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Humanize Korean — AI 한글 티 제거 오케스트레이터 (v1.5) · bam-bam-2 | DecimalAI

Documentationby bam-bam-2

Humanize Korean — AI 한글 티 제거 오케스트레이터 (v1.5)

AI(ChatGPT·Claude·Gemini 등)가 쓴 한글 텍스트를 "사람이 쓴 글처럼" 윤문해주는 오케스트레이터 스킬. 번역투·영어 인용 과다·기계적 병렬·관용구·피동태 남용·접속사 남발·리듬 균일성·이모지/불릿 과다 등 10대 카테고리 40+ AI 티 패턴을 탐지·분류해 내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스러운 한국어로 재작성한다. 트리거 — "AI 티 없애줘", "AI 같은 글 자연스럽게", "GPT/ChatGPT 문체", "AI 번역투 고쳐", "사람이 쓴 것처럼 윤문", "AI 윤문", "ChatGPT 티 제거", "한글 AI 탐지·윤문", "AI 글 사람처럼", "번역투 제거", "영어 인용 많은 글 윤문", "AI 글 티 안 나게", "휴머나이저", "humanize Korean", "AI detector bypass 한글". 후속 작업 — "특정 카테고리만 다시", "윤문 강도 조정", "장르 바꿔서", "이 문단만", "2차 윤문" 도 모두 이

Maintained

SkillScore

SkillScore80/100

Test results 73 · Live runs 87

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

45%73%of test cases passed +27 pts

Without skill10 of 22

With skill16 of 22

6 fixed10 passed either way6 still failing0 broke

Measured on gemini-3.6-flash9/26/2026v119 comparable

Track record

Agent runs: 25; 17 in the last 30 days

See one task, with and without the skill

One test task기존 작업 `2026-04-26-002` 결과에서 특정 카테고리 어휘 패턴만 다시 윤문하라는 요청이 들어왔습니다.

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 bam-bam-2-humanize-korean
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("bam-bam-2-humanize-korean")
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 bam-bam-2-humanize-korean

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/bam-bam-2-humanize-korean/SKILL.md https://app.decimal.ai/s/bam-bam-2-humanize-korean/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/bam-bam-2-humanize-korean/SKILL.md

This gets SKILL.md only. The skill's 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/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/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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("bam-bam-2-humanize-korean")
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/bam-bam-2-humanize-korean/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/bam-bam-2-humanize-korean/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 9 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 bam-bam-2-humanize-korean

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/bam-bam-2-humanize-korean/SKILL.md https://app.decimal.ai/s/bam-bam-2-humanize-korean/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/bam-bam-2-humanize-korean/SKILL.md

This gets SKILL.md only. The skill's 9 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/bam-bam-2-humanize-korean/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/bam-bam-2-humanize-korean.json

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 9/26/2026
Source: bam-bam-2/solo-skills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Humanize Korean — AI 한글 티 제거 오케스트레이터 (v1.5)

v1.5 변경 고지 (2026-04-26) — v1.1 베이스라인 + Monolith Fast Path v1.2(voice profile)·v1.3(candidate pool)·v1.4(역할별 모델 분산)는 모두 핫패스 비용을 잡지 못해 5,000자 입력에 25분이 걸렸습니다. v1.5는 v1.1 단순 구조로 롤백한 뒤 단일 호출 monolith 에이전트만 추가한 설계입니다.

Fast 모드(디폴트) — humanize-monolith 에이전트가 한 콜에서 탐지·윤문·자체검증 일괄 처리. 도구 호출 4~~5회. 5,000자 이하 wall-clock 2~~3분 목표.

Strict 모드(--strict) — v1.1 5인 파이프라인 그대로(detector·rewriter·auditor·reviewer + taxonomist 분류 자산 유지). 정밀 검증·장문(8,000자+) 처리·etc.

삭제됨: voice profile·candidate pool·promotion-checklist·sample-collection·권한 위계 §1~§6.

