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Core scenarios (what this skill handles) · superdesigndev | DecimalAI

Design & Mediaby superdesigndev

Core scenarios (what this skill handles)

Design or redesign frontend UI on the Superdesign canvas.

Maintained

SkillScore

SkillScore74/100

Test results 74

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

35%74%of test cases passed +39 pts

Without skill8 of 23

With skill17 of 23

10 fixed7 passed either way5 still failing1 broke

Measured on gemini-3.6-flash8/27/2026v420 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen selecting context files to redesign an existing page in a large codebase under context payload limits, which file in `.superdesign/init/` provides the page component dependency tree?

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 superdesigndev-superdesign
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("superdesigndev-superdesign")
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 superdesigndev-superdesign

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/superdesigndev-superdesign/SKILL.md https://app.decimal.ai/s/superdesigndev-superdesign/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/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/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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("superdesigndev-superdesign")
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/superdesigndev-superdesign/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/superdesigndev-superdesign/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 superdesigndev-superdesign

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/superdesigndev-superdesign/SKILL.md https://app.decimal.ai/s/superdesigndev-superdesign/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/superdesigndev-superdesign/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/superdesigndev-superdesign/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/superdesigndev-superdesign.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: v4, tested 8/27/2026
Source: superdesigndev/superdesign-skill ↗
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

Superdesign helps you find design inspiration and generate or iterate design drafts on an infinite canvas, with multiple leading models available for different design tasks and side-by-side exploration. When a design needs a new visual asset, it can also provide image and video generation.

Core scenarios (what this skill handles)

Analyze a codebase for design work — superdesign init builds reusable UI context in .superdesign/init/; read INIT.md.
Design, reproduce, or improve UI — create pages, features, flows, and new products on the canvas; read SUPERDESIGN.md.
Choose and compare leading design models — run list-models, select a model suited to the task, or use different models to explore independent directions.
Create design systems and reusable components — establish visual foundations, extract patterns, and design connected multi-page experiences.
Design from a live website or reference URL — extract and apply its design language; read WEBSITE.md.
Create or export presentations — plan an editable slide outline, approve it in chat, generate a real presentation draft, make slide-safe edits, or reconstruct an editable PPTX when requested and supported; read PRESENTATION.md.
Create graphics — posters, covers, social posts, thumbnails, flyers, and ads; read GRAPHIC.md.
Generate supporting images or video — use native image generation when appropriate or choose from Superdesign's generation models; read ASSET_GENERATION.md.
Continue or directly correct existing work — resume saved targets through RESUME.md, or use design-with-your-model.md when direct authoring is the right path.

When continuing a draft, follow SUPERDESIGN.md ITERATION MODE ROUTING: replace refines the selected direction with version history; branch is only for alternatives the user wants to compare.

Step 0 — Environment preflight (BEFORE any CLI step)

Superdesign runs entirely through its CLI, so you must be able to execute shell commands. Confirm that capability first, before any CLI verification.

If you have no way to run shell commands in this environment (no terminal/execution tool at all), OR your very first bare npx --yes @superdesign/cli@latest preflight attempt fails because command execution itself is unavailable (the harness reports it cannot run commands / there is no shell) then STOP. Do NOT keep retrying or improvise workarounds. Tell the user once, and pick the message that matches where you are running:

Standard ChatGPT chat without Work Mode tools — this exact copy, because the Work tab is the fix:

Chat isn't supported by the Superdesign plugin. Please switch to the Work tab and paste this prompt in for the full experience.

Any other harness (a coding agent whose shell is unavailable or disabled) — do NOT send the ChatGPT copy; there is no Work tab to switch to. Say plainly that Superdesign drives its CLI over the shell, that this session cannot run shell commands, and that they can re-run it in a session with shell access or design in the web app at https://superdesign.dev.

Step 1 — Is there a codebase to analyze?

Two entry paths. Choose one with this cheap, deterministic check BEFORE any init or design work.

No meaningful codebase (empty workspace, scratch/sandbox dir, no frontend code) — treat the workspace as "no codebase" when ALL of these hold:

No .superdesign/init/ files already exist, AND
No dependency manifest with frontend deps (no package.json, or a package.json whose deps include no frontend framework/UI library — react, vue, svelte, angular, next, nuxt, astro, etc.), AND
No frontend source found (a quick scan for .tsx/.jsx/.vue/.svelte files, any .html/.css files such as a root index.html + style.css, or a src//app//components/ dir with UI files, turns up nothing).

→ SKIP repo init entirely. Do NOT "analyze" an empty sandbox, and do NOT ask the user to point you at a repo they don't have. Instead, gather design context conversationally FIRST: ask what they want to build, the target audience/platform, style/brand preferences, and any reference designs or inspirations. Then design from that conversation via the BRAND NEW PROJECT path in SUPERDESIGN.md.

