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Office Transform · cherryhq | DecimalAI

Documentationby cherryhq

Office Transform

Extract or edit a selection from xlsx, docx, pdf, or pptx into a new file; the source is never modified.

Maintained

SkillScore

SkillScore76/100

Test results 76

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

33%76%of test cases passed +43 pts

Without skill7 of 21

With skill16 of 21

10 fixed6 passed either way4 still failing1 broke1 errored

Measured on gemini-3.6-flash11/5/2026v122 attempted

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI selected the table range A1:C10 on 'Sheet1' in my spreadsheet `/abs/sales_data.xlsx`. Could you extract that specific region and export it into a new CSV file located at `/abs/sales_q1.csv`?

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 cherryhq-office-transform
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("cherryhq-office-transform")
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 cherryhq-office-transform

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/cherryhq-office-transform/SKILL.md https://app.decimal.ai/s/cherryhq-office-transform/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/cherryhq-office-transform/SKILL.md

This gets SKILL.md only. The skill's 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/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/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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("cherryhq-office-transform")
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/cherryhq-office-transform/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/cherryhq-office-transform/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 4 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 cherryhq-office-transform

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/cherryhq-office-transform/SKILL.md https://app.decimal.ai/s/cherryhq-office-transform/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/cherryhq-office-transform/SKILL.md

This gets SKILL.md only. The skill's 4 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/cherryhq-office-transform/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/cherryhq-office-transform.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/5/2026
Source: cherryhq/cherry-studio ↗
License: AGPL-3.0

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

Office Transform

Turn a structural selection of an Office/PDF document into a new derived file.

Two invariants hold for every operation:

The source file is read-only. Every result is a new file; both scripts refuse to write to the source path or overwrite an existing file. Never work around this with ad-hoc shell edits to the original.
Anchors are structural. Selections address the document's own coordinates — worksheet + A1 range, body-level paragraph ordinal, one-based page number — never screen or DOM positions.

When to use which tool

Only need to read a document to summarize or answer questions → use mcp__cherry-tools__to_markdown instead (see the cherry-tool-guide skill); it is lossy but built for reading.
The user wants a file out — an extracted range, a converted fragment, an edited copy → this skill.

Input: selection references

Chat surfaces may hand you a fenced selection-ref block:

{"path": "/abs/report.xlsx", "anchor": {"format": "xlsx", "sheet": "Sheet1", "range": "A1:C10"}, "excerpt": "…", "fileStamp": {"size": 1024, "mtimeMs": 1700000000000}}

The anchor object is exactly what the scripts take via --anchor.

A block may also carry comment: what the user wants done at that location, in their own words. Treat it as the user's instruction for that anchor. It is not part of the anchor, so never pass it to a script.

Freshness check. fileStamp.mtimeMs is milliseconds since the Unix epoch, floored to whole milliseconds. Do not compare it to stat's default output: stat -f %m (macOS) and stat -c %Y (GNU) give whole seconds, so multiplying by 1000 loses the sub-second part and never matches. Read whole milliseconds the way the app wrote them:

uv run python -c "import os,sys;st=os.stat(sys.argv[1]);print(st.st_size, st.st_mtime_ns//1_000_000)" '/abs/report.xlsx'

Treat the file as changed when the size differs, or when the mtimes differ by more than 2 ms. The tolerance is small on purpose: both sides floor the same nanosecond timestamp to milliseconds, so they agree exactly, and the couple of milliseconds only covers the rounding of a double that can no longer represent current epoch milliseconds exactly. A wider window would hide real edits — a same-size change made within it would read as unchanged, which is why the anchor check below is not optional. On a change, tell the user the file changed since they selected and ask them to re-select — never silently re-anchor.

Anchor check. Before a patch-copy edit, also verify the anchor still points where the user thinks: extract the anchored region and compare its text with the reference's excerpt, normalizing both sides the same way (NFC, collapse each whitespace run to one space, trim). The two are not equal and are not meant to be — the excerpt is what the user selected, while the extract is the whole region an edit would replace. A selection sitting inside one region makes the excerpt the shorter side; a selection running past that region anchors to where it starts, which makes it the longer one. So the test is containment, whichever way round fits: the excerpt appears inside the extract, or the extract's tail is where the excerpt begins. Only when neither holds has the anchor stopped pointing at the selection — then stop and tell the user, never edit at a mismatched anchor and never go searching for a "close enough" location. Two cases need care because the two sides are not directly comparable as-is:

