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ecm-dd-data-verify · thomasmoreai | DecimalAI

Legalby thomasmoreai

ecm-dd-data-verify

ECM 尽职调查工具类 Skill：调用第三方公开数据源（Tushare Pro / 企查查开放平台）与客户提供的材料做交叉比对，发现数据不一致、未披露变更、过期信息等问题。当用户提到以下场景时触发：外部数据验证、第三方数据核实、自动比对工商信息 / 股东 / 主要人员 / 知识产权 / 行政处罚、用 Tushare 查财务数据 / 用企查查查工商、客户提供的营业执照 / 股东名册与外部工商档案是否一致、 API 数据比对、public data verification、cross-check 客户材料、抓取外部公开数据、核实注册资本、核实法定代表人、核实实控人穿透、查专利 / 商标 / 软著是否真实存在、查行政处罚记录。典型输入：公司统一社会信用代码 / 名称 / ts_code、需比对的维度列表、客户提供的对应数据；典型输出：数据比对报告（Markdown 表格 + 差异分析 + 建议核实清单）。非触发边界：本 skill 是**工具类**不输出 DD Memo（五段式）；真正的合规判断由各业务 DD skill 去做。不负责文件读取（归 ecm-dd-file-

Never made a case worse

SkillScore

SkillScore74/100

Test results 74

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

22%74%of test cases passed +52 pts

Without skill5 of 23

With skill17 of 23

12 fixed5 passed either way6 still failing0 broke

Measured on gemini-3.6-flash8/6/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task对'华创微电子有限公司'注册地址做比对。客户登记地址为'北京市海淀区中关村南大街1号101室'，外部接口返回'北京市海淀区中关村南大街1号101'。请给出差异级别判断。

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 thomasmoreai-ecm-dd-data-verify
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("thomasmoreai-ecm-dd-data-verify")
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 thomasmoreai-ecm-dd-data-verify

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/thomasmoreai-ecm-dd-data-verify/SKILL.md https://app.decimal.ai/s/thomasmoreai-ecm-dd-data-verify/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md

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

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

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

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

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

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

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

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

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

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/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/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

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

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

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

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

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

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

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

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add thomasmoreai-ecm-dd-data-verify

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("thomasmoreai-ecm-dd-data-verify")
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/thomasmoreai-ecm-dd-data-verify/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

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

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add thomasmoreai-ecm-dd-data-verify

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/thomasmoreai-ecm-dd-data-verify/SKILL.md https://app.decimal.ai/s/thomasmoreai-ecm-dd-data-verify/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/thomasmoreai-ecm-dd-data-verify/SKILL.md

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

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/thomasmoreai-ecm-dd-data-verify/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/thomasmoreai-ecm-dd-data-verify.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/6/2026
Source: thomasmoreai/legal-skills-open ↗
License: Apache-2.0

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

ecm-dd-data-verify

定位与边界

本 skill 是 ECM DD 的工具类 skill，和 17 个业务性 DD skill（dd-approval / dd-entity / … / dd-compliance）有明显的职责分工：

业务性 DD skill 输出"合规判断"（核查要点 + 审阅发现 + 风险分级 + 建议措施，五段式）
本 skill 输出"数据差异清单"（客户数据 ↔ 外部公开数据的逐字段比对结果），供业务 DD skill 作为输入使用

⚠️ 本 skill 不套用 shared/templates/dd-skill-template.md 的五段式输出契约。 该模板只约束业务性 DD skill。本 skill 走自己的"数据比对报告"格式。

本 skill 负责

按需求维度调用 scripts/tushare_connector.py / scripts/qcc_connector.py 取外部数据
将外部数据与客户材料中的对应字段做逐项比对
为每条差异打"高 / 中 / 低"级别（仅指比对差异本身的严重程度，不等同于业务风险分级）
输出差异清单 + 可能原因 + 建议核实措施

本 skill 不负责

合规结论——差异是否构成发行上市障碍，交由调用它的业务 DD skill 判断
文件读取——客户文件的 PDF/Word/Excel 读取归 ecm-dd-file-review
文件归位——归 ecm-setup-file-organize
法规解释——归 ecm-research-reg-search / ecm-research-reg-study

免责声明

本 skill 产出的数据比对报告仅为尽调辅助工具底稿，不构成最终法律意见。Tushare 与企查查是第三方商业数据源，数据口径与更新频率由其运营方决定，存在滞后或偏差的可能。关键事项仍须以政府主管部门出具的官方文件为准。完整免责声明见 DISCLAIMER.md。