Real codebase present (any frontend code, or an existing .superdesign/init/) — repo init must have completed at least once before designing. Reuse a valid initialized target through Step 1.5; run the full analysis only when init is incomplete or warm state cannot be used.

Exception — standalone extraction: if the task is ONLY to extract a site's design DNA or set/refresh design-system.md from a URL (extract-website → design-system.md, no design generation; read WEBSITE.md for the recipes), run it WITHOUT repo init — extracting an external site's style doesn't require analyzing the user's codebase. Init is still required before generating designs FOR the existing codebase's UI (reproducing/redesigning an existing page).

Exception — graphics: posters/marketing assets (scenario 7) skip init even in a real codebase — the brief carries the style, and most of init's output (components, layouts, routes, pages) has no bearing on a fixed-canvas artwork. The graphic brief round asks whether the artwork should be on-brand with this repo's product (GRAPHIC.md Step 1); only an on-brand "yes" pulls in the design-system/brand context — running init first only if that context doesn't already exist.

Exception — presentations: slide decks (scenario 6) skip UI repo init by default because routes, components, and page dependency trees do not help deck creation. Follow PRESENTATION.md. Use narrowly relevant product documents, design-system context, and Brand Assets when the user wants an on-brand product presentation; run init only when matching the codebase brand is required and no usable brand/theme context exists yet.

Exception — image/video generation: a standalone generated asset (scenario 8) skips init even in a real codebase. Read ASSET_GENERATION.md and gather only the project, brand, reference, or destination context the requested asset actually needs. If the asset is one step inside a broader UI, presentation, or graphic-design task, follow that task's normal init/brief path and use asset generation only at the point where a new visual is needed.

Step 1.5 — Resume before rediscovery (real-codebase UI path)

Before reading init artifacts or source files, check .superdesign/resume.json for the requested route/feature. A matching target defaults to RESUME.md regardless of whether the user says "continue", "change", "redesign", or gives only a direct instruction such as "make the dashboard darker". Intent phrasing never decides warm versus cold routing.

Apply the saved target's trust/structural checks FIRST. A safe, structurally valid target reuses the saved project, draft, component records, design direction, and exact --context-file bundle: matching hashes go to warm resume, while mismatches go to incremental refresh without cold rediscovery. If a request needs code understanding not captured by the active draft metadata, use RESUME.md's targeted context-expansion rule; do not rerun full discovery merely to understand that request.

Use the cold path only when no saved entry covers the requested target, the user explicitly asks to start over from fresh ground truth, the state is unsafe/structurally invalid, or targeted repair determines it is stale beyond incremental repair. A new agent session, different wording, or a fingerprint mismatch alone never forces cold routing.

Init: Repo Analysis (real-codebase path)

When a real codebase is present (per Step 1, and no Step 1 exception applies) and init is NOT complete, you MUST automatically:

Create the .superdesign/init/ directory
Read INIT.md
Follow its instructions to analyze the repo and write context files

Init-complete test (one decidable rule, used everywhere): init is complete only if all six named files below exist AND are non-empty. A directory that is missing any of them, or holds an empty one (e.g. an interrupted init), is NOT complete — rerun the full init, which regenerates all six; overwriting existing files is expected and fine.

Do NOT ask the user to do this manually — just do it.

Init Files (cold/stale context path)

For a first design of a target, or after RESUME.md determines that saved context is stale/unusable, read all six files before collecting the target context:

components.md — shared UI primitives with full source code
layouts.md — shared layout components (nav, sidebar, header, footer)
routes.md — page/route mapping
theme.md — design tokens, CSS variables, Tailwind config
pages.md — page component dependency trees (which files each page needs)
extractable-components.md — components that can be extracted as reusable DraftComponents

On a valid warm resume, only check that all six files exist and are non-empty — do NOT read their contents. Reuse the target's saved context bundle; read only narrowly selected source files when RESUME.md explicitly triggers targeted context expansion.

When cold-designing an existing page: First check pages.md for the page's dependency tree — the candidate set of --context-file files. Pass them under the PAYLOAD BUDGET rules in SUPERDESIGN.md so the payload does not 400. Then also add the globals.css tokens, tailwind.config, and design-system.md. Persist the final selection per RESUME.md.

Superdesign CLI (MUST use before any command)

IMPORTANT: Run the CLI on demand with npx --yes @superdesign/cli@latest. Start every session with the bare command — it IS the preflight.

Preflight once:
```
npx --yes @superdesign/cli@latest
```
The bare command verifies everything in one shot: that the CLI runs at all, an auth: status line (authenticated as team "…" vs not authenticated — run superdesign login), and a list of recent projects. On a valid warm resume, use the saved projectId/activeDraftId directly. Otherwise read the recent-project list when deciding whether to reuse an existing project or create-project; fetch-design-nodes --project-id <id> is the fallback for recovering draft ids when durable resume state is unavailable or rejected.
If the auth: line says not authenticated, run login NOW, before any real command:
```
npx --yes @superdesign/cli@latest login
```
Wait for login to complete successfully before proceeding.
Run the intended commands with the same npx --yes @superdesign/cli@latest prefix. A session can still expire mid-flow — handle a later auth/login error per the failure block below.

