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AI 슬롭 검수 및 인간화 스킬 · modu-ai | DecimalAI

Documentationby modu-ai

AI 슬롭 검수 및 인간화 스킬

AI가 생성한 텍스트에서 기계적 패턴(AI 슬롭)을 감지하고 인간적인 글쓰기로 수정하는 스킬. 모든 텍스트 산출물(블로그, 뉴스레터, 카피, 사업계획서, 계약서, 제안서, 보고서, 이메일, 랜딩 카피 등)의 필수 후처리 단계로 사용합니다. 다음과 같은 요청 시 반드시 이 스킬을 사용하세요: - "AI 티 나는 부분 고쳐줘", "사람이 쓴 것처럼 수정해줘", "AI 슬롭 제거해줘" - "너무 AI 같아", "너무 딱딱해", "자연스럽게 다듬어줘" - "이 글 AI가 쓴 것 같아 보여서...", "어색한 표현 수정해줘" - "보고서가 너무 기계적이야", "이메일이 너무 형식적이야" - "AI 패턴 검사해줘", "기계적인 느낌 없애줘" - Cowork/다른 AI 도구로 생성한 문서의 후처리 검수 요청 시 - 글쓰기 결과물을 인간적인 톤으로 개선하고 싶을 때

Maintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

45%95%of test cases passed +50 pts

Without skill10 of 22

With skill21 of 22

12 fixed9 passed either way0 still failing1 broke

Measured on gemini-3.6-flash8/22/2026v2 · latest is v3

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskHere is a draft bulleted summary: '다음과 같은 주요 업데이트 항목을 확인하세요: • 로딩 속도 30% 향상 • 다크 모드 지원 • 자동 저장 기능' Please process this section.

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 modu-ai-ai-slop-reviewer
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("modu-ai-ai-slop-reviewer")
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 modu-ai-ai-slop-reviewer

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/modu-ai-ai-slop-reviewer/SKILL.md https://app.decimal.ai/s/modu-ai-ai-slop-reviewer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/modu-ai-ai-slop-reviewer/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/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/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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("modu-ai-ai-slop-reviewer")
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/modu-ai-ai-slop-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/modu-ai-ai-slop-reviewer/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 1 other file is 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 modu-ai-ai-slop-reviewer

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/modu-ai-ai-slop-reviewer/SKILL.md https://app.decimal.ai/s/modu-ai-ai-slop-reviewer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/modu-ai-ai-slop-reviewer/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/modu-ai-ai-slop-reviewer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/modu-ai-ai-slop-reviewer.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v2 tested 8/22/2026)
Source: modu-ai/cowork-plugins ↗
License: Apache-2.0

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

AI 슬롭 검수 및 인간화 스킬

AI가 생성한 텍스트는 내용은 정확하지만, 종종 기계적이고 예측 가능한 패턴으로 가득 차 있습니다. 이 스킬은 그 패턴을 체계적으로 찾아내고, 독자가 사람의 목소리로 느낄 수 있도록 글을 재구성합니다.

개요

이 스킬은 사용자가 문체 검수를 요청하거나 다른 스킬이 현재 앱에 노출된 이 스킬로 서술형 본문의 검수를 맡길 때 사용합니다. 블로그, 뉴스레터, 카피, 사업계획서, 계약서, 제안서, 보고서, 이메일, 랜딩 카피, 사업보고, 특허 초안, 강의자료 원고 등에 적용할 수 있습니다.

코드, JSON/CSV 데이터, 차트·표, 숫자 리포트, 단순 조회 응답에는 적용하지 않습니다.

트리거 키워드

이 스킬이 현재 앱에 노출된 경우 다음 요청이나 작업 흐름에서 사용할 수 있습니다. 작성 직후 자동 호출을 보장하지 않습니다:

명시적 요청: "AI 슬롭 제거", "사람이 쓴 것처럼 수정", "AI 티 나는 부분 고쳐줘"
웹사이트/콘텐츠 제작 후 검수: 블로그 포스트, 랜딩 페이지, 카피라이팅, 뉴스레터의 서술형 본문
비즈니스 문서 제작 후 검수: 사업계획서, 제안서, 보고서, 이메일 초안의 서술형 본문
AI 도구 출력물 후처리: Cowork, Claude, ChatGPT 등으로 생성한 텍스트의 검수 요청 시
품질 개선 요청: "너무 기계적인 느낌이야", "자연스럽게 다듬어줘"

워크플로우

1단계: AI 슬롭 패턴 진단

텍스트를 받으면 먼저 체크리스트를 기준으로 문제 패턴을 식별합니다.

