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omd-cjk-type-shader — 먹 번짐: CJK display type as sumi ink on hanji · kwakseongjae | DecimalAI

Frontendby kwakseongjae

omd-cjk-type-shader — 먹 번짐: CJK display type as sumi ink on hanji

One effect, done well: 먹 번짐 — a large Korean, Japanese or Chinese display headline that reads as sumi ink printed on hanji. The heading stays real, selectable DOM text in ink color from the first frame; a WebGL2 layer over it adds what real ink does: a wet, glossy stroke with a soft bloom that shrinks as it dries, ink pooling darker at stroke rims, ends and corners, paper fibers showing through thick strokes, and capillary feathering along fibers at a few edges only. Two parameters (bleed, durat

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%100%of test cases passed +68 pts

Without skill7 of 22

With skill22 of 22

15 fixed7 passed either way0 still failing0 broke

Measured on gemini-3.8-flash11/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhat are the default values for the bleed and duration parameters in the ink bleeding effect configuration?

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 kwakseongjae-omd-cjk-type-shader-a66d9b
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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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 kwakseongjae-omd-cjk-type-shader-a66d9b

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/kwakseongjae-omd-cjk-type-shader/SKILL.md https://app.decimal.ai/s/kwakseongjae-omd-cjk-type-shader-a66d9b/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/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/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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("kwakseongjae-omd-cjk-type-shader-a66d9b")
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/kwakseongjae-omd-cjk-type-shader/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/kwakseongjae-omd-cjk-type-shader/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 kwakseongjae-omd-cjk-type-shader-a66d9b

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/kwakseongjae-omd-cjk-type-shader/SKILL.md https://app.decimal.ai/s/kwakseongjae-omd-cjk-type-shader-a66d9b/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/kwakseongjae-omd-cjk-type-shader/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/kwakseongjae-omd-cjk-type-shader-a66d9b/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/kwakseongjae-omd-cjk-type-shader-a66d9b.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: v1, tested 11/2/2026
Source: kwakseongjae/oh-my-design ↗
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

omd-cjk-type-shader — 먹 번짐: CJK display type as sumi ink on hanji

Ink soaking into paper is the one image Korean, Japanese and Chinese writing share. This skill makes a big CJK headline read like it was printed or brushed in sumi on hanji. On a fast skim it should look like a calligraphy poster, not a filter.

How it works. The heading is ordinary DOM text in the ink color, readable and selectable from the first frame. The skill traces every grapheme where the browser laid it out and builds three fields from that mask: distance outside the strokes, distance inside them, and local ink coverage. A WebGL2 layer drawn over the text then adds only what real ink does:

wet → dry: the stroke starts glossy-dark with a soft bloom around it, and the bloom shrinks back as the ink dries;
pooling: ink collects darker along the stroke rim and at stroke ends and convex corners (low local coverage);
kasure: paper fibers show through the middle of thick strokes once dry;
nijimi: capillary feathering runs along a few paper fibers at some edges, chosen by low-frequency noise, never as an even ring around every glyph;
paper: a procedural hanji texture (SVG turbulence, no image) on the page.

It plays once when the heading enters the viewport, then the render loop stops. Line wrapping, keep-all, font swaps and resizes are re-traced.

Working example: examples/demo.html (single file, Korean + Japanese + Traditional Chinese). Proof: scripts/verify.mjs.

1. When to use / when not

Use when:

a Korean, Japanese or Chinese display headline (≥ 40px; hero, launch title, campaign line, section opener) should feel inked, calligraphic, "printed on hanji / washi / 宣紙";
the brand's ink and paper colors are known (DESIGN.md foreground/canvas, or the heading's computed color and the page background).

Don't use for:

body text, labels, buttons, nav, anything below ~32px (pooling and feathering need stroke width to read);
Latin-only headlines (it works, but the cultural point is gone);
more than one or two headings per page. Boldness in one place (tf-shaders rule);
light text on dark paper. The effect darkens ink, so it needs a paper that ink reads on.

2. The effect and its two parameters

Param	Range	Default	What it does
`bleed`	0–1	0.7	How far the capillary feathering travels along fibers (up to `0.12em × bleed`, at the edges noise picks) and how big the wet bloom gets. 0.4 = barely there, 0.8 = a wet brush.
`duration`	s	2.6	Time from wet to dry. The bloom peaks early and shrinks; pooling and fiber show-through come in as it dries. Under 1.5s it reads as a flash, over 4s as a loading state.

Everything else is fixed on purpose. Ink = the heading's computed color, darkened to 25% for pools; paper = the page background color. Text ink should be a sumi grey-black, not pure black (the demo uses #3a3530 on #f3eee4, about 10:1), so the darker pools have room to show. One signature, not a control panel.

