

This gets SKILL.md only. The skill's 45 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-ding-skills

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-ding-skills")
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-ding-skills/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-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/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-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills")
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-ding-skills/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-ding-skills/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 45 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-ding-skills

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-ding-skills/SKILL.md https://app.decimal.ai/s/leoyeai-ding-skills/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-ding-skills/SKILL.md

This gets SKILL.md only. The skill's 45 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-ding-skills/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-ding-skills.json

This gets SKILL.md only. The skill's 45 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

scripts/

SKILL.md

Ding Skills

钉钉全功能技能集：用户管理、部门管理、消息发送、OA审批、视频会议、日程管理。

前置要求

已设置环境变量 DINGTALK_APP_KEY 和 DINGTALK_APP_SECRET
钉钉应用已创建并拥有相应 API 权限

环境变量配置

export DINGTALK_APP_KEY="<your-app-key>"
export DINGTALK_APP_SECRET="<your-app-secret>"
export DINGTALK_ROBOT_CODE="<your-robot-code>"  # 可选，发消息时使用

重要：常用工作流（必读）

大部分钉钉 API 需要 userId 或 unionId，但用户通常只会说人名。遇到人名时，必须先查人再执行操作。

工作流1：按人名预约会议 / 创建视频会议

当用户说"帮我和张三、李四开个会"或"预约一个会议，参会人：张三、李四"时：

步骤1: python scripts/search_user.py "张三"  → 得到 userId
步骤2: python scripts/get_user.py "<userId>"  → 得到 unionId
步骤3: 对每个参会人重复步骤1-2
步骤4: python scripts/create_schedule_conference.py "<主题>" "<发起人unionId>" "<开始时间>" "<结束时间>" "<参会人unionId1,unionId2>" "[会议地点]"

工作流2：按人名发消息

当用户说"给张三发个消息"时：

步骤1: python scripts/search_user.py "张三"  → 得到 userId
步骤2: python scripts/send_user_message.py "<userId>" "<消息内容>"

注意：robotCode 自动从环境变量 DINGTALK_ROBOT_CODE 读取，也可作为第3个参数手动传入。

工作流3：按人名查审批

当用户说"查下张三的待审批"时：

步骤1: python scripts/search_user.py "张三"  → 得到 userId
步骤2: python scripts/list_user_todo_approvals.py "<userId>"

工作流4：按人名查日程

当用户说"查下张三今天的日程"时：

步骤1: python scripts/search_user.py "张三"  → 得到 userId
步骤2: python scripts/get_user.py "<userId>"  → 得到 unionId
步骤3: python scripts/list_events.py "<unionId>" "[开始时间]" "[结束时间]"

工作流5：在知识库中创建文档

当用户说"在知识库里创建一个文档"时：

步骤1: python scripts/search_user.py "张三"  → 得到 userId
步骤2: python scripts/get_user.py "<userId>"  → 得到 unionId
步骤3: python scripts/list_workspaces.py "<unionId>"  → 得到 workspaceId
步骤4: python scripts/create_doc.py "<workspaceId>" "<文档名>" "<unionId>"

工作流6：搜索知识库文档获取链接

当用户说"帮我找一下知识库里的《周报》"时：

步骤1: python scripts/search_user.py "张三"  → 得到 userId
步骤2: python scripts/get_user.py "<userId>"  → 得到 unionId
步骤3: python scripts/search_doc.py "<unionId>" "周报"  → 得到文档链接

通用规则

用户说人名 → 必须先调用 search_user.py 获取 userId
需要 unionId 的 API（日历、会议相关） → 再调用 get_user.py 从 userId 获取 unionId
需要 userId 的 API（消息、审批、部门相关） → search_user.py 的结果可直接使用
可以并行查询多个用户以提高效率

功能列表

1. 搜索用户 (search-user)

根据姓名搜索用户，返回匹配的 UserId 列表。

python scripts/search_user.py "<搜索关键词>"

输出：

{
  "success": true,
  "keyword": "张三",
  "totalCount": 3,
  "hasMore": false,
  "userIds": ["123456789", "987654321"]
}

2. 查询用户详情 (get-user)

获取指定用户的详细信息。

python scripts/get_user.py "<userId>"

