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数据整理与图表（gongwen-data） · duyunzhoudyz | DecimalAI

Databy duyunzhoudyz

数据整理与图表（gongwen-data）

为公文整理数据、出图表，包括报表体检（空值、合并单元格、合计校验、单位和时段口径冲突），汇总下级报表，计算同比、环比、增速、百分点、占比、拉动、完成率、平均值并附算式，把数据写成能直接进公文的句子，在本机生成黑白灰 Excel 图表，数据不出本机，缺的数标【待补】。用户说合并科室月报、汇总下面报的表、查查合计对不对、算同比环比、百分点怎么写、这组数写成一段话、做个柱状图折线图时使用。整篇总结和分析报告交 gongwen-report，装有行业包时，本行业专用文书交对应行业包；成稿核数字交 gongwen-review，整改台账转 Excel 交 gongwen-rectify。

Maintained

SkillScore

SkillScore90/100

Test results 90

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

38%90%of test cases passed +52 pts

Without skill8 of 21

With skill19 of 21

11 fixed8 passed either way2 still failing0 broke1 errored

Measured on gemini-3.8-flash11/3/2026v122 attempted

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task这是一篇已经成稿并排版完毕的《全区上半年经济运行调研报告》Word全文（共12页，包含政治定调、产业分析和政策建议各章节）。请通读全文，对其中的文字表述、语病逻辑、标点符号规范以及政治提法进行全篇审校把关。

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 duyunzhoudyz-gongwen-data
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("duyunzhoudyz-gongwen-data")
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 duyunzhoudyz-gongwen-data

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/duyunzhoudyz-gongwen-data/SKILL.md https://app.decimal.ai/s/duyunzhoudyz-gongwen-data/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/duyunzhoudyz-gongwen-data/SKILL.md

This gets SKILL.md only. The skill's 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/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/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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("duyunzhoudyz-gongwen-data")
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/duyunzhoudyz-gongwen-data/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/duyunzhoudyz-gongwen-data/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 12 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 duyunzhoudyz-gongwen-data

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/duyunzhoudyz-gongwen-data/SKILL.md https://app.decimal.ai/s/duyunzhoudyz-gongwen-data/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/duyunzhoudyz-gongwen-data/SKILL.md

This gets SKILL.md only. The skill's 12 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/duyunzhoudyz-gongwen-data/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/duyunzhoudyz-gongwen-data.json

This gets SKILL.md only. The skill's 12 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/3/2026
Source: duyunzhoudyz/gongwen-skills ↗
License: NOASSERTION

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

数据整理与图表（gongwen-data）

本 skill 为写材料理数据、出图。每个结果附算式，正文每个数都能在计算底稿里找到原样写法；缺的数标【待补】，口径对不上的不悄悄合并。

适用范围

适用：
1. 报表体检：空值占位符、文本型数字、合并单元格、合计小计校验、单位时段口径不一致、重复行、个人信息。
2. 多表合并：汇总科室、下级单位、子公司报来的同类报表。
3. 指标计算：增减量、同比、环比、增速、百分点、占比、拉动、贡献率、完成率和时间进度、倍数、年均增速、平均值。
4. 数据转文字：写成能直接放进总结、汇报、分析报告的句段。
5. 公文图表：柱状、条形、折线、饼图、堆积图，黑白灰。
不适用（转交）：

用户要的	转给
写整篇总结、汇报材料、调研报告；汇总文字材料	gongwen-report
行业专用文书全文，如医疗质量分析报告、经营分析报告	装有行业包时交对应行业包（gongwen-hospital、gongwen-soe 等）；未安装的交 gongwen-report
政务信息、简报全文	gongwen-brief
已成稿件里的数字、百分点、前后一致核查	gongwen-review
整改台账、任务分解表、督办清单转 Excel	gongwen-rectify、gongwen-implement、gongwen-minutes
把表格和图排进国标 Word	gongwen-format

用户要整篇稿子的，先交数据段落、计算底稿和图表，再转交。统计表要写进调研报告等整篇材料的，除逐行数据外，给出汇总数、平均值等派生数，分母口径写清（如“有数据的6个村平均2.4万元”）。能调用其他 skill 的环境直接调用；未安装或在网页环境的，按 references/_shared/style/output-format.md 第四部分转交：给交接卡、“新开对话粘贴 dist/prompts/<skill 名>.md 继续”的说明和安装命令。其余回复不写 skill 名，用描述性说法。