Never assume the user is already logged in — read the preflight's auth: line instead of guessing or probing with real commands.

When a command fails

Auth/login error (the CLI ran but rejected the session): run login (above), then retry the intended command ONCE. If login itself fails (headless/no-browser auth, expired flow, user declines), tell the user plainly and STOP — do not keep retrying or improvise.
extract-website fails or times out (it can take ~60–120s): retry ONCE. If it still fails, offer to continue WITHOUT the extraction (design from the conversation / existing design system) rather than blocking.
General rule: retry a failed command at most once. If create-design-draft or iterate-design-draft still fails, continue via design-with-your-model.md; otherwise report the failure and stop.

Command examples

Always use the full on-demand runner prefix, e.g.:

npx --yes @superdesign/cli@latest create-project --title "X"

Full invocations live at their use sites — the SOPs in SUPERDESIGN.md and the graphic steps in GRAPHIC.md; flag sets come from <command> --help, and the COMMAND CONTRACT in SUPERDESIGN.md covers the traps help leaves out.

The CLI defaults to an agent-optimized output (compact TOON plus help[] next-step hints); add --json only when you need the full machine-readable payload.

Surface the canvas URL

Every project/draft command's default output includes a canvas: link (the project canvas, https://superdesign.dev/teams/<teamId>/projects/<projectId>) and, for drafts, a preview: link (https://superdesign.dev/preview/draft/<draftId>). Read these from the command output — do NOT hand-construct them (the ids are server-generated).

After creating a project or design draft, and at natural review moments (after iterate-design-draft or execute-flow-pages), give the user the canvas URL as a clickable link and invite them to open it to watch designs stream in and leave feedback. Adding ?live=1 to the canvas URL opens the live view where drafts appear as they generate.

Browser Choice

create-project auto-opens the canvas in user's browser by default. Leave it on, and tell the user the canvas was opened (with the canvas URL as a clickable link). Only pass --no-open when there's no user-facing browser (CI, headless).

Images and local assets

Before generation, inventory only the images the user attached or the narrowly relevant local assets selected for this target. Follow SUPERDESIGN.md ASSET PURPOSE ROUTING: temporary screenshots/references go to canvas reference nodes; logos, fonts, and reusable identity imagery go to Brand Assets; final-content imagery stays project content. Pass returned node ids or Brand Asset keys with --reference-id so create, iterate, and flow generation receive the actual pixels. When any source component or requested design has a logo position, enforce SUPERDESIGN.md's Logo invariant: an available appropriate Brand Asset logo must visibly render there, including inside reusable components; never substitute initials, emoji, generic marks, invented SVGs, or text alone. For a pasted direct public image URL or a website reference, read WEBSITE.md and materialize only the selected visual through the same upload flow. Never bulk-upload the repository and never put local filesystem paths into draft HTML.

After generating: offer to go further

Always close with a short, warm follow-up that offers to go further (on every surface). Ask one question with 2 to 3 concrete options tailored to what you just made, not a generic list. For example: try a different hero image or key visual direction, try an alternate layout or composition, or generate a few more variations or asset ideas as surprises. Only generate after the user picks, since every generation spends credits.

(Graphics get a dedicated one-round visual self-review before this close — GRAPHIC.md Step 5. UI drafts are reviewed by the user on the canvas.)

How it works

Read SUPERDESIGN.md, then follow its instructions.

For an image or video generation request, read ASSET_GENERATION.md. Load the other scenario-specific references linked above when those scenarios apply.

When it activates

Automatically, when a task matches. Use whenever the user wants to design a page, feature, flow, or a brand-new product with no code yet; redesign or improve existing UI; faithfully reproduce current UI; explore visual variants; set or extract a design system (including borrowing a style from a live website URL); build reusable design components; design multi-page flows; or create static posters, flyers, cover art, or social/marketing graphics composed on canvas, even if th

About this skill

Automatic

Design & UX Review pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/26/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

superdesigndev / superdesign-skill

skills/superdesign/SKILL.md

View on GitHub ↗

License MITImported 8/26/2026Version v4Bundled 9 files

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

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

Core scenarios (what this skill handles)

Step 0 — Environment preflight (BEFORE any CLI step)

Step 1 — Is there a codebase to analyze?

Step 1.5 — Resume before rediscovery (real-codebase UI path)

Init: Repo Analysis (real-codebase path)

Init Files (cold/stale context path)

Superdesign CLI (MUST use before any command)

When a command fails

Command examples

Surface the canvas URL

Browser Choice

Images and local assets

After generating: offer to go further

How it works

When it activates

SkillSafety

Source

Related skills

More in the registry