输出：

{
  "success": true,
  "user": {
    "userid": "user001",
    "name": "张三",
    "mobile": "138****1234",
    "dept_id_list": [12345],
    "unionid": "xxxxx"
  }
}

3. 根据手机号查询用户 (get-user-by-mobile)

python scripts/get_user_by_mobile.py "<手机号>"

输出：

{ "success": true, "mobile": "13800138000", "userId": "user001" }

4. 根据 unionid 查询用户 (get-user-by-unionid)

python scripts/get_user_by_unionid.py "<unionid>"

输出：

{ "success": true, "unionid": "xxxxx", "userId": "user001" }

5. 获取员工人数 (get-user-count)

python scripts/get_user_count.py [--onlyActive]

输出：

{ "success": true, "onlyActive": false, "count": 150 }

6. 获取用户待审批数量 (get-user-todo-count)

python scripts/get_user_todo_count.py "<userId>"

输出：

{ "success": true, "userId": "user001", "count": 5 }

7. 获取未登录用户列表 (list-inactive-users)

python scripts/list_inactive_users.py "<queryDate>" [--deptIds "id1,id2"] [--offset 0] [--size 100]

queryDate 格式: yyyyMMdd

输出：

{ "success": true, "queryDate": "20240115", "userIds": ["user001"], "hasMore": false }

8. 查询离职记录列表 (list-resigned-users)

python scripts/list_resigned_users.py "<startTime>" ["<endTime>"] [--nextToken "xxx"] [--maxResults 100]

startTime/endTime 格式: ISO8601

输出：

{
  "success": true,
  "startTime": "2024-01-01T00:00:00+08:00",
  "records": [{ "userId": "user001", "name": "张三", "leaveTime": "2024-01-15T10:00:00Z" }]
}

9. 搜索部门 (search-department)

python scripts/search_department.py "<搜索关键词>"

输出：

{ "success": true, "keyword": "技术部", "totalCount": 2, "departmentIds": [12345, 67890] }

10. 获取部门详情 (get-department)

python scripts/get_department.py "<deptId>"

输出：

{ "success": true, "department": { "deptId": 12345, "name": "技术部", "parentId": 1 } }

11. 获取子部门列表 (list-sub-departments)

根部门 deptId = 1。

python scripts/list_sub_departments.py "<deptId>"

输出：

{ "success": true, "deptId": 1, "subDepartmentIds": [12345, 67890] }

12. 获取部门用户列表 (list-department-users)

自动分页获取所有用户（简略信息）。

python scripts/list_department_users.py "<deptId>"

输出：

{
  "success": true,
  "deptId": 12345,
  "users": [{ "userId": "user001", "name": "张三" }, { "userId": "user002", "name": "李四" }]
}

13. 获取部门用户详情 (list-department-user-details)

分页获取，支持 cursor 和 size。

python scripts/list_department_user_details.py "<deptId>" [--cursor 0] [--size 100]

输出：

{ "success": true, "deptId": 12345, "users": [...], "hasMore": true, "nextCursor": 100 }

14. 获取部门用户 ID 列表 (list-department-user-ids)

python scripts/list_department_user_ids.py "<deptId>"

输出：

{ "success": true, "deptId": 12345, "userIds": ["user001", "user002"] }

15. 获取部门父部门链 (list-department-parents)

python scripts/list_department_parents.py "<deptId>"

输出：

{ "success": true, "deptId": 12345, "parentIdList": [12345, 67890, 1] }

16. 获取用户所属部门父部门链 (list-user-parent-departments)

python scripts/list_user_parent_departments.py "<userId>"

输出：

{ "success": true, "userId": "user001", "parentIdList": [12345, 1] }

17. 获取群内机器人列表 (get-bot-list)

python scripts/get_bot_list.py "<openConversationId>"

输出：

{
  "success": true,
  "openConversationId": "cid",
  "botList": [{ "robotCode": "code", "robotName": "name" }]
}

18. 机器人发送群消息 (send-group-message)

robotCode 自动从环境变量 DINGTALK_ROBOT_CODE 读取，也可作为第3个参数手动传入。

python scripts/send_group_message.py "<openConversationId>" "<消息内容>" ["<robotCode>"]