资深律师执行标准

执行本 skill 时，必须同时遵循 senior-lawyer-execution-standards.md。本 skill 的任何输出不得突破四条底线：事实可追溯、法源可核验、风险可分级、建议可落地；无法核验时必须显式标注。

本 skill 的实务加固点

数据不替代底稿：Tushare、企查查等第三方数据仅作交叉验证线索，不得覆盖客户原件和官方登记信息。
差异分级：主体名称、股本、股东、董监高、处罚、诉讼、经营范围等差异需按发行影响分级。
原始响应留存：每次调用必须记录来源、查询关键词、时间戳、接口返回和失败原因，归入数据比对底稿。
高风险触发器：第三方数据出现未披露处罚/诉讼/股权冻结/经营异常，应转对应 DD skill 复核。

前置依赖

外部依赖

Python 3.9+（脚本使用 dataclass / from __future__ import annotations）
pip install -r scripts/requirements.txt（requests / pandas 等）
至少一个以下环境变量组合（缺失时自动进入 fallback）：
- TUSHARE_TOKEN（Tushare Pro 账号 token）
- QCC_APPKEY + QCC_SECRET（企查查开放平台凭证）

内部依赖

ecm-setup-file-organize：已归位的客户文件，本 skill 据此定位"客户侧数据"
ecm-dd-file-review：如需从 PDF/Word/Excel 中抽取客户侧字段值，先调用它

可选数据源

目前已封装的数据源：

数据源	覆盖维度	典型用途
Tushare Pro	上市公司基础、财务指标、前十大股东、利润表 / 资产负债表	已上市标的反查、财务口径核验
企查查	工商基础、股东及出资、主要人员、工商变更、知识产权、行政处罚	全量非上市标的核验（发行人自身 / 股东 / 子公司 / 关联方）

核心工作流（五步）

第 1 步：明确比对维度

和律师确认本次需要比对的维度。ECM DD 场景常见的 6 大维度：

维度	对应字段	数据源优先级
工商基本信息	企业名称、统一社会信用代码、注册资本（实缴 / 认缴）、成立日期、注册地址、经营范围、法定代表人、登记机关、企业状态	企查查 > Tushare
股东信息	股东名称、出资额、持股比例、出资方式、出资日期	企查查（穿透）+ Tushare（上市公司前十大）
主要人员	法定代表人、董事、监事、高管	企查查 + Tushare `stock_company`
知识产权	专利号、商标注册号、软著登记号、权利人	企查查
行政处罚	处罚机关、处罚日期、处罚事由、处罚结果	企查查
财务数据	营业收入、净利润、资产总额、负债总额、股东权益、ROE	Tushare（仅上市公司）

不在列表里但用户要求的维度，应以"可扩展点"加入 references/dimensions-registry.md。

第 2 步：凭证预检

使用对应脚本的 --smoke 模式检查凭证：

python scripts/tushare_connector.py --smoke
python scripts/qcc_connector.py --smoke

两个都没凭证：直接跳到第 5 步的"fallback 路径"，不要伪造数据
只有一个有凭证：走可用那个的维度，另一个相关维度进 fallback

第 3 步：取客户侧数据

对于每个维度，先确定"客户说什么"：

从 02-尽职调查/02-02-主体资格/ 等目录读取相关文件（由 ecm-dd-file-review 完成）
从已完成的业务 DD skill 的 Memo 里摘取字段（如已运行 dd-entity，可从其 Memo 抽注册资本）
直接由律师口头告知（适用于快速核对场景）

把客户侧数据整理成结构化表格写入 references/client-data-staging.md（工作区临时文件），明确每个值的来源。

第 4 步：调用 API 取外部数据

按维度调用 connector。推荐脚本方式（可追溯 + 留日志）：

# 工商基础 + 股东 + 处罚
python scripts/qcc_connector.py \
    --basic "腾讯科技（深圳）有限公司" \
    > 05-底稿和附件/数据比对/qcc_basic_$(date +%Y%m%d_%H%M%S).json

# 已上市标的：基础 + 财务
python scripts/tushare_connector.py \
    --ts-code 600519.SH --fields full \
    --period 20241231 --financials

库方式（供编排脚本使用）：

from scripts.qcc_connector import QccClient, MissingCredentialError
c = QccClient()
try:
    basic = c.company_basic("腾讯科技（深圳）有限公司")
    keyno = basic["Result"][0]["KeyNo"]
    shareholders = c.shareholders(keyno)
    penalty = c.penalty(keyno)
except MissingCredentialError:
    # 跳到 fallback
    ...