유지됨: 분류 체계 본진(C-9·C-10·D-7·H-3·I-3·I-4 등 v1.2~v1.3.1 신규 패턴)·rewriting-playbook·5인 에이전트 정의(strict 모드 백본).

Phase 0: 컨텍스트 확인 및 모드 결정

작업 시작 시 가장 먼저 다음 한 줄을 사용자에게 출력한다.

humanize-korean v1.5 — {fast|strict} 모드 / run_id: {YYYY-MM-DD-NNN}

모드 결정

사용자가 --strict·"정밀 모드"·"5인 파이프라인" 명시 → strict
입력 8,000자 초과 → strict (자동 승급 + 사용자에 1줄 고지)
그 외 모두 → fast (디폴트)

run_id 결정

모든 경로는 cwd 기준. 새 폴더 생성도 cwd 기준 _workspace/{YYYY-MM-DD-NNN}/에 만든다.
기존 시퀀스 확인은 Glob 도구로 표지 파일을 매칭해 간접 조회. 올바른 사용법: Glob(pattern="_workspace/YYYY-MM-DD-*/01_input.txt") → 결과에서 폴더명 추출 후 NNN 최댓값 + 1. 주의: Glob은 디렉토리 자체는 매칭하지 못한다. 반드시 그 안의 표지 파일(01_input.txt)을 매칭할 것. Bash ls는 OS·셸 환경에 따라 경로 해석이 달라지므로 사용 금지.
당일 폴더가 없으면 NNN = 001. 있으면 마지막 NNN + 1.
부분 재실행 신호("이 카테고리만 다시"·"2차 윤문")일 경우 기존 run_id 재사용 + strict 모드로 자동 승급.

Fast 모드 (디폴트)

Phase 1: 입력 저장

cwd 기준 _workspace/{run_id}/ 생성
입력 텍스트를 01_input.txt에 저장
첫 300자로 장르 자동 추정 (사용자 명시 시 우선)

Phase 2: Monolith 호출

humanize-monolith 에이전트를 Agent 도구로 1회 호출.

입력:

input_path: <abs path>/_workspace/{run_id}/01_input.txt
quick_rules_path: ${CLAUDE_SKILL_DIR}/references/quick-rules.md
genre_hint: 칼럼 | 리포트 | 블로그 | 공적 | null

출력 (에이전트가 직접 작성):

_workspace/{run_id}/final.md — 윤문본
_workspace/{run_id}/summary.md — 메트릭·자체검증·하이라이트

monolith는 단일 호출 안에서 다음을 모두 수행 (자세히는 에이전트 정의 참조):

quick-rules 룰북 로드 → 메모리에서 패턴 탐지 + 윤문 + 자체검증 6항 점검
변경률 50% 초과 시 자동 롤백
자체검증 위반 시 1회 부분 재실행
final.md + summary.md 작성

Phase 3: 결과 전달

사용자에게 다음 4개를 반환:

한 줄 상태: 완료. 변경률 X% / 등급 Y / 자체검증 N/6 통과
윤문본 본문 (마크다운 블록)
summary.md의 핵심 표 (메트릭 + 카테고리 탐지 + 자체검증)
등급 B 이하면 "정밀 검증이 필요하면 --strict로 5인 파이프라인" 안내

디폴트 wall-clock 목표: 5,000자 이하 2~~3분, 8,000자 5~~7분.

Strict 모드 (`--strict` 또는 자동 승급)

v1.1 5인 파이프라인 그대로. 검증 분리·재윤문 루프가 의미 있을 때만 사용.