3. The contract that keeps text text

The heading is a normal <h1>/<h2> with its real text. inkBleed() wraps the text in .ink-text and inserts an aria-hidden="true", pointer-events: none layer over it (z-index 2). The layer only darkens, lightens or extends what is already there. It never replaces or re-draws the glyphs, and with the layer removed the heading is still complete.
The headline paints on the first frame. No opacity: 0, no clip, no wait for the shader. tf-shaders measured LCP going to 7.8s when hero copy waited for a shader.
Selection, copy, find-in-page, translation, screen readers and SEO see plain text. verify.mjs checks that selecting the heading returns its full text in every pass.
Leave ~0.4em of clear space around the heading (the demo uses margin-bottom: 0.4em). Feathering and bloom extend up to ~0.3em past the glyphs.
Never draw CJK headlines with a canvas/Text shader component. Canvas text loses selection, accessibility, indexing and correct CJK shaping (tf-shaders "Don't" list).

4. CJK typesetting (applies before the shader)

From /hangul (HG rules) for Korean; ja/zh scoped separately so Korean rules don't leak:

.ink:lang(ko) { word-break: keep-all; overflow-wrap: anywhere;  /* HG-10, scoped to :lang(ko) */
                letter-spacing: -0.02em;                          /* HG-7: display never below -0.03em */
                line-height: 1.3; }                               /* HG-5: wrapping display >= 1.3 */
.ink:lang(ja), .ink:lang(zh-Hant) {
  line-break: strict;                                 /* kinsoku: no small kana / closing marks at line start */
  word-break: keep-all; overflow-wrap: anywhere;      /* break only at <wbr> phrase marks you place */
  letter-spacing: 0; line-height: 1.4; }
:lang(ko), :lang(ja), :lang(zh) { font-synthesis: none; } /* no fake bold/italic on CJK */

Mark Japanese/Chinese phrase boundaries with <wbr> (BudouX-style: 墨がにじむように、<wbr>名前が<wbr>紙に染みこむ。). In headless Chrome 149, word-break: auto-phrase computed correctly but did not produce phrase breaks in this demo, so don't rely on it.
Korean: line breaks only at spaces between 어절, never syllable by syllable. Hand-place display line breaks when the copywriter cares (tf-shaders cjk-type.md).
Set lang on the heading (ko, ja, zh-Hant/zh-Hans). It picks the right glyph variants for shared Han code points and scopes the rules above.
Fonts: use a face that ships the weight you set (HG-12). The demo uses Noto Serif KR / JP / TC 700 from Google Fonts, which loads only the glyph slices the page uses. A myeongjo/mincho/song face suits ink. Pretendard works too. Trace after document.fonts.ready so the field follows the real face, not the fallback.

5. Library, license, budget

Library: @paper-design/shaders (vanilla ShaderMount), pinned 0.0.81, WebGL2. Decision record (verified 2026-10-13 from the npm tarballs' LICENSE files, npm metadata and the GitHub license API):

	`@paper-design/shaders` 0.0.81	`shaders` 4.0.4 (shaders.com)
License	Apache-2.0 (LICENSE + NOTICE "Paper Shaders, Copyright 2026 Paper")	MIT (© 2026 Shader Effects Inc.); dep `typegpu` 0.12.3 MIT (Software Mansion)
Redistribution in a skill/template	allowed; keep NOTICE if you vendor code	allowed
Runtime	WebGL2: every current desktop and mobile browser	WebGPU only, no fallback (Chrome/Edge, Android 12+ Chrome, Safari 26+, recent Firefox)
JS for this effect	17.5 KiB min / 5.3 KiB gzip (esbuild, `ShaderMount` only); CDN per-file `+esm` 16.4 KiB / 5.0 KiB gzip	2,535 KiB min / 710 KiB gzip (esbuild, `Shader`+`MeshGradient` from `shaders/react`)
Telemetry	none (its only `fetch()` reads a local blob's content type)	on by default: samples 5% of sessions to `shaders.com/api/telemetry`; pass `disableTelemetry`
Custom GLSL + textures	yes (`ShaderMount(el, frag, uniforms)`, image uniforms)	via its component graph

The effect needs a custom fragment shader and one texture, which is exactly what ShaderMount is, at 1/130th of the WebGPU runtime's weight and with twice the browser reach. The demo imports Paper from jsDelivr (pinned, per-file build) and does not vendor it, so no NOTICE copy is needed. If you vendor Paper's files, copy its NOTICE alongside. If a project already ships shaders, this effect still runs on Paper. Don't load both runtimes on one page.