🔴 금지어: 즉시 제거/교체

한국어 과용 표현:

~에 대해 살펴보겠습니다 → 바로 본론으로
~의 중요성은 아무리 강조해도 지나치지 않습니다 → 왜 중요한지 구체적으로
~을 통해 알 수 있듯이 → 삭제하거나 직접 서술
다음과 같습니다: + 목록 → 산문으로 통합하거나 진짜 필요할 때만 목록 사용
이처럼 ~은 매우 중요한 의미를 가집니다 → 결론 부분의 공허한 마무리
종합적인, 체계적인, 효율적인 (의미 없이 반복될 때)
또한, 그러나, 따라서, 더불어, 한편 (문단마다 반복될 때)
~라고 할 수 있습니다 → ~입니다 또는 구체적 서술로
~를 기반으로 한 접근 방식 → 무슨 접근 방식인지 직접 설명
핵심은 + 같은 말 반복

영어 과용 표현:

delve, leverage, utilize, harness, embark → 단순한 동사로 대체
pivotal, robust, seamless, cutting-edge, comprehensive → 구체적 묘사로
tapestry, landscape, realm (비유로 쓸 때), synergy, testament
"It's important to note that", "In today's ever-evolving world"
"In conclusion / In summary / In essence" (서두에 오는 결론)
"Not just X, but Y" 구조의 남용
"Certainly!" 로 시작하는 답변

🟡 구조적 AI 패턴

획일적인 문장 길이:

문장 길이가 지나치게 획일적이면 독자가 읽기 편한지 검토합니다. 길이만으로 작성 주체를 판정하지 않습니다.

공식적인 단락 구조:

모든 단락이 "주제문 → 설명 → 예시 → 소결론" 패턴을 따르고 있으면 AI 글입니다
단락 길이와 구조에 변화를 줍니다

AI식 도입부:

"오늘날의 급변하는 세상에서...", "디지털 시대에는..." 류의 문장
"이 글에서는 X에 대해 알아보겠습니다" 형식

AI식 결말:

"지금까지 살펴본 바와 같이..." + 앞 내용 반복
구체적 내용 없이 "더 나은 미래를 위해 노력해야 합니다" 류의 호소

과도한 목록화:

산문으로 쓸 수 있는 내용을 모두 글머리 기호(•)로 나열
3개 이상의 중첩 목록

수동태 남용:

"~이 확인되었습니다", "~가 제안됩니다", "~로 판단됩니다"
→ 주체를 명확히 하고 능동태로

🔴 구조적 슬롭 S1 패턴 3종 (헤드라인·카피 필수 탐지)

마케팅 카피·슬라이드 헤드라인에서 반복되면 어색할 수 있는 구조 세 가지입니다. 문장 구조만으로 AI 작성 여부를 확정할 수 없고, 한 번 사용됐다는 이유만으로 삭제하지 않습니다. 장르·독자·원문의 의도를 보고 수정 여부를 결정합니다.

#	패턴	탐지 신호	[나쁜 예]	수정 방향
1	대시 대비 헤드라인	대시(em-dash `—`)로 문장을 분할해 대비를 만드는 "X — Y" 구조 (대시 대비 헤드라인)	[나쁜 예] "복붙에서 위임으로 — 목표만 주면, 나머지는 에이전트가"	대시 제거 후 한 문장으로 통합하거나 두 문장으로 분리
2	조사·체언 종결 조각문	문장이 조사(~은/에/로)나 체언(명사형)으로 끝나는 파편화된 조각문 (조사·체언 종결)	[나쁜 예] "성공의 열쇠 — 자동화" (체언 종결 조각문)	서술어를 포함한 완전한 문장으로 재작성
3	"A에서 B로" 전환 공식	"X에서 Y로" 전환 공식으로 도입/헤드라인을 여는 상투문	[나쁜 예] "엑셀에서 노션으로, 일하는 방식이 바뀌었다" (전환 공식)	전환 공식 대신 구체적 사례·근거로 시작

탐지 원본: plugins/moai-writer/skills/korean-humanize/references/ai-tell-taxonomy.md D카테고리("X에서 Y로" 변환 공식) + J카테고리(em-dash 남용). 본 게이트가 이 3종을 잡지 못하면 커머스/카피 스킬 체인(본 스킬만 호출)을 통과한 채 slop 카피가 배포됩니다.

