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Design — spatial composition · sickn33 | DecimalAI

Design & Mediaby sickn33

Design — spatial composition

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

82%86%of test cases passed +5 pts

Without skill18 of 22

With skill19 of 22

2 fixed17 passed either way2 still failing1 broke

Measured on gemini-3.6-flash8/9/2026v1 · latest is v220 comparable

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen performing visual critique on rendered screenshots of a new web UI, who should evaluate the rendered output for layout flaws and edge collisions?

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 sickn33-design-spatial
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("sickn33-design-spatial")
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 sickn33-design-spatial

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/sickn33-design-spatial/SKILL.md https://app.decimal.ai/s/sickn33-design-spatial/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/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/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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("sickn33-design-spatial")
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/sickn33-design-spatial/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sickn33-design-spatial/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 sickn33-design-spatial

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/sickn33-design-spatial/SKILL.md https://app.decimal.ai/s/sickn33-design-spatial/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sickn33-design-spatial/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/sickn33-design-spatial/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sickn33-design-spatial.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: v2 (v1 tested 8/9/2026)
Source: sickn33/antigravity-awesome-skills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Design — spatial composition

When to Use

Use this skill when you need design — spatial composition.

A model cannot trust its own UI output. Everything else follows from two failures.

1. It can't see what it made

UI is generated as a token stream, never as pixels — so the model cannot perceive collisions, overlap, imbalance, or broken spacing. It will write a headline that runs into the hero image and have no idea.

Render it and judge the image, not the code. Serve with any static server (e.g. python3 -m http.server or npx serve) and screenshot headless via Playwright. Screenshot at a few widths.

Critique with fresh eyes — not your own. Grading your own output rationalizes it; the builder looks at its overlapping headline and calls it fine (this is exactly how a real collision shipped in testing). Use a separate judge — a subagent that did not write the page — and tell it to hunt for what's wrong: collisions, edge tangents, ragged alignment, lopsided weight, no clear focal point, breaks at some width. Fix, re-render, re-judge.

2. Its first idea is the average

Whatever it produces first is the mean of its training data — and there is more than one mean:

the generic-AI mean: Inter, purple-on-white gradients, centered single column, three equal cards;
the designer-trend mean: oversized condensed caps, dark-mode + grain, monospace "vibes" microtext, sticker badges.

Landing on the second isn't taste — it's a more flattering average, which is why it slips past. Treat your first instinct as the mean and deviate deliberately — toward this product's specific world (use design-thinking's domain / color-world / signature as the direction), not toward another trend. If the result could be any startup, you shipped the mean.

3. So don't prescribe a style

Any fixed rule — a 12-col grid, an 8-point scale, "mono = data" — becomes next cycle's mean, and a blind model executes it into collisions anyway. Prescribe the process, not the look: see it with fresh eyes, and push off the average toward the domain. Taste supplies the direction (design-thinking / design-philosophy); this skill only insists you look and don't ship the mean.

For iterative spatial tuning, a local page with live controls (sliders, pickers, drag handles) beats one-shot critique.

4. NEVER ship horizontal overflow — THE mandatory gate, no exceptions

BLOCKING GATE. You may not call any web UI "done", "working", "fixed", or "looks good" until you have run the scrollWidth check below at a narrow width THIS turn and seen 0. Not "I added overflow-x:clip so it's fine." Not "it looked fine at my width." MEASURE. Narrow. Every time. If you didn't measure, it isn't done — say "haven't checked overflow yet" instead of claiming done.

A side-to-side scrollbar that doesn't match the content is the single most common and most embarrassing layout failure, and it ships over and over because the dev viewport is wide enough to hide it — the overflow only appears once the window is narrower than some element. It is invisible at desktop width, so the §1 render-critique loop will NOT catch it unless you screenshot narrow. Separate, explicit, non-negotiable gate.

It recurs because layouts GROW after they were last checked. Every time you add a nav tab, a toolbar button, a header control, a chip, a wider equation/<pre>, or any new item to a flex/inline row, you have invalidated the last overflow check — the row that fit yesterday now pushes past the edge between ~720–1200px while your 1440px dev window shows nothing wrong. (Real ship, 2026-06, TWICE: a progress-bar edge label overflowed 23px; then a flex-wrap:nowrap header that grew 4 tabs scrolled the whole page 309px across 720–1200px — both invisible at dev width, both caught only by measuring narrow.) So: any change that adds an element to a horizontal row re-arms this gate. Re-measure.