开工检查

保密（references/_shared/guardrails.md 第一条）：
- 有 Python 时，先运行 python scripts/secret_check.py <文件> 查保密标识（含页眉页脚），命中即停。
- 表格有敏感信号时才在起草说明末提示一句，同一会话只说一次。
- 文件、工作表、表头前、表注或单元格带密级标识或“内部资料”“工作秘密”“不得外传”等内部标注的，立即停止，不读数、不复述，告知涉密信息不得输入联网 AI 工具，列出可继续的三种情形，等用户说明。表中只是出现“保密工作经费”之类项目名称的照常处理。
- 明细中有姓名、身份证号、手机号、病案号、住院号、学号、银行卡号、家庭住址的，提醒脱敏；只用汇总数，输出写“张某”“138****0000”（脚本输出已脱敏），“保密提示”写明。未公开的经营数据、统计数据、考核排名，提醒不进对外材料。
数据不出本机：只用 scripts/ 下脚本和本机 Excel、WPS，不用在线图表网站、在线表格转换工具和远程渲染的图表插件。
单位画像：先读 .gongwen/单位画像.md，没有再找 ~/.gongwen/单位画像.md，用户粘贴的按粘贴内容；再读 references/units.md 对应小节。角色模式按 references/_shared/profiles/roles.md 第一节确定，差异见“角色适配”；无画像时按该文件第九节降级，单位类型能从表格判断的不问。
交接卡：用户贴了交接卡的（网页和 Agent 环境一样），按卡上的画像、模式、任务、本站和已确认事项开工，已有的不再问（references/_shared/style/output-format.md 第四部分）。
定任务：完整链条是“体检→合并→计算→转文字→出图”，用户只要一段的只做那段；凡引用表中的数，先体检，至少核合计和单位。
首轮交付（references/_shared/profiles/roles.md 第十节）：有数据就一次交付体检结果、底稿、正文和图表，口径冲突处正文用最可靠的写法（如明细相加数）并注明口径，不等确认；用户说“直接做”“别问了”或高效模式的，不提问，疑点只进待补、待核实；其余模式提问不超过 3 个，放回复末尾。
用户要“把数调得好看点”、补表里没有的数、改原始数据凑考核目标的，说明不能这样做（references/_shared/guardrails.md 第二条、第六条），其余照常。

工作流程

第一步　登记数据

给每张表编号（表A、表B……），登记文件或工作表、报送单位、统计时段、计量单位、口径说明、数据来源系统，作为计算底稿的材料清单。
提问只问业务口径，取舍顺序：统计时段 → 对比基期（同比还是环比、和哪年比）→ 计量单位或统计范围。表头、表注、文件名能看出的不问，“直接做”的不问。没问清的先取默认值做完并写进“假设”。
用户没给任何数（如“帮我写一段今年的数据”）的不编数，交数据收集清单（指标、时段、基期、取数处）和带【待补】的句式，如“1—9月，全院出院【待补：人次】人次，同比增长【待补：增速】%”。

第二步　报表体检

有 Python 时运行 python scripts/data_tool.py inspect <表格> --json（多个工作表加 --all-sheets），逐条复核，表头前说明（pre_texts）和表注（notes）登记为口径说明；不能运行时按 references/inspect-checklist.md 第二部分人工检查。重点：

查什么	发现后怎么办
带密级、内部标注（DT-SEC-01，P0，脚本已停止）	回到开工检查第1条；经保密管理部门确认或已解密、已删除后才可加 `--marking-ok` 重跑，不自行添加
合计、小计与明细相加不符（P0），或超出修约容差（P1）	不直接引用表中合计；正文用明细相加数，“待核实”一行写明表中合计
同列单位不一致（P0）	换算统一后再算，换算写进底稿
空值、“—”“/”等占位符	空不等于 0。表注或用户说明了含义的（如“不开展此项”）照说明处理并写进起草说明；没说明的，正文涉及的写【待补：说明】
约数（“约20”“30多”）	不做精确计算，正文照写约数，起草说明请用户核实准确数
比率、平均数列的合计	分子合计除以分母合计重算，不简单平均

表中自带的合计、小计行就是校验和，逐项相加去对，不凭目测。例：18 个街道（乡镇）相加为 4356，表中合计 4382，差 26，列为 P0；正文写“新登记经营主体4356户”，待核实写明表中合计4382户。

体检结果写成体检报告：一行结论加问题清单，格式见 references/inspect-checklist.md 第四部分。

第三步　合并汇总（多张表时）

运行 python scripts/data_tool.py merge <表A> <表B> ... --expect <应报单位名单> --out <汇总表.xlsx> --json；不能运行时按 references/inspect-checklist.md 第三部分人工合并。规则：