2단계: 인간화 수정 전략

진단이 끝나면 아래 전략을 적용해서 텍스트를 다시 씁니다.

전략 1: 목소리 구체화

글쓴이가 제공한 의견·판단·경험이 있으면 분명하게 드러냅니다.
"이 기능은 효율적입니다" → "어떤 작업이 어떻게 달라졌는지 확인할 자료가 필요합니다"
사례·날짜·숫자는 원문이나 확인된 자료에 있을 때만 씁니다. 없으면 구체화를 위한 질문으로 남깁니다.

전략 2: 리듬 변화

짧은 문장으로 강조점을 만드세요. 딱 한 줄만
그 다음 문장에서 맥락을 채웁니다. 이렇게 하면 독자의 호흡이 자연스러워집니다
길고 복잡한 문장은 두 개로 나누거나, 때로는 일부러 길게 두어서 설명이 풍부한 느낌을 줍니다

전략 3: 전환어 다이어트

"또한", "더불어", "한편" 등의 전환어가 연속으로 나오면 하나씩 삭제해보세요
전환 없이 자연스럽게 이어지는지 확인합니다. 대부분은 이어집니다
꼭 필요한 전환에만 씁니다. 그래야 전환어가 의미를 갖습니다

전략 4: 목록 → 산문 변환

3개 이하의 항목은 산문으로: "A, B, C 세 가지가 핵심입니다"
목록이 정말 필요한 경우: 실제로 비교하거나 순서가 중요한 정보

전략 5: 구체성 주입

"많은 사람들이" → 실제 조사 대상·표본·출처를 확인하거나 표현을 삭제
"상당한 개선" → 측정 자료가 있으면 수치와 기간을 표시하고, 없으면 효과를 단정하지 않음
"최근의 연구에 따르면" → 논문·발행일을 확인할 때만 구체적으로 인용

전략 6: 도입부 재작성

AI 글의 도입부는 거의 항상 버려야 합니다. 처음 1-2문장을 직접 새로 씁니다
독자가 궁금할 만한 질문이나 의외의 사실로 시작합니다
또는 결론을 먼저 던지는 역피라미드 방식도 효과적입니다

전략 7: 결말 재작성

앞 내용을 단순 반복하지 않습니다
다음 행동(CTA), 열린 질문, 혹은 독자에게 직접 던지는 한 마디로 마무리합니다

3단계: 수정 출력 형식

수정된 텍스트를 제공할 때는 다음 형식을 따릅니다:

### 🔍 진단 요약
발견된 패턴: (예: AI식 도입부, 수동태 남용, 금지어 12개, 획일적 문장 길이)
심각도: 낮음 / 보통 / 높음

### ✍️ 수정된 텍스트
(수정된 전체 텍스트)

### 📝 주요 변경사항
(중요한 수정 포인트를 3~5개 간략히 설명)

원본이 길 경우(500자 이상), 진단 요약과 주요 변경사항을 먼저 보여주고 수정된 전체 텍스트를 그 다음에 제공합니다.

사용 예시

예시 1: 제공된 사실만 보존

입력: "오늘날의 급변하는 비즈니스 환경에서 효율적인 팀 커뮤니케이션의 중요성은 아무리 강조해도 지나치지 않습니다. 정기 미팅, 역할 분담, 피드백이 필요합니다."

수정: "팀 소통을 개선하려면 정기 미팅의 목적을 정하고 담당 역할을 분명히 하며, 작업 결과에 대한 피드백을 주고받아야 합니다."

확인: 회의 시간을 줄였다거나 생산성이 높아졌다는 결과는 원문에 없으므로 추가하지 않습니다.

예시 2: 영어 문장

입력: "In today's rapidly evolving landscape, it is crucial to leverage cutting-edge technologies to streamline your workflow."

수정: "Choose tools that help with the work your team actually does."

확인: 사용한 앱의 개수, 비용, 테스트 결과는 제공되지 않았으므로 넣지 않습니다.