所有原始响应留痕到 05-底稿和附件/数据比对/ 目录，保留到项目归档。

第 5 步：逐字段比对 + 输出报告

按下方"输出格式契约"生成 Markdown 报告。差异分级规则：

级别	含义	典型情形
高	核心字段存在实质差异，可能触发业务级高风险	注册资本客户说 5000 万、API 显示 3000 万；法定代表人客户写张三、API 显示李四；实控人穿透链断裂
中	次要字段差异或信息滞后	经营范围表述不同但实质相近；最近一次变更未同步到 API（<30 天）
低	格式 / 笔误 / 翻译差异	名称全角 / 半角括号差异；地址后缀"号""栋"差异；单位万元 / 元

差异 ≠ 业务风险。差异的业务意义由调用本 skill 的上层业务 DD skill 判断。

fallback 路径（凭证 / 数据完全不可用时）

无法取到外部数据时，绝不伪造。改为输出"人工核对清单"：

# 数据比对报告（fallback 模式）

> 本次比对因 [外部 API 凭证缺失 / API 返回错误 / 数据源暂时不可用]，
> 已自动降级为"人工核对清单"。以下字段需要项目组律师**手工登录相应平台**核对，
> 核对完成后把结果补充到本报告。

## 建议的人工核对渠道

- 企查查官网 https://www.qcc.com/
- 天眼查 https://www.tianyancha.com/
- 国家企业信用信息公示系统 https://www.gsxt.gov.cn/
- 裁判文书网 https://wenshu.court.gov.cn/
- 证监会行政处罚库 http://www.csrc.gov.cn/pub/zjhpublic/ (搜索"行政处罚决定书")

## 待人工核对字段清单

| 维度 | 字段 | 客户侧数据 | 官方口径（待核对） | 核对结果 |
|------|------|-----------|-------------------|---------|
| 工商 | 注册资本 | 5000 万元 | （待核对） | |
| 工商 | 法定代表人 | 张三 | （待核对） | |
| ... | ... | ... | ... | |

输出格式契约

# 数据比对报告：{客户简称}

> 项目：{客户简称} | 编制日期：YYYY-MM-DD | 编制人：{律师姓名}
> 数据源：{Tushare Pro / 企查查 / 二者}
> 凭证状态：{均可用 / 仅 Tushare / 仅企查查 / 均不可用（fallback 模式）}

## 一、比对维度与数据源

| 维度 | 数据源 | 抓取时间 | 原始响应存档 |
|------|-------|---------|-------------|
| 工商基本信息 | 企查查 | 2026-04-24 10:30 | 05-底稿和附件/数据比对/qcc_basic_20260424_103000.json |
| 股东信息 | 企查查 | 2026-04-24 10:31 | ... |
| 财务数据 | Tushare Pro | 2026-04-24 10:32 | ... |

## 二、比对结果汇总

| 序号 | 维度 | 字段 | 客户侧数据 | 外部数据 | 差异 | 级别 | 备注 |
|-----:|------|------|-----------|---------|------|------|------|
| 1 | 工商 | 注册资本 | 5000 万元 | 5000 万元 | 无 | - | 一致 |
| 2 | 工商 | 法定代表人 | 张三 | 李四 | 人名不同 | 高 | 建议核实最近变更 |
| 3 | 股东 | 股东 A 持股比例 | 30% | 28% | -2 个百分点 | 高 | 疑有未披露股权转让 |

## 三、差异分析

### 差异 2：法定代表人不一致
- 客户侧来源：02-02-主体资格/营业执照-20250312.pdf
- API 侧数据：企查查 2026-04-23 抓取
- 可能原因：
  1. 客户营业执照为旧版，未及时更换
  2. 外部数据源未同步最近变更
  3. 存在未披露的治理层变更
- 建议核实：向客户索取最新营业执照 + 变更登记通知书；同时查工商局在线档案

### 差异 3：股东持股比例不一致
...

## 四、建议核实事项

- **高风险项（立即核实）**：
  1. 法定代表人差异——要求客户提供最近一次变更登记完整档案
  2. 股东 A 持股比例差异——要求客户提供近两年历次股权变更协议
- **中风险项（次轮核实）**：
  - ...
- **低风险项（可一并处理）**：
  - ...