同量纲单位按多数表换算（元换万元÷10000），每次记一条；人与人次、户与人、件与次不换算、不相加。
时段与多数表不同的表（如1—5月混进1—6月）不计入合计，单列出来请对方重报。
比率和平均数不相加；缺分子分母的写【待补：分子和分母数据】。
同一单位报了两次的，确认前不重复相加：正文用只计一次（或不含该单位）的合计并注明口径，待核实一行请确认。应报未报的列入待补，正文写全口径合计时注明“不含……”。
比不齐的不加总、不取平均、不挑大的，全部进“附：口径冲突表”请用户确认。

第四步　指标计算

按要说明的事选指标，公式和 Excel 写法见 references/calc-sheet.md 第一部分：

要说明的	用
比去年同期多了还是少了	增减量＋同比增速
比上个月	环比（只用于相邻两期）
率、占比、增速本身的变化	百分点
某部分在总体中的分量；对总体增长的作用	占比；拉动（百分点）、贡献率
任务进度	完成率＋时间进度
多年变化	倍数、年均增速（水平法）
全院、全系统的率或平均数	分子合计÷分母合计
台账、明细按类别统计件数	分类计数和占比

同期对比的数量类指标（含时长、天数、平均数）增减量与增速并写；一句超 3 组数据的拆句，不删增减量。

有 Python 时用 python scripts/data_tool.py phrase --type <类型> ... --md，按类别计数用 count 子命令，详见 references/scripts.md。脚本提示逐条处理：句子里的【待核实】进正文前删去，写进“待核实”一行；合计不符的，脚本已改用明细相加数，不要改回表中合计；两期口径不同的，改用同口径结果并注明“不含××”。
不能运行脚本时按 references/calc-sheet.md 第三部分人工演算并反算验证；基期为 0、亏损、两期口径不同等特殊情形按其第二部分。
所有结果记入计算底稿（references/calc-sheet.md 第四部分）；算不了的记入“未计算的项目”并写明原因。

第五步　数据转文字

按 references/_shared/rules/data-expression.md 写。示例的数字和单位均为虚构，不得带入正文。最常错的几条：

两个百分数相减用“个百分点”；数量变化用百分数。
- 误：占比由28.6%升至31.0%，提高2.4%。正：提高2.4个百分点。
先写本期数，再写变化并写基期：“新登记经营主体4356户，同比增长8.3%”。“同比”不与“比上年同期”叠用。
下降写“下降X%”，不写“负增长”；倍数不用于减少。
原因只写用户给的，没有就不写“主要得益于……”。
程度靠数字说明，少用“大幅”“显著”；一句不超过 3 组数据；一段只写一类指标，先总后分；正文不写算式和“据测算”之类过程话。
数据段落开门见山，不为凑篇幅罗列数据。用户指定字数的，按 references/_shared/guardrails.md 第四条第2款：数据撑不起的，不新增数、不凑数，起草说明一句话说明；缺数标【待补】，不计入字数。

第六步　公文图表

有 Python 时用 python scripts/chart_tool.py <表格> --type <类型> --y <列名> --title <图题> --source <数据来源> --out <文件> 生成 Excel 原生图表（参数见 references/scripts.md 第五部分）；同期对比两列都写（如 --y 2026年,2025年），脚本自拼的图题要核对。选图见 references/chart-guide.md：

比大小用柱状图、条形图（类别多时用条形图并排序）；看变化用折线图；看构成用饼图（不超过 6 类）。
黑白灰，纵轴从 0 开始，不用双坐标轴，不同单位分开出图（references/chart-guide.md 第三部分）。
图题写时段和指标，纵轴写“单位：……”，图下写“数据来源：……”（没有的写【待补：数据来源】）；合计、小计行不入图，空值不画成 0。

不能运行脚本时，起草说明后给“附：图表规格卡”和 Excel、WPS 操作步骤（references/chart-guide.md 第四部分）。

第七步　自检

底稿对账（必做）：正文每个数都要在计算底稿“文字表述”列找到原样写法。有 Python 时运行 python scripts/fact_diff.py 计算底稿.md 正文.txt --ledger，“正文中有、台账中没有”的逐条补进底稿或改为【待补】。
机检：运行 python scripts/gongwen_lint.py 正文.txt --doc-type <文书类型> --json，文书类型取数据段落要放进的材料（总结、汇报材料、信息等），不清的用“其他”。P0 必改，P1 逐条判断；【待补】报的 P2 保留。
不能运行脚本时按“自检清单”人工检查，并在“假设”写“未运行脚本，计算和核对为人工完成，请复核”。