Phase A: 탐지

ai-tell-detector 호출 → 02_detection.json

Phase B: 윤문 (최대 3회 루프)

korean-style-rewriter 호출 → 03_rewrite.md + 03_rewrite_diff.json

Phase C: 병렬 검증 (에이전트 팀)

TeamCreate로 humanize-review-team 구성:

content-fidelity-auditor → 04_fidelity_audit.json (의미 동등성)
naturalness-reviewer → 05_naturalness_review.json (잔존·과윤문)

TeamDelete 후 종합 판정 매트릭스에 따라 분기:

fidelity	naturalness	종합	후속
full_pass	accept / accept_with_note	최종 승인	Phase D
full_pass	rewrite_round_2	2차 윤문	Phase B 재호출 (target finding)
full_pass	rollback_and_rewrite	롤백 후 재윤문	윤문가에 edit 롤백 지시
conditional_pass	-	롤백된 edit만 재시도	Phase B 재호출
fail	-	전면 재작업	Phase B 전면 재호출

2차/3차 윤문 진입 시 03_rewrite_v2.md·v3.md로 버전 분리. **최대 3회 후 미해결이면 hold_and_report**로 사람 개입.

Phase D: 최종 출력

final.md에 최종 윤문본 복사
summary.md 생성 (fast 모드와 동일 포맷)
사용자에게 결과 + 등급 + 안내

부분 재실행 / 후속 명령

사용자 신호	처리
"특정 카테고리만 다시"	strict 모드로 자동 전환, 해당 카테고리 finding만 Phase B 재실행
"이 문단만"	strict 모드, 해당 문단만 입력으로 새 run_id 생성
"2차 윤문"·"`/humanize-redo`"	기존 run_id의 `final.md`를 새 입력으로 strict Phase B 재실행
"윤문 강도 조정"	strict 모드, `min_severity` 옵션 변경 후 Phase A부터 재실행
"장르 바꿔서"	`genre_hint` 변경 후 Phase A부터 재실행

옵션 (인자 끝에 자연어로)

장르: 칼럼|리포트|블로그|공적 — 장르 명시 (생략 시 자동 추정)
강도: 보수|기본|적극 — 윤문 강도 (기본값: 기본)
최소심각도: S1|S2|S3 — 탐지 임계값 (기본값: S2)
--strict — 5인 파이프라인 강제 사용

데이터 흐름 요약

Fast 모드 (디폴트)

01_input.txt
    ↓ [humanize-monolith — 단일 호출]
    ├ 메모리: quick-rules 로드 → 탐지 → 윤문 → 자체검증
    └→ final.md + summary.md

Strict 모드

01_input.txt
    ↓ [ai-tell-detector]
02_detection.json
    ↓ [korean-style-rewriter]
03_rewrite.md + 03_rewrite_diff.json
    ↓ [병렬 팀]
    ├→ [content-fidelity-auditor] → 04_fidelity_audit.json
    └→ [naturalness-reviewer]      → 05_naturalness_review.json
    ↓ [오케스트레이터 종합]
    ├→ (재작업) Phase B로 복귀 (최대 3회)
    └→ (승인) final.md + summary.md

에이전트 호출 규칙

모델: 모두 model: opus 통일 (v1.1 베이스라인). 모델 다운그레이드는 v1.4에서 시도했으나 도구 호출 chain이 진짜 병목이라 효과 미미했음.

에이전트 정의 위치: 저장소 루트 agents/에 12종 정의(플러그인 컨벤션). Claude Code 탐색 경로:

플러그인 설치 시 — humanize-korean 플러그인이 agents/를 번들로 제공(전역).
스크립트 설치 시 — install.sh가 agents/*.md를 ~/.claude/agents/에 심링크(전역).

필요 에이전트 6종:

humanize-monolith (v1.5 신규, fast 전용)
ai-tell-detector · korean-style-rewriter · content-fidelity-auditor · naturalness-reviewer (strict 5인 중 4명)
korean-ai-tell-taxonomist (분류 체계 유지·확장 — 본 스킬 실행 중에는 호출 안 됨, 별도 명령으로만 트리거)

테스트 시나리오

Fast 정상 흐름

입력: ChatGPT가 생성한 AI 칼럼 초안 (2,000~5,000자, 번역투·결말 공식·hype 어휘 풍부)
기대: monolith 1콜로 변경률 15~~25%, 등급 A/B, wall-clock 2~~3분, 자체검증 5~6/6