## 五、数据源局限与注意事项

- 企查查数据有 T+{N} 天滞后，本次抓取时间：2026-04-24
- Tushare 财务数据以公司披露的定期报告为准，定期报告披露前可能暂无最新季度数据
- 行政处罚维度仅覆盖公开发布的处罚决定书，部门内部处理、未公开的警示谈话等不在覆盖范围
- 关键事项仍以政府主管部门出具的官方文件为准

## 六、附件

- 05-底稿和附件/数据比对/qcc_basic_YYYYMMDD_HHMMSS.json
- 05-底稿和附件/数据比对/qcc_shareholders_YYYYMMDD_HHMMSS.json
- 05-底稿和附件/数据比对/tushare_fina_YYYYMMDD_HHMMSS.csv
- （下级业务 DD skill 可直接引用上述原始响应）

契约硬性要求：

一级标题必须是"数据比对报告：{客户简称}"——供上层 Skill 识别
必有"比对维度与数据源 / 比对结果汇总 / 差异分析 / 建议核实事项 / 数据源局限与注意事项" 五个二级标题
汇总表的"级别"列取值必须是"高 / 中 / 低 / -"（"-" 表示一致）
输出文件放到 05-底稿和附件/数据比对/数据比对报告-{YYYYMMDD}.md （如该目录尚未建立，先按 shared/templates/project-folder-structure.md 创建）
所有 API 原始响应必须留痕存档到 05-底稿和附件/数据比对/，不得仅贴到报告里

如何被其他 DD skill 调用

业务 DD skill 可以在自己的"核心工作流"里插入本 skill 作为可选前置步骤。约定：

调用方式：在业务 DD skill 的 SKILL.md 里写"可选：调用 ecm-dd-data-verify 对 {具体字段} 做外部数据核验"
传递的输入：公司名称 / 统一社会信用代码 / ts_code、需比对的字段清单、客户侧字段值
接收的输出：本 skill 生成的 Markdown 报告 + 原始响应 JSON
后续处理：业务 DD skill 将"高"级差异写进自己的"审阅发现"段，"中 / 低"级差异作为补充材料注明

典型链路：

ecm-dd-entity（主体资格）
    └─ 可选调用 ecm-dd-data-verify（工商基本信息维度）
            └─ 调用 scripts/qcc_connector.py --basic

常见误用 / FAQ

Q：可以让本 skill 直接生成 DD Memo 吗？ A：不可以。本 skill 是工具，产出的是"数据差异"。业务合规判断必须由对应业务 DD skill 完成。
Q：API 数据和客户数据都对不上怎么办？ A：记为"高"级差异，写进报告的"差异分析"，并建议人工登录政府主管部门官网核对 —— 以官方档案为最终仲裁。
Q：可以把 TUSHARE_TOKEN 写到仓库的某个文件里吗？ A：不可以。任何形式落盘都违反本 skill 的安全约束。见 docs/data-connectors.md。
Q：我只想手动对一下，不想跑脚本？ A：可以。走 fallback 模式：不调用 API，仅输出"人工核对清单"+"建议核对渠道"。

参考资料索引

references/dimensions-registry.md — 6 大比对维度 + 字段级映射
references/report-template.md — 数据比对报告完整模板（含 fallback 版）
../../docs/data-connectors.md — 连接器安装、环境变量、异常分层、故障排查
../../scripts/tushare_connector.py — Tushare 封装
../../scripts/qcc_connector.py — 企查查封装

变更规则

输出契约变动（报告结构）→ MAJOR，同步更新所有引用本 skill 的业务 DD skill
新增数据源 / 新增比对维度 → MINOR，更新 references/dimensions-registry.md 和 docs/data-connectors.md
connector 脚本 API 兼容性不变的修复 → PATCH

About this skill

Automatic

Legal & Contracts pack

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✓ 3 of 3 passed

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0 critical · 0 warning · 0 info — scanner v5 — 10/7/2026

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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

thomasmoreai / legal-skills-open

cn/corporate/skills/ecm-dd-data-verify/SKILL.md

View on GitHub ↗

License MITImported 10/8/2026Version v1Bundled 2 files

Illustrative Want to see what thomasmoreai/ecm-dd-data-verify 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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前置依赖

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可选数据源

核心工作流（五步）

第 1 步：明确比对维度

第 2 步：凭证预检

第 3 步：取客户侧数据

第 4 步：调用 API 取外部数据

第 5 步：逐字段比对 + 输出报告

fallback 路径（凭证 / 数据完全不可用时）

输出格式契约

如何被其他 DD skill 调用

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