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UIKit App Modernization Skill · superagents-lab | DecimalAI

Frontendby superagents-lab

UIKit App Modernization Skill

Modernizes UIKit apps for multi-window environments by replacing legacy shared-state APIs with context-appropriate modern alternatives. This includes references to mainScreen, interfaceOrientation, application and scene lifecycle, as well as safe area inset updates.

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

73%86%of test cases passed +14 pts

Without skill16 of 22

With skill19 of 22

6 fixed13 passed either way0 still failing3 broke

Measured on gemini-3.6-flash8/23/2026v120 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIn an Objective-C view subclass `CustomButton.m`, inside `- (void)layoutSubviews`, the code sets `self.layer.borderWidth = 1.0 / [UIScreen mainScreen].scale;`. Modernize this scale lookup.

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 superagents-lab-uikit-app-modernization
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("superagents-lab-uikit-app-modernization")
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 superagents-lab-uikit-app-modernization

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/superagents-lab-uikit-app-modernization/SKILL.md https://app.decimal.ai/s/superagents-lab-uikit-app-modernization/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/superagents-lab-uikit-app-modernization/SKILL.md

This gets SKILL.md only. The skill's 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/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/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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("superagents-lab-uikit-app-modernization")
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/superagents-lab-uikit-app-modernization/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/superagents-lab-uikit-app-modernization/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 4 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 superagents-lab-uikit-app-modernization

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/superagents-lab-uikit-app-modernization/SKILL.md https://app.decimal.ai/s/superagents-lab-uikit-app-modernization/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/superagents-lab-uikit-app-modernization/SKILL.md

This gets SKILL.md only. The skill's 4 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/superagents-lab-uikit-app-modernization/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/superagents-lab-uikit-app-modernization.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/23/2026
Source: superagents-lab/xcode27-skills ↗
License: Not recorded

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

UIKit App Modernization Skill

Purpose

Modernize UIKit apps to behave correctly on modern iOS by:

Eliminating references to legacy shared-state APIs
Migrating from application lifecycle to scene lifecycle
Supporting dynamic scene sizing and multi-window environments

Scope

This skill performs specific, targeted modernizations in both Swift and Objective-C codebases:

Replace legacy shared-state APIs with context-appropriate modern APIs
Migrate to scene-based lifecycle
Update apps to support a resizable user interface by removing usage of:
- main screen (UIScreen.mainScreen, UIScreen.main)
- interface orientation (interfaceOrientation)
- assumptions of symmetric safe areas (safeAreaLayoutGuide, safeAreaInsets)