xlsx: the excerpt is tab-separated cells and newline-separated rows, while extraction writes csv or md. Compare cell values, not the raw file — read the csv with a csv reader and join the fields with single spaces before normalizing, so , and | never count as a difference. That join drops the cell boundaries on both sides, so a match proves the text is unchanged, not the grid — the same words re-split across cells still reads as a match. The freshness check above is what catches that; never skip it on a passing anchor check. Number formats are the second asymmetry: the excerpt holds what Excel displays, because that is what the user pointed at (1,234.50, 45.67%), while extraction writes the stored value (1234.5, 0.4567). Neither side is wrong, so a formatted cell never matches literally. Compare those cells by value, and leave them out of the joined strings so the containment test runs on the text cells alone. Dates, times and durations are the same asymmetry seen from the other side: extraction writes one fixed shape (2024-01-03, 2024-01-03 15:04:05, 26:30:00) rather than the cell's format, so treat a cell that differs only in date or time presentation as a format difference too. A difference that is only the number format is not a moved anchor, and stopping on one sends the user back to re-select a selection that never moved.
docx with charRange: the slice is only part of what patch-copy compares and replaces — it rewrites the whole paragraph. Extract the paragraph without charRange as well, and read "Edit docx" below before writing.

Users may also describe the region in words ("sheet 2, columns A through C"); build the anchor JSON yourself, confirming the worksheet name or paragraph if ambiguous.

Anchor shapes:

Format	Anchor
xlsx	`{"format":"xlsx","sheet":"Sheet1","range":"A1:C10"}` (range may be one cell)
docx	`{"format":"docx","paragraph":3,"paraId":"502E8D33","charRange":[0,12]}` (`paraId` optional = the paragraph's `w14:paraId`, resolved first when present; `charRange` optional; ordinal counts body-level paragraphs only, tables excluded)
pdf	`{"format":"pdf","page":3,"charRange":[0,120]}` (`charRange` optional, applies to extracted text)
pptx	`{"format":"pptx","slide":2,"nodeId":"4","paragraph":0}` or `{"format":"pptx","slide":2,"nodeId":"7","tableCell":{"row":1,"col":0}}` (`slide` is one-based; `nodeId` is the OOXML shape id — omit for the whole slide; `paragraph` and `tableCell` are optional, mutually exclusive, and only valid together with `nodeId`)

Operations

Scripts live in this skill's scripts/ directory; resolve paths relative to this skill folder. The library-edit recipes routed to below live in references/ beside them. Python dependencies are per-format and provided at invocation time via uv run --with <pkg> (the bundled-shell idiom — do not pip install globally).

Which route an edit takes follows from the library that can write the format: openpyxl drops charts and drawings on a round-trip, which is why xlsx edits go through patch-copy, while python-pptx keeps XML it does not understand, which is why pptx edits go through the library.

Extract — pull the anchored region into a new file

Always single-quote paths — real documents have spaces in their names (Q1 report.xlsx). If a path itself contains a single quote, close and reopen the quoting around it: '/abs/Bob'\''s deck.pptx'.

uv run --with openpyxl python scripts/office_extract.py \
  --file '/abs/report.xlsx' \
  --anchor '{"format":"xlsx","sheet":"Sheet1","range":"A1:C10"}' \
  --out '/abs/report-q1-range.csv'

The output format is inferred from --out's extension:

Source	Dependency (`--with`)	Output formats
xlsx	`openpyxl`	`xlsx`, `csv`, `md`
docx	`'python-docx>=1.1,<2'`	`docx`, `txt`, `md`
pdf	`pypdf`	`pdf` (page copy), `txt`, `md`
pptx	`python-pptx`	`txt`, `md` (slide, shape, paragraph, or table-cell text)

The docx pin is not optional. Patch-copy's expectText gate compares a paragraph read with python-docx against the same paragraph read by the script's own paragraph_text, which reproduces python-docx's Paragraph.text element for element. That equivalence was checked against 1.x; a release that changes what .text spells would make the gate refuse paragraphs nobody edited. Quote the specifier — > and < are redirects to a shell.

xlsx extraction reads computed values (data_only), so formula cells yield their last saved result. docx extraction to docx carries text only, not run styling.