Default defenses to apply up front (so the gate passes by construction):

Header / nav / toolbar rows: flex-wrap: wrap, never nowrap. A growing single-row flex is the #1 source of this bug. Wrapping is a no-op when it fits and saves you when it doesn't.
Use clipping only for intentional decorative overflow after checking focus rings, menus and offscreen content; do not apply it globally to conceal a layout defect.

The check — run before calling ANY page done: document.documentElement.scrollWidth - document.documentElement.clientWidth must equal 0, tested at your dev width AND resized narrow (≤1024px, and a phone width ~390px). If > 0, find the offender:

document.querySelectorAll('*').forEach(el=>{const r=el.getBoundingClientRect();
  if(r.right>innerWidth+1||r.left<-1) console.log(Math.round(r.right), el);});

Safety net: overflow-x: clip on body (prefer clip over hidden — it clips without creating a scroll container, so it won't break position:sticky/anchored layouts). But a net is not a fix — find and kill the root cause:

position:absolute + white-space:nowrap anchored at an edge (left:100%, right:0): a centered nowrap label on the right edge juts past the viewport. (Real ship, 2026-06: a progress bar's "300 · learned model" milestone label at left:100% with translateX(-50%) overflowed 23px → phantom horizontal scroll at sub-1180px widths.) Anchor edge labels inward — right end right:0; transform:none, left end left:0; transform:none.
100vw — includes the scrollbar width (~15px), so on any vertically-scrolling page it guarantees ~15px of horizontal overflow. Use 100%.
flex / grid children without min-width:0 — they refuse to shrink below their content and blow out the track (a long title in a flex card, a <pre> in a grid cell). Add min-width:0.
long unbreakable strings (URLs, hashes, tokens): overflow-wrap:anywhere or word-break:break-word.
fixed pixel widths wider than the viewport; large negative margins; oversized position:absolute elements.

The generalization: anything pinned to an edge or sized in viewport units is a horizontal-overflow suspect — test narrow, measure scrollWidth, clip the body as backstop, and anchor edge-pinned content inward.

5. Lay out in TASK order — minimize transition cost

Before placing elements, walk the user's actual step sequence for completing the page's action, then arrange elements in that same perceptual/view order. The layout should read like the task: orient → work → confirm. Any mouse travel or scrolling that serves no practical purpose is a defect.

Orient at top: controls/options up top are good — they tell the user what the page is for and what it can do before they commit to reading it.
Confirm where the work ENDS: if the task is "review a long list, then act" (approve, flag, submit, save), the action buttons must ALSO exist at the bottom — where the user's eyes and cursor are when they finish. The original failure: a delete-review page with confirm buttons only in the top toolbar — after scrolling through 120 images, the user had to scroll all the way back up to click "flag the rest." Duplicate the action bar at the bottom (or make the toolbar sticky); both are one line of code, the scroll-back is paid per page.
The heuristic: save the user transit time. Every interaction has a path: where the eyes/cursor are when a step ends vs where the next step's control is. Sum those distances; shrink the big ones. Fitts's law for the page as a whole, not just one button.
Check it in the render-and-critique loop (§1): ask the judge "trace the task: where is the user when they finish each step, and how far is the next control?" — a layout can be aligned, balanced, and still force a round trip.

6. Balance is measurable — don't eyeball it (or trust a VLM's eye)

§1 says render and have fresh eyes critique it. That qualitative pass catches collisions and ragged alignment, but a model has no reliable sense of visual balance — ask a VLM "is this centered / balanced?" and it confabulates a verdict. The fix is to stop asking opinions and measure a number, then keep that number honest with an independent check. Use both: §1's fresh-eyes critique AND the hard number below. (This pairs a live in-browser box model + auto-balancer with an offline pixel-oracle that re-measures the rendered screenshot — see the layout-audit.js companion script in this skill.)

The principle. Visual balance is the center of mass of visual weight. It's arithmetic, not taste — so compute it.

Optical center, not geometric. Target x = 0.50, y ≈ 0.46 — slightly high, because a centroid at literal 50% reads as sagging.