输出：

{ "success": true, "openConversationId": "cid", "robotCode": "code", "processQueryKey": "key", "message": "消息内容" }

19. 机器人发送单聊消息 (send-user-message)

robotCode 自动从环境变量 DINGTALK_ROBOT_CODE 读取，也可作为第3个参数手动传入。

python scripts/send_user_message.py "<userId>" "<消息内容>" ["<robotCode>"]

输出：

{ "success": true, "userId": "user001", "robotCode": "code", "processQueryKey": "key", "message": "消息内容" }

20. 获取审批实例 ID 列表 (list-approval-instance-ids)

python scripts/list_approval_instance_ids.py "<processCode>" --startTime <timestamp> --endTime <timestamp> [--size 20] [--nextToken "xxx"]

输出：

{ "success": true, "processCode": "PROC-XXX", "instanceIds": ["id1", "id2"], "totalCount": 2, "hasMore": false }

21. 获取审批实例详情 (get-approval-instance)

python scripts/get_approval_instance.py "<instanceId>"

输出：

{
  "success": true,
  "instanceId": "xxx-123",
  "instance": {
    "processInstanceId": "xxx-123",
    "title": "请假申请",
    "status": "COMPLETED",
    "formComponentValues": [...],
    "tasks": [...]
  }
}

22. 查询用户发起的审批 (list-user-initiated-approvals)

python scripts/list_user_initiated_approvals.py "<userId>" [--startTime <ts>] [--endTime <ts>] [--maxResults 20]

输出：

{ "success": true, "userId": "user001", "instances": [...], "totalCount": 5, "hasMore": false }

23. 查询用户抄送的审批 (list-user-cc-approvals)

python scripts/list_user_cc_approvals.py "<userId>" [--startTime <ts>] [--endTime <ts>] [--maxResults 20]

24. 查询用户待审批实例 (list-user-todo-approvals)

python scripts/list_user_todo_approvals.py "<userId>" [--maxResults 20]

输出：

{ "success": true, "userId": "user001", "instances": [...], "totalCount": 3, "hasMore": false }

25. 查询用户已审批实例 (list-user-done-approvals)

python scripts/list_user_done_approvals.py "<userId>" [--startTime <ts>] [--endTime <ts>] [--maxResults 20]

26. 发起审批实例 (create-approval-instance)

python scripts/create_approval_instance.py "<processCode>" "<originatorUserId>" "<deptId>" '<formValuesJson>' [--ccList "user1,user2"]

formValuesJson 示例: '[{"name":"标题","value":"请假申请"}]'

输出：

{ "success": true, "processCode": "PROC-XXX", "originatorUserId": "user001", "instanceId": "xxx-new" }

27. 撤销审批实例 (terminate-approval-instance)

python scripts/terminate_approval_instance.py "<instanceId>" "<operatingUserId>" ["<remark>"]

输出：

{ "success": true, "instanceId": "xxx-123", "message": "审批实例已撤销" }

28. 执行审批任务 (execute-approval-task)

同意或拒绝审批任务。

python scripts/execute_approval_task.py "<instanceId>" "<userId>" "<agree|refuse>" [--taskId "xxx"] [--remark "审批意见"]

输出：

{ "success": true, "instanceId": "xxx-123", "userId": "user001", "action": "agree", "message": "已同意审批" }

29. 转交审批任务 (transfer-approval-task)

python scripts/transfer_approval_task.py "<instanceId>" "<userId>" "<transferToUserId>" [--taskId "xxx"] [--remark "转交原因"]

输出：

{ "success": true, "instanceId": "xxx-123", "userId": "user001", "transferToUserId": "user002", "message": "审批任务已转交" }

30. 添加审批评论 (add-approval-comment)

python scripts/add_approval_comment.py "<instanceId>" "<commentUserId>" "<评论内容>"

输出：

{ "success": true, "instanceId": "xxx-123", "userId": "user001", "message": "评论已添加" }

31. 创建即时视频会议 (create-video-conference)

立即创建视频会议并邀请参会人。

python scripts/create_video_conference.py "<会议主题>" "<发起人unionId>" "[邀请人unionId1,unionId2]"

输出：

{ "success": true, "title": "测试会议", "conferenceId": "xxx", "conferencePassword": "123456" }