Strict 정밀 검증 흐름

사용자 명시 --strict 또는 8,000자+ 입력
5인 파이프라인 끝까지 실행, 변경률 18~22%, 검증팀 full_pass

엣지 케이스 — 이미 사람이 쓴 글

monolith 자체 탐지에서 매치 거의 없음 → 변경률 5% 미만 + summary.md에 "윤문 불필요 가능성" 메모
사용자가 --strict로 강제 검증 가능

주의 사항

의미 불변이 최상위 불문율. monolith·strict 모두에서 위반 즉시 롤백.
수치·고유명사·직접 인용은 탐지/윤문 대상 아님. Do-NOT list 엄수.
장르 이탈 금지. 칼럼이 에세이로, 에세이가 문학으로 옮겨가지 않는다.
register 보존. 격식체 입력 → 격식체 출력. AI 티는 문법·수사이지 격식 자체가 아님.
변경률 30% 초과 → 경고, 50% 초과 → 강제 중단.
자동 로드 금지. 프로젝트 CLAUDE.md 등 다른 파일을 자동 파싱해 옵션을 추론하지 않는다.

참고 자료

슬림 룰북 (Fast 전용): references/quick-rules.md — S1·S2 핵심 패턴 + 자체검증 체크리스트
분류 체계 본진 (Strict 전용): references/ai-tell-taxonomy.md — 10대분류 × 40+ 패턴 전수
윤문 처방 (Strict 전용): references/rewriting-playbook.md — 카테고리별 치환 레시피·장르별 허용 표
웹 서비스 스펙 (옵션): references/web-service-spec.md — 웹 확장 시 로드

출처

이 스킬의 분류 체계는 내가 만든 것이 아니다. 외부 연구를 가져와 세운 것이다.

한국 번역학계 번역투 연구 — 8대 번역투 유형이 분류 체계의 뼈대다. 이영옥(2001) 「무생물 주어 타동사구문의 영한번역」 번역학연구 2(1): 53-76 을 비롯해 윤미선·김택민·임진주·홍승연(2018), 이상빈(2017·2018a·2018b), 마승혜(2018), 김혜림(2022), 김혜영(2019) 등.
KatFish 정량 보고서 — 인간/AI 텍스트의 어절 수, POS 다양성, 한자어 명사화 밀도 같은 수치 임계값은 이 보고서에서 가져왔다. (예: 에세이 평균 어절 인간 4.35 vs AI 8.56)
arXiv 계열 AI 텍스트 탐지 연구 — 일부 지표 정의를 참고했다.

학자 이름·연도·저널·페이지는 references/scholarship.md에 원문 그대로 보존돼 있다. 자체 추정이나 확장 없이 옮겼다.

내가 더한 부분은 이것뿐이다 — 한국어 실무에서 반복적으로 지적받은 표현 (~가 아니라 대조 구문, 여정, 굴러가다, 방어적 안심 문장 등)을 금지어로 추가하고, 윤문 강도를 Fast/Strict로 나눠 실제 원고에 돌 수 있게 절차화한 것.

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skills/humanize-korean/SKILL.md

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License MITImported 8/27/2026Version v1Bundled 9 files

Illustrative Want to see what bam-bam-2/humanize-korean 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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Humanize Korean — AI 한글 티 제거 오케스트레이터 (v1.5)

Phase 0: 컨텍스트 확인 및 모드 결정

모드 결정

run_id 결정

Fast 모드 (디폴트)

Phase 1: 입력 저장

Phase 2: Monolith 호출

Phase 3: 결과 전달

Strict 모드 (--strict 또는 자동 승급)

Phase A: 탐지

Phase B: 윤문 (최대 3회 루프)

Phase C: 병렬 검증 (에이전트 팀)

Phase D: 최종 출력

부분 재실행 / 후속 명령

옵션 (인자 끝에 자연어로)

데이터 흐름 요약

Fast 모드 (디폴트)

Strict 모드

에이전트 호출 규칙

테스트 시나리오

Fast 정상 흐름

Strict 정밀 검증 흐름

엣지 케이스 — 이미 사람이 쓴 글

주의 사항

참고 자료

출처

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Strict 모드 (`--strict` 또는 자동 승급)