Visual weight estimate = area × guessed ink-density. These coefficients and optical-center tolerances are exploratory heuristics, not validated perceptual laws. Same-size ≠ same-weight: a solid-black heading is heavy; a grey/ASCII/light image reads far lighter than its area; body text is sparse. Calibrated starting multipliers (from asym.html, re-tune per project — these were hand-guesses until corrected against the pixel oracle):

const DENS = {portrait:0.34, h1:0.82, kicker:0.42, lead:0.22, body:0.16, meta:0.5};

Centroid. Per axis, centroid = Σ(wᵢ·posᵢ) / Σwᵢ; balanced ⇔ the centroid sits on the optical center. To FIX imbalance, think see-saw: what counts is the moment = weight × distance-from-axis, so a heavy element near the edge is counterweighted by (a) an opposing weight, (b) a bigger element on the other side, (c) pulling the heavy element inward (shorter lever arm), or (d) shrinking it. That's exactly the auto-balancer's escalation order in asym.html — grow the opposing heading first (cheapest), then add weight, then pull the heavy element in, then shrink it (last resort).

Two models — and why you need the independent one:

Cheap box model (live tuning): put each element's weight at its bounding-box center. Instant, fine for dragging sliders. BUT it has a systematic bug — left-aligned text's ink sits left of its box, so the box model misplaces the weight. A metric that shares the layout's own assumptions is circular; it once reported "balanced" at a pixel-measured 0.93 lopsided.
Ground-truth pixel oracle (analyze.py): rasterize the rendered page (Playwright screenshot or html2canvas) and take the centroid of actual non-paper pixels, weighting each pixel by its distance from the background color. It knows nothing about the layout's intent — it just counts ink. When the box model and the pixels disagree, the pixels win. (asym.html closes the loop: it regresses the box-vs-pixel discrepancy and offers a trust dial α to blend toward the oracle.)
Acceptance criterion (measurable): |centroid_x − 0.50| < 0.03 and |centroid_y − 0.46| < 0.04, plus low left/right and top/bottom imbalance (|w_left − w_right| / total).

The verification gate (lighter than §4's, same spirit). Before calling a balance-critical layout "balanced", do NOT assert it from the code or a VLM opinion — screenshot the rendered page, compute the ink-centroid offset from optical center, and report the actual number. This is the design-skill application of verify-outputs-rule: look at the real artifact, and make the validating check (pixels) independent of the thing you tuned (the layout). It's the quantitative complement to §1's qualitative critique.

7. The layout audit — metrics that MEDIATE the eye, never replace it

§6 covers balance; this generalizes it to a full deterministic sweep, and fixes the failure mode that matters most: the model reads a metric/JSON and never looks at the screenshot, so it can't apply the common sense that catches the metric being wrong.

scripts/layout-audit.js is a dependency-free pass you run via Playwright MCP browser_evaluate on a rendered page. It measures six things deterministically — all geometry, color, and pixels, no "does this look right?":

check	how (deterministic)	tier
collision	content-rect intersection ≥12%	gate
contrast	WCAG luminance ratio of text vs effective bg (<4.5, large <3)	gate
tap	interactive targets <44×44 CSS px (conservative project heuristic, not a WCAG verdict)	review
overflow	`scrollWidth − clientWidth` (the §4 gate)	gate
alignment	left-edge clusters → near-misses 1–7px off the shared line	signal
spacing	gap CoV among a container's children	signal
balance	ink-density-weighted centroid vs optical center (§6)	signal

What makes it mediate rather than replace: it doesn't just return JSON — it draws every finding as an SVG overlay onto the page, so the next browser_take_screenshot is an annotated screenshot. The number tells you WHERE to look; you then look and decide. This is mandatory, not optional:

browser_evaluate({ function: "() => { <paste scripts/layout-audit.js> ; return __audit({}); }" })
browser_take_screenshot()      // ← the overlay is now on the page. VIEW IT. Reason over it.

Pass {align:'.card .title,.card .price', space:'.feature-list'} to scope the two selector-dependent checks; pass {contentSelector:'…'} for non-semantic layouts where collision needs help finding the blocks.

These are HEURISTICS, not laws — and they split into two kinds you must not conflate:

GATES = correctness (overflow, contrast, tap). These measure accessibility/ usability facts, not taste. Failing one is a real defect. Safe to block on. (Collision is a near-gate: usually a real bug, but can be intentional — so eye-confirm, don't auto-fail.)
SIGNALS = convention (balance, alignment, spacing rhythm). These measure how closely the layout matches a symmetric, regular, gridded aesthetic — which is exactly the generic mean §2 tells you to push away from. Optimizing a layout to maximize these scores makes it blander. An off-center balance, a deliberate misalignment, an uneven rhythm are core creative tools and frequently the best thing on the page. Treat signals as "worth a look," never as defects to fix.