32. 关闭视频会议 (close-video-conference)

python scripts/close_video_conference.py "<conferenceId>" "<操作人unionId>"

输出：

{ "success": true, "conferenceId": "xxx", "message": "视频会议已关闭" }

33. 创建预约会议 (create-schedule-conference)

通过日历 API 创建预约会议，自动关联钉钉视频会议，日程会出现在钉钉日历中。

python scripts/create_schedule_conference.py "<会议主题>" "<创建人unionId>" "<开始时间>" "<结束时间>" "[参会人unionId1,unionId2]" "[会议地点]"

时间格式: "2026-03-16 14:00" 或 ISO 8601

输出：

{
  "success": true,
  "title": "周会",
  "eventId": "NXZCUEtxOGZMN3JpcDQ3ZE45UVRFdz09",
  "onlineMeetingUrl": "dingtalk://...",
  "conferenceId": "xxx",
  "startTime": "2026-03-16T14:00:00+08:00",
  "endTime": "2026-03-16T15:00:00+08:00",
  "attendeeCount": 2
}

34. 取消预约会议 (cancel-schedule-conference)

python scripts/cancel_schedule_conference.py "<scheduleConferenceId>" "<创建人unionId>"

输出：

{ "success": true, "scheduleConferenceId": "xxx", "message": "预约会议已取消" }

35. 查询日程列表 (list-events)

python scripts/list_events.py "<用户unionId>" [--time-min "2026-03-01 00:00"] [--time-max "2026-03-31 23:59"]

输出：

{
  "success": true,
  "totalCount": 5,
  "events": [{ "id": "eventId", "summary": "周会", "start": {...}, "end": {...} }]
}

36. 查询日程详情 (get-event)

python scripts/get_event.py "<用户unionId>" "<eventId>"

输出：

{
  "success": true,
  "event": { "id": "eventId", "summary": "周会", "attendees": [...], "onlineMeetingInfo": {...} }
}

37. 删除日程 (delete-event)

python scripts/delete_event.py "<用户unionId>" "<eventId>" [--push-notification]

输出：

{ "success": true, "eventId": "xxx", "message": "日程已删除" }

38. 添加日程参与者 (add-event-attendee)

python scripts/add_event_attendee.py "<用户unionId>" "<eventId>" "<参与者unionId1,unionId2>"

输出：

{ "success": true, "eventId": "xxx", "addedCount": 2, "message": "已添加 2 位参与者" }

39. 移除日程参与者 (remove-event-attendee)

python scripts/remove_event_attendee.py "<用户unionId>" "<eventId>" "<参与者unionId1,unionId2>"

输出：

{ "success": true, "eventId": "xxx", "removedCount": 1, "message": "已移除 1 位参与者" }

40. 获取知识库列表 (list-workspaces)

获取用户能访问的所有知识库。

python scripts/list_workspaces.py "<操作人unionId>"

输出：

{
  "success": true,
  "totalCount": 2,
  "workspaces": [
    { "workspaceId": "xxx", "name": "技术部知识库", "type": "TEAM", "url": "https://...", "rootNodeId": "yyy" }
  ]
}

41. 创建知识库文档 (create-doc)

在指定知识库中创建新文档。

python scripts/create_doc.py "<workspaceId>" "<文档名>" "<操作人unionId>" ["<docType>"]

docType 可选值：alidoc（钉钉文档，默认）、alisheet（表格）、alinote（笔记）

输出：

{
  "success": true,
  "name": "周报",
  "docType": "alidoc",
  "workspaceId": "xxx",
  "nodeId": "yyy",
  "docKey": "zzz",
  "url": "https://..."
}

42. 搜索知识库文档 (search-doc)

根据文档名关键词搜索知识库文档，返回文档链接。

python scripts/search_doc.py "<操作人unionId>" "<文档名关键词>" ["<workspaceId>"]

不指定 workspaceId 时搜索所有知识库。

输出：

{
  "success": true,
  "keyword": "周报",
  "totalCount": 3,
  "documents": [
    { "name": "3月第2周周报", "nodeId": "xxx", "url": "https://...", "category": "ALIDOC", "workspaceName": "技术部知识库" }
  ]
}