Budget for one heading:

JS: 5.0–5.3 KiB gzip library + 5.3 KiB gzip of effect code (shader, two distance transforms, coverage blur, tracing; measured unminified with comments).
GPU: one quad, one texture lookup and 7 value-noise calls per pixel, over the heading box plus a ≤0.32em pad, minPixelRatio: 1, capped at 1920×1080 px. It runs for duration and then setSpeed(0) stops the rAF loop. Paper also pauses it offscreen and in hidden tabs.
CPU: per layout change (font swap, resize, re-wrap; debounced 120ms), two Euclidean distance transforms (outside and inside the strokes) and one separable box blur, at half resolution above 64px type.
Paper texture: an inline SVG feTurbulence data URI on html (~600 bytes), no image request.
Fonts are the real cost. CJK faces are large. Load only the display weight, as unicode-range slices.

6. Fallback and reduced motion

No WebGL2 (probe canvas.getContext('webgl2') before mounting; ShaderMount throws if it can't get a context or link the program): no layer is inserted, the heading gets .ink-static (a faint CSS text-shadow). The heading is already in ink on textured paper, so it is complete without the shader. verify.mjs forces this path by stubbing getContext.
prefers-reduced-motion: reduce: mount at the dried frame with speed 0. The reader gets the final inked state as one still frame, with no motion and no rAF loop.
Shader compile failure (one happened while building this: a precision mismatch with Paper's vertex shader) takes the same .ink-static path. In your own GLSL, declare uniform mediump vec2 u_resolution;, because the precision must match Paper's vertex shader.

7. Verify on a real GPU

next build or a passing lint proves nothing for shaders (tf-shaders rule). Run:

npm i -D playwright-core && npx playwright-core install chromium   # a fresh Playwright Chromium, not your own Chrome
node <skill>/scripts/verify.mjs path/to/page.html ./ink-verify --frames 36

It launches headless Chromium with --enable-gpu --ignore-gpu-blocklist --use-angle=metal (macOS) and runs three passes: GPU, reduced-motion, no-WebGL. Then it:

records the WebGL renderer string, so you know whether a real GPU or SwiftShader drew the frames;
steps the shader clock deterministically and saves frames (feed them to ffmpeg for the demo GIF/MP4);
proves rendering with pixels: shader-on vs shader-off inside the heading box, and first frame vs dried frame;
checks that the selected text equals the heading text, the layer is aria-hidden + pointer-events: none, reduced motion is still, and no-WebGL leaves no canvas.

Measured on 2026-10-13 (headless Chromium 149, ANGLE Metal Renderer: Apple M5 Pro, not SwiftShader): 10.2% of heading-box pixels changed by the shader, wet ≠ dry (5.9% differ), all checks passed. Look at the frames anyway, at full size: a misaligned field (wrong baseline) shows as pooling offset from the strokes, and a feathering setting that is too strong reads as fur. Only your eyes catch those.

8. Next.js / React port

Keep the same function and lifecycle in a 'use client' leaf: useEffect(() => { let h; document.fonts.ready.then(async () => { const { ShaderMount } = await import('@paper-design/shaders'); h = await inkBleed(ref.current, { bleed, duration }); }); return () => h?.mount.dispose(); }, []). Render the heading server-side as plain text, so the shader only decorates it after hydration. Don't use @paper-design/shaders-react's <ShaderMount> here: it doesn't give you the WebGL2 probe, the fallback class, or control over when the field is re-traced.

9. Attribution

The budget-first framing, the fallback-is-design rule, the first-frame-LCP rule, 어절-not-syllable reveals, keep-all, and the real-GPU screenshot loop come from the owner's tf-shaders experiment (skill/shader-landing/SKILL.md, references/cjk-type.md, scripts/shoot.mjs, RESEARCH.md, 2026-10). Korean type values come from OmD's /hangul skill. The distance transform is Felzenszwalb & Huttenlocher's linear-time EDT, written fresh here.

Don't

Don't hide the heading until the shader is ready. The text is the content; the ink is the frame.
Don't animate syllable by syllable, and don't split Hangul into per-character spans for a reveal.
Don't run the loop forever. A dried ink effect costs nothing; a looping one drains batteries.
Don't put two runtimes (Paper + shaders) on one page for one heading.
Don't feather every edge or draw a uniform halo. An even ring around each glyph reads as a sticker, not ink; feathering belongs to a few edges.
Don't use pure black text ink. Pools need a darker value to show, and sumi is grey-black anyway.
Don't push bleed until the fibers read as fur. Check the dried frame at your real font size.

About this skill

Automatic

Frontend / UI pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/30/2026

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LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

kwakseongjae / oh-my-design

.claude/skills/omd-cjk-type-shader/SKILL.md

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License MITImported 10/30/2026Version v1Bundled 1 file · scripts

Illustrative Want to see what kwakseongjae/omd:cjk-type-shader 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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omd-cjk-type-shader — 먹 번짐: CJK display type as sumi ink on hanji

1. When to use / when not

2. The effect and its two parameters

3. The contract that keeps text text

4. CJK typesetting (applies before the shader)

5. Library, license, budget

6. Fallback and reduced motion

7. Verify on a real GPU

8. Next.js / React port

9. Attribution

Don't

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