예시 3: 원인 추정과 관찰값 분리

입력: "2024년 고객 만족도가 전년 대비 15% 향상됐습니다. 제품 품질과 응대 속도가 주요 요인으로 판단됩니다."

수정: "2024년 고객 만족도는 전년보다 15% 높았습니다. 제품 품질과 응대 속도가 영향을 줬는지는 별도 자료로 확인해야 합니다."

확인: 불량률·응대 시간·증원 계획은 원문에 없으므로 만들지 않습니다.

출력 형식

이 스킬은 다음 구조로 결과를 출력합니다:

진단 요약: 발견된 AI 패턴 목록과 심각도(낮음/보통/높음)
수정된 텍스트: 원본의 의미를 유지하면서 인간적인 목소리로 재구성된 전체 텍스트
주요 변경사항: 3-5개 핵심 수정 포인트 설명

진단 요약은 수정 전 사용자에게 "어떤 패턴이 발견되었는지"를 빠르게 파악하게 합니다. 심각도가 '높음'인 경우 AI 패턴이 많아 수정 전후 차이가 클 것임을 시사합니다.

주의사항

✅ 의미 보존 원칙

형태는 바꾸되 핵심 정보와 메시지는 반드시 보존합니다
원문에 없는 사실·경험·수치·고유명사를 추가하지 않습니다. 숫자·날짜·계약 조건은 대조합니다.

✅ 톤 앤 매너 유지

공식 문서, 보고서, 기술 문서는 격식이 필요합니다
대상 독자와 목적에 맞는 톤을 유지합니다
과도하게 구어체로 만들지 않습니다

✅ 문맥 기반 판단

모든 AI 표현을 기계적으로 제거하지 않습니다
"활용", "중요합니다" 같은 표현도 문맥에 따라 자연스러울 수 있습니다
패턴의 반복이 문제이지, 단어 자체가 나쁜 것은 아닙니다

✅ 사용자 의도 확인

사용자가 유지하고 싶은 스타일 특성이 있다면 확인합니다
예: "전문적인 톤은 유지할게요, AI 패턴만 제거해드릴까요?"

❌ 적용 제외 대상

이 스킬은 다음 경우에 적용하지 않습니다:

코드 (프로그래밍 언어 소스)
JSON/CSV 데이터 (구조화된 데이터)
차트·표 (시각화 자료)
숫자 리포트 (통계, 지표)
단순 조회 응답 (정보 제공)

References

파일	로드 조건
references/kr-pii-masking.md	개인정보 마스킹 공유 규격 (이 스킬이 물리 소유). 입력에 주민등록번호·계좌·카드·연락처·주소 등이 포함된 산출물을 처리하는 모든 스킬(career·recruiter·cs·accountant·lawyer 라인)이 참조 — 마스킹 자릿수 규칙, 산출물 재출력 금지 원칙, 개인정보 보호법 국외 이전 고지 조건

Classification: Post-processing Skill (Text Output Quality)

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Writing & Editing pack

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LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

modu-ai / cowork-plugins

moai-core/skills/ai-slop-reviewer/SKILL.md

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License Apache-2.0Imported 10/13/2026Version v3Bundled 1 file

Illustrative Want to see what modu-ai/ai-slop-reviewer would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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AI 슬롭 검수 및 인간화 스킬

개요

트리거 키워드

워크플로우

1단계: AI 슬롭 패턴 진단

🔴 금지어: 즉시 제거/교체

🟡 구조적 AI 패턴

🔴 구조적 슬롭 S1 패턴 3종 (헤드라인·카피 필수 탐지)

2단계: 인간화 수정 전략

전략 1: 목소리 구체화

전략 2: 리듬 변화

전략 3: 전환어 다이어트

전략 4: 목록 → 산문 변환

전략 5: 구체성 주입

전략 6: 도입부 재작성

전략 7: 결말 재작성

3단계: 수정 출력 형식

사용 예시

예시 1: 제공된 사실만 보존

예시 2: 영어 문장

예시 3: 원인 추정과 관찰값 분리

출력 형식

주의사항

✅ 의미 보존 원칙

✅ 톤 앤 매너 유지

✅ 문맥 기반 판단

✅ 사용자 의도 확인

❌ 적용 제외 대상

관련 스킬

선행 스킬 (입력 생성)

후속 스킬 (선택적)

통합 워크플로우

References

SkillSafety

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