43. 覆写文档内容 (overwrite-doc)

覆写知识库文档的全部内容（全量替换，非追加）。

python scripts/overwrite_doc.py "<workspaceId>" "<nodeId>" "<操作人unionId>" "<内容>"

输出：

{ "success": true, "workspaceId": "xxx", "nodeId": "yyy", "message": "文档内容已覆写" }

错误处理

所有脚本在错误时返回统一格式：

{
  "success": false,
  "error": {
    "code": "ERROR_CODE",
    "message": "错误描述"
  }
}

常见错误码：

MISSING_CREDENTIALS - 未设置环境变量
INVALID_ARGS - 参数不足
UNKNOWN_ERROR - API 调用异常

重要说明

userId 是企业内部用户 ID，unionId 是全局唯一标识
会议、日程、知识库相关的 API 使用 unionId，可通过 get-user 查询获取
根部门 deptId 为 1
知识库 workspaceId 通过 list-workspaces 获取，nodeId 通过 search-doc 获取

When it activates

Automatically, when a task matches. Use when user mentions anything about DingTalk or when the message comes from a DingTalk channel: looking up people, searching users/departments, scheduling meetings, creating conferences, sending messages, managing approvals, checking calendar ev

About this skill

Automatic

Personal Assistant pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/ding-skills/SKILL.md

View on GitHub ↗

License MITImported 11/2/2026Version v1Bundled 45 files · scripts

Illustrative Want to see what leoyeai/ding-skills 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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What it changes

Track record

Use this skill

Ding Skills

前置要求

环境变量配置

重要：常用工作流（必读）

工作流1：按人名预约会议 / 创建视频会议

工作流2：按人名发消息

工作流3：按人名查审批

工作流4：按人名查日程

工作流5：在知识库中创建文档

工作流6：搜索知识库文档获取链接

通用规则

功能列表

1. 搜索用户 (search-user)

2. 查询用户详情 (get-user)

3. 根据手机号查询用户 (get-user-by-mobile)

4. 根据 unionid 查询用户 (get-user-by-unionid)

5. 获取员工人数 (get-user-count)

6. 获取用户待审批数量 (get-user-todo-count)

7. 获取未登录用户列表 (list-inactive-users)

8. 查询离职记录列表 (list-resigned-users)

9. 搜索部门 (search-department)

10. 获取部门详情 (get-department)

11. 获取子部门列表 (list-sub-departments)

12. 获取部门用户列表 (list-department-users)

13. 获取部门用户详情 (list-department-user-details)

14. 获取部门用户 ID 列表 (list-department-user-ids)

15. 获取部门父部门链 (list-department-parents)

16. 获取用户所属部门父部门链 (list-user-parent-departments)

17. 获取群内机器人列表 (get-bot-list)

18. 机器人发送群消息 (send-group-message)

19. 机器人发送单聊消息 (send-user-message)

20. 获取审批实例 ID 列表 (list-approval-instance-ids)

21. 获取审批实例详情 (get-approval-instance)

22. 查询用户发起的审批 (list-user-initiated-approvals)

23. 查询用户抄送的审批 (list-user-cc-approvals)

24. 查询用户待审批实例 (list-user-todo-approvals)

25. 查询用户已审批实例 (list-user-done-approvals)

26. 发起审批实例 (create-approval-instance)

27. 撤销审批实例 (terminate-approval-instance)

28. 执行审批任务 (execute-approval-task)

29. 转交审批任务 (transfer-approval-task)

30. 添加审批评论 (add-approval-comment)

31. 创建即时视频会议 (create-video-conference)

32. 关闭视频会议 (close-video-conference)

33. 创建预约会议 (create-schedule-conference)

34. 取消预约会议 (cancel-schedule-conference)

35. 查询日程列表 (list-events)

36. 查询日程详情 (get-event)

37. 删除日程 (delete-event)

38. 添加日程参与者 (add-event-attendee)

39. 移除日程参与者 (remove-event-attendee)

40. 获取知识库列表 (list-workspaces)

41. 创建知识库文档 (create-doc)

42. 搜索知识库文档 (search-doc)

43. 覆写文档内容 (overwrite-doc)

错误处理

重要说明

When it activates

SkillSafety

Source

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