The discipline (the whole point — bias hard toward this):

Never accept a metric you have not looked at. A flag is a pointer to look, not a verdict. Reading collisions: 1 and acting without viewing the annotated shot is the exact failure this section exists to kill.
Use signals to catch ACCIDENTS, never to enforce convention. A 7px alignment drift you didn't mean, a phantom scrollbar, a 1.9:1 caption — catch those. But the same balance/alignment/spacing signal fires on deliberate asymmetry, intentional overlap, and expressive rhythm. When the metric and the interesting choice conflict, the interesting choice usually wins. Do not "fix" a signal toward symmetry/evenness unless the eye judges the deviation actually worse. A model that maximizes these scores designs the mean.
Overrule flags the eye judges intentional. Brutalist headline overlap, avatar on a banner, asymmetric hero — the metric flags them; common sense overrules. Proven live in the worked example: obeying the collision check on the brutalist mock removes the overlap and the design goes flat.
Gates are necessary, not sufficient. gates_pass:true (overflow/contrast/tap all 0) clears the deterministic floor — it does not mean the layout is good. A bland centered template passes every gate and is still the mean. After gates pass, the real judgment (§1 fresh-eyes critique, taste, brand fit) still has to happen.

The proof, made concrete: build a page that runs all six algorithms live on a few realistic mock sites in different styles, each with a toggle between the layout as designed and the version obeying the metric, plus on-render overlays. It shows both halves: obeying a gate fixes a real bug (low-contrast CTA, sub-44 tap target, overlapping cards), while obeying a signal makes it worse (an asymmetric editorial hero is the more interesting layout; "correcting" a deliberate brutalist overlap flattens it). That explorable is where layout-audit.js was distilled from.

8. Optical craft — perception beats geometry

The audit's alignment check (§7) measures geometric edges. The eye doesn't read geometry, it reads perception — so a few cases need a manual nudge the metric can't make. These are eye-judgments, not gates. (From the Web Interface Guidelines, vercel-labs/web-interface-guidelines @ 4e799d4.)

Optical alignment — nudge ±1–2px when it looks off though it measures centered. A play-triangle in a round button must shift right of geometric center to look centered (its visual mass is left-biased). Glyphs, arrows, and asymmetric icons often need the same. Text vertically centered by box metrics frequently sits a hair low — lift it. Geometry is the starting point; the eye is the judge.
Balance icon/text lockups. When an icon sits beside text, match their visual weight — adjust the icon's stroke, size, spacing, or color so neither overpowers. A thin-stroke icon next to medium-weight text looks weak; thicken its stroke (or size it up slightly) so they read as one lockup. Optical size, not equal pixel size, is the target.
This is the same principle as the §6/§7 debias: the number gets you close; the eye makes the final 1px call. Don't let a geometric alignment metric prevent an optical correction.

Limitations

WCAG 2.2 AA target minimum is 24×24 CSS px with spacing and other exceptions; 44×44 is a stricter target, not a universal failure threshold. The script does not implement exceptions or full contrast compositing (images, gradients, transparent ancestors and modern color syntax). gates_pass only summarizes this partial heuristic; it cannot certify accessibility.
analyze.py and asym.html mentioned above are not bundled. Pixel centroid is background/viewport-dependent and cannot establish visual quality by itself. Verify intentional scroll regions and keyboard/zoom behavior in addition to root scrollWidth.
Use this skill only when the task clearly matches its upstream source and local project context.
Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

About this skill

Automatic

Design & UX Review pack

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 11/8/2026

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IntentSafe

LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

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sickn33 / antigravity-awesome-skills

skills/design-spatial/SKILL.md

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License MITImported 11/8/2026Version v2Bundled 1 file · scripts

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Design — spatial composition

When to Use

1. It can't see what it made

2. Its first idea is the average

3. So don't prescribe a style

4. NEVER ship horizontal overflow — THE mandatory gate, no exceptions

5. Lay out in TASK order — minimize transition cost

6. Balance is measurable — don't eyeball it (or trust a VLM's eye)

7. The layout audit — metrics that MEDIATE the eye, never replace it

8. Optical craft — perception beats geometry

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