第八步　交付

按 references/_shared/style/output-format.md 和本文“输出规范”交付。

能写文件的：建 公文/<YYYYMMDD>-<简短标题>/，按需写入 正文.txt（数据段落，只含正文）、计算底稿.md、体检报告.md、汇总表和图表。对话中报文件位置、体检结论一句、主要数据一两句，再给【起草说明】，不复述全文；交接卡写入文件，对话里只报位置。
网页环境：依次输出正文（数据段落）、【起草说明】，正文前不加引导语。起草说明按 references/_shared/style/output-format.md 第二部分，不超过 5 行、每行一句，另有计算说明一行（用了哪些公式，是否运行脚本，表述判断依据）；教学模式的写作要点另列“附：写作要点”。之后按需附“附：体检问题清单”“附：口径冲突表”“附：计算底稿”“附：图表规格卡”，不计入 5 行；有计算的，各模式都附计算底稿。
用户只要体检或合并的，交体检报告（和汇总表），不硬写正文。
还有下一站的（如交 gongwen-report 成稿），在起草说明之后输出更新后的交接卡，本站产出写“数据段落、计算底稿”（注明已算出的汇总数、平均值及分母口径），不抄原始数据和个人信息；告知用户新开对话，先粘贴下一站的提示词文件，再粘贴交接卡和本站产出。

单位差异

单位类型	口径要点	特别注意
党政机关	对外引用的统计数以统计部门公布的为准；快报数、年报数、预计数分开	未公开统计数、考核排名不进对外材料
基层、社区	人与人次、户与人分清；多部门台账同一件事只计一次	不虚报，不补记台账；居民信息脱敏
公立医院	国家监测指标不自行改变计算方法；全院比率用分子分母合计	患者姓名、病案号、住院号脱敏
高校	学年与自然年分清；与高基报表、状态数据库口径一致	学生个人信息不进明细
中小学	学期与学年分清；达标率、优秀率、平均分用人数重算	义务教育阶段不对学生、班级排名，成绩图不排序
国有企业	金额单位全文统一；与财务快报一致；完成率配时间进度；合并口径与单体口径分开	未披露经营数据属商业秘密

输出规范

正文（数据段落）纯文本，不用 Markdown；数字写法按 references/_shared/rules/gbt15835.md。正文只允许【待补：说明】一种提示，在起草说明汇总，不写【待核实】。
体检报告、口径冲突表、计算底稿、图表规格卡是工作底稿，可用 Markdown 表格。

自检清单

角色适配

教学模式：有报表的，体检结果、计算底稿和正文一次交付，口径冲突处正文注明口径；只问一两个数怎么写的，直接给句子和讲解。“附：写作要点”按本次数据涉及的用法各举一处正误对照（百分点、同比环比、倍数、翻番、年均增速等），涉及年均增速的，说明按水平法计算、不是各年增速的简单平均，并说明每个【待补】一般到哪取数（统计报表、业务系统、财务快报）。
高效模式：不提问，一次交付；需要时数据段落给详、简两版。起草说明只写假设、待补、待核实和计算说明，不展开讲解，但每条体检问题和表述判断仍写明问题类型和依据（列入附表）；底稿可只列算式和结果两栏。
转译模式：接受原始表格和口头描述；只问统计时段、对比基期、计量单位；体检问题用大白话解释（如“有数字被存成了文字，Excel 加总会漏掉”）；底稿完整列出算式。
审稿模式：只出体检报告或数据表述问题清单，每条写位置、问题、依据、建议，不重写全文，是否采用由用户定；整篇稿件核稿转 gongwen-review。
严谨模式：用于对外发布、报上级党组织的数据。底稿为每个数注明出处（报表名称、时段、口径）；未确认已公开的数据，“待核实”一行请用户确认是否已对外公布，并附数据出处核对清单。

参考资料

没有文件系统（网页版、内网提示词版）时，读不到的文件按本文和附录执行，不向用户索要。各步骤已写明何时读哪个文件，另外：

文件	何时读取
`references/scripts.md`	运行脚本前查命令和参数
`references/_shared/profiles/units/`	按单位类型补充读取
`references/_shared/style/ai-tone.md`、`references/_shared/style/defensive.md`	写数据段落时避开 AI 腔和对冲语气

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skills/gongwen-data/SKILL.md

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License PolyForm-Noncommercial-1.0.0Imported 11/2/2026Version v1Bundled 12 files · scripts

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数据整理与图表（gongwen-data）

适用范围

开工检查

工作流程

第一步 登记数据

第二步 报表体检

第三步 合并汇总（多张表时）

第四步 指标计算

第五步 数据转文字

第六步 公文图表

第七步 自检

第八步 交付

单位差异

输出规范

自检清单

角色适配

参考资料

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第一步　登记数据

第二步　报表体检

第三步　合并汇总（多张表时）

第四步　指标计算

第五步　数据转文字

第六步　公文图表

第七步　自检

第八步　交付