Patch-copy — derive an edited copy, standard library only

uv run python scripts/office_patch_copy.py \
  --file '/abs/report.xlsx' \
  --edits '{"format":"xlsx","sheet":"Sheet1","cells":{"B2":42,"C3":"hello"}}' \
  --out '/abs/report-updated.xlsx'

OOXML packages are ZIPs of XML parts. Patch-copy copies every part byte-for-byte and re-serializes only what an edit reaches — the target worksheet or word/document.xml, plus the workbook bookkeeping noted below for xlsx — so styles, charts, images, and macros in untouched parts survive exactly. Edit shapes:

{"format":"xlsx","sheet":"S","cells":{"B2":42,"C3":"text","D4":true}} — numbers, strings, and booleans; an existing formula in an edited cell is replaced by the value. Patch-copy does not recalculate, so a formula reading an edited cell keeps the value it last cached; every write sets fullCalcOnLoad in xl/workbook.xml so Excel recomputes those on open. Replacing a formula also drops xl/calcChain.xml (a recalculation cache Excel rebuilds), along with the content-type and relationship entries that would otherwise point at a part no longer there; keeping a chain entry for a cell that no longer has a formula makes Excel report the derived file as damaged. Cells in a shared, array, or data-table formula group are refused — the expression lives in one member and the others only reference it, so overwriting a member would strip the formula from cells you never named. Rewrite such a range with openpyxl. Coordinates outside the worksheet grid (past XFD or row 1048576) are refused too.
{"format":"docx","replacements":[{"paragraph":3,"text":"new text","paraId":"502E8D33","expectText":"old text"}]} — the paragraph keeps its paragraph style and the first run's character style; extra run-level styling within that one paragraph is flattened into the new text. text must be the complete new paragraph. The whole body paragraph is replaced, and charRange does not narrow that — feeding back a charRange slice as text silently discards the rest of the sentence. The output is exactly w:p > [w:pPr] + w:r > [w:rPr] + w:t, so a paragraph holding anything that shape cannot carry is refused, not silently stripped. That covers bookmarks, comment anchors and fields (their start/end can pair across paragraphs, and rewriting one half unbalances the document), images, embedded objects, footnote/endnote references, hyperlinks, tracked changes and moves, content controls, equations — and anything else not on the short allow-list, including elements from namespaces that did not exist when this was written. A dropped w:del would even accept a pending deletion on the user's behalf. A page or column break (<w:br w:type="page"/>) is refused for a quieter reason: it carries no characters, so the extract reads the text on either side of it as one string and the anchor check cannot see that the rewrite would delete it. A bare <w:br/> line break still passes. To edit such a paragraph, see "Edit docx" below — do not reach for Paragraph.text, which destroys exactly the same content, only silently. paraId (optional) is resolved before the ordinal; a disagreement between the two is an error, never a silent pick. expectText (optional but strongly recommended) is a hard gate: the target paragraph's current text must match it after whitespace normalization or the edit is refused. Take its value from an extract of the same paragraph taken without charRange — the gate compares the whole paragraph, and a selection-ref excerpt is truncated at 2000 chars and may span more than the edit target.
Any text written into a cell or paragraph must be storable in XML: control characters other than tab, newline and carriage return are refused. Text extracted from a deck can carry them (python-pptx maps a soft line break to \x0B), so strip them before feeding extracted text back in as an edit value. A docx paragraph refuses tab, newline and carriage return as well: WordprocessingML spells those <w:tab/> and <w:br/>, and the single w:t this rewrite emits cannot carry them. Extracting a paragraph that holds them gives you \t / \n, so editing that string and writing it back would delete the elements while reading identically — split the content across separate body paragraphs, or see "Edit docx" below.

Text comparisons on both sides of this skill use one normalization rule, identical to the renderer's normalizeSelectionText: NFC-normalize, collapse every whitespace run to a single space, trim the ends.

Generate — write ad-hoc library code for new documents

Generation (a fresh deck, workbook, or document — from scratch or from data you just extracted) has no source file to protect, so there is no fixed script: write a short Python program against the matching library (python-pptx, openpyxl, python-docx) and run it via uv run --with <pkg> python. The two skill invariants still apply: write to a path nothing occupies yet — open it with "xb" rather than handing the library a name, since every one of these save() methods overwrites — and verify the output by reopening it before reporting success.

Edit docx — edit runs, never `Paragraph.text`

When patch-copy refuses a paragraph, python-docx can still edit it — but only run by run. Never assign paragraph.text = "...": it inflicts exactly the loss patch-copy refused, minus the refusal. save(path) overwrites, so open the destination with "xb".

Read references/docx-edit.md before writing any python-docx edit.

Edit pptx — use python-pptx, saving to a new path

pptx edits do not go through office_patch_copy.py. Never assign .text at paragraph or shape level: it rebuilds that subtree as one unformatted run. Edit runs, and open the destination with "xb".

Read references/pptx-edit.md before writing any python-pptx edit.

Output conventions

Name derived files after the source with an operation suffix: report.xlsx → report-updated.xlsx, report-q1-range.csv, spec-p3.txt.
Write into the session workspace (or where the user asked). Both scripts print the written path on success — report it to the user.
--file and --out must both be absolute; a relative path is refused rather than resolved against whatever working directory the shell happens to be in.
Output is staged and renamed on success, so a failed run leaves nothing behind and the same command can be retried at the same path.

Verify before reporting success

Always reopen the derived file with the matching reader and check the result, e.g.:

uv run --with openpyxl python -c "
from openpyxl import load_workbook
ws = load_workbook('/abs/report-updated.xlsx', data_only=True)['Sheet1']
print(ws['B2'].value)"

data_only reads cached values and patch-copy does not recalculate, so a formula reading a cell you just wrote still reads back its old result. Check the cells you wrote, and tell the user the dependent totals are recomputed when Excel opens the file.

A written xlsx string cell that held a literal _xHHHH_ or a carriage return reads back through openpyxl in its escaped form (_x005F_xHHHH_, _x000D_) — see the ST_Xstring note under "Limits". That is the reader, not a verification failure.

If verification fails, say so and show the error — do not present an unverified file.

Limits

pptx edits go through python-pptx (see "Edit pptx"), not patch-copy; slide-copy into a new deck is not supported (python-pptx cannot clone slides) — say so when asked for it.
Patched xlsx string cells become inline strings (valid OOXML; Excel reads them fine). Their text is written as ST_Xstring, so a literal _xHHHH_ in the value is escaped to _x005F_xHHHH_ and a carriage return is stored as _x000D_ — that is what makes Excel show the characters that were written. openpyxl decodes neither, so a verification read of such a cell shows the escaped form; that is the reader, not the file.
Edited XML parts may lose insignificant serialization details (attribute quoting, empty-element form); namespace prefixes and untouched content are preserved.
A patched docx paragraph keeps its characters but not every break opportunity: w:softHyphen leaves no mark in the extract and is dropped, and w:noBreakHyphen comes back as a plain -, so what both lose is only where a line may break. w:sym is refused instead — its glyph is a character the reader can see, not a hyphenation hint.
xlsx extraction refuses ranges over 1,000,000 cells; both scripts refuse packages with more than 10,000 entries, an entry over 256 MiB uncompressed, or over 1 GiB total uncompressed — ask the user for a smaller selection or file instead of retrying.
xlsx extraction does not mask merge followers — the streaming reader cannot see the merge ranges. Excel and openpyxl clear the covered cells when a merge is made, so followers normally extract as empty, matching what the user sees; a file that kept hidden text under a merge (LibreOffice offers to) extracts that text as its own cell. If the anchor check fails inside a merged range, look at the cells before telling the user to re-select.
csv and md carry cell text verbatim, formula-looking text included (=SUM(A1), +1, -); a spreadsheet opening the csv would evaluate it, which matters only for a source the user does not trust — say so if they mean to open the csv in one. In the derived xlsx, text that openpyxl would re-type — a leading = into a live formula, an error code such as #N/A into an error value — is written back as quote-prefixed text; +1 and - are already stored as text and stay that way, and a cell that really holds an error stays an error.
Scanned/image-only PDFs yield no text (no OCR here).

About this skill

Automatic

Doc Ops pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 11/5/2026

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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

cherryhq / cherry-studio

resources/skills/office-transform/SKILL.md

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License AGPL-3.0Imported 11/5/2026Version v1Bundled 4 files · scripts

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What it changes

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Use this skill

Office Transform

When to use which tool

Input: selection references

Operations

Extract — pull the anchored region into a new file

Patch-copy — derive an edited copy, standard library only

Generate — write ad-hoc library code for new documents

Edit docx — edit runs, never Paragraph.text

Edit pptx — use python-pptx, saving to a new path

Output conventions

Verify before reporting success

Limits

SkillSafety

Source

Related skills

More in the registry

Edit docx — edit runs, never `Paragraph.text`