Core Principles

Closest to consumer — Prefer information nearest the point of use (e.g., view's trait collection over window's).
Always apply a replacement when the target API is present. A TODO alone is a failure. An empty diff for a file containing the target API is also a failure. If the file contains the target deprecated API and a concrete replacement is feasible under any pattern in the active task's reference file, apply it. Only skip when the target API appears exclusively inside dead code (#if 0/#endif). When uncertain between two valid replacements, pick the one that best fits the user's request rather than producing an empty diff. Never silently skip a file: if you are unwilling to apply a change, talk to the user about possible options — never produce no output for it. Do not get stuck weighing edge cases on simple files; when the substitution is obvious, apply it and move on.
TODOs must be actionable. Every TODO you do leave must state (a) why the change is needed, (b) what the correct replacement would look like, and (c) any lifecycle or threading concerns. Place the TODO on its own line above the unchanged code — never inline. A vague TODO ("fix this later") is worse than no TODO; it consumes review attention without telling the next reader anything they couldn't infer.
Don't add a redundant TODO when an existing annotation already covers the migration. If the call site already has a #pragma clang diagnostic ignored paired with a bug-report reference, an existing // TODO, or a deprecation comment that points at the migration, do not add another one. Only add a new TODO when it provides additional migration guidance not present in the existing annotation.
Ask the user before making a risky code change; fall back to a TODO only when interactive guidance is unavailable. When a replacement risks breaking callers or changing observable behavior (e.g., changing a method signature in a header that other modules import; substituting width > height for orientation when left-vs-right matters), the first move is to ask the user how to proceed. Only when the skill is running non-interactively, or when the user explicitly declines to provide guidance, drop a TODO and move on. This does not apply to standard, drop-in safe replacements specified by the active task's reference file — those must be applied per Core Principle 2.
Honor explicit user instructions; otherwise apply the defaults from the task reference file. When the user asks for a specific approach — a particular attribute, parameter name, parameter position, trait source, or fallback behavior — use that exactly. Don't silently substitute what you consider the modern equivalent. When the user is general ("modernize this app", "fix UIScreen.main usages"), apply the defaults from the active task's reference file.
Never replace dynamic values with literals — Always keep replacements dynamic.
Preserve control flow — Prefer drop-in replacements that maintain the original code structure. Only add guard/early-return patterns when a direct substitution does not work. When editing code around control flow (if/else, switch/case/default, do/catch), verify that the branching structure is preserved after your edit. Never remove a branch (} else {, default:, catch) unless the user explicitly asks for it. A diff that collapses an if/else into sequential execution is a critical bug — both branches will execute unconditionally.
Stay in scope — no opportunistic cleanup. Only modify lines containing the target deprecated API for the active task. Do NOT also fix other deprecation that happens to live nearby. Do NOT trim trailing whitespace, reformat blank lines, or "clean up" surrounding formatting. Even if you see an obvious modernization opportunity on an adjacent line, leave it alone — each task is independent and out-of-scope edits convert a successful in-scope change into a warning.
Extract repeated expressions — When the same replacement value is used multiple times in a scope, extract it into a named local variable.
Never walk global scene/window state — Never use UIApplication.shared, UIDevice.current, UIScreen.main, or other shared objects as a replacement. If no local object is available, modify the method to accept a new parameter and deprecate the old method.
Complete patterns — atomic, never partial — Every multi-part pattern requires ALL parts applied together as a single atomic unit. Deprecate-and-forward requires deprecation + new overload + forwarding — never just an inline replacement when the pattern calls for method extraction. When the active task requires both an API replacement AND a reactive update (e.g., trait change observation), these form a single atomic change — never apply one without the other. Downgrading the deprecate-and-forward pattern to an inline reference to a shared object is an error — it silently breaks the migration story by removing the deprecated bridge that callers rely on to find the new API. If you cannot complete all four parts (new overload with the appropriate parameter name/type/position, old method delegates with shared state (e.g. UITraitCollection.current, UIScreen.main), old method marked deprecated with the appropriate attribute, deprecated wrapper kept in place), do not apply a partial change — either complete the full pattern or skip with an explicit reason.
Never remove the old method when adding a new overload. When applying deprecate-and-forward, the old method must remain in the file as the deprecated wrapper that forwards to the new overload via .current. Deleting the old method (even if it appears unused in the diff) removes the deprecation signal from the codebase and silently drops the migration bridge. This applies to ObjC methods, Swift methods, Swift initializers, computed properties, and protocol-extension methods. If you find yourself removing a method as part of adding a new overload, STOP — you should be keeping it with a deprecation attribute, not deleting it.
Preserve unrelated guards and fallbacks. When removing a UIScreen.mainScreen reference, change ONLY that reference. Do not simultaneously delete respondsToSelector: checks, nil-screen guards, if (screen != nil) defenses, version checks (#available, @available), or any other defensive logic that wraps the call site — unless the user explicitly asks for it. Each guard exists for an independent reason (selector availability across SDK versions, nil-window safety, feature flags); the modernization touches only the screen-derived value, not the surrounding control flow.
Apply the deprecation at the lowest method that touches the deprecated API. When several callers funnel into one helper that actually reads the deprecated shared state, put the deprecate-and-forward on the helper, not on every public caller. Forcing every public caller to grow a traitCollection: parameter when the helper is the only site that needs it produces over-broad churn and a wider blast radius than the migration requires. Conversely, when the deprecated state is read directly inside each public caller (no helper), the deprecation belongs on the public callers — there is nothing lower to deprecate. Rule of thumb: identify which method contains the line you would otherwise need to change; deprecate that method. The deprecation chain should grow only as wide as the actual surface that touches the deprecated API.
Off-target replacement guard. Before editing any line, verify two things: (a) the line contains the target deprecated API for the active task, and (b) you're editing the deprecation the user asked about — not a nearby line that "looks similar."

Workflow

Phase 0: Fast Path for Simple Cases

Before reaching for the decision tree, check if the occurrence matches the simple case. A large fraction of UIScreen.main/UIScreen.mainScreen occurrences are simple substitutions inside a UIView/UIViewController instance method where the value is consumed fresh. These cases need no analysis — just substitute and move on:

Original	Replacement
`UIScreen.main.scale` (Swift) inside a UIView/UIViewController instance method, used inline (not stored)	`self.traitCollection.displayScale`
`[UIScreen mainScreen].scale` (ObjC) inside a UIView/UIViewController instance method, used inline (not stored)	`self.traitCollection.displayScale`
`UIScreen.main.scale` inside `layoutSubviews`, `drawRect:`, `updateConstraints`, or `viewIsAppearing:`	`self.traitCollection.displayScale` (no registration needed — UIKit auto-calls these on trait change)

Do not over-think simple substitutions. If the enclosing class is UIView/UIViewController and the value isn't being assigned to an ivar, layer property, constraint, or stored image, just substitute. Empty diffs on simple files are the most common mistake — apply the substitution and move on. Reach for the decision tree only when the simple case doesn't fit (non-view class, cached value, class/static method, special user instructions).

Phase 1: Detection

Identify patterns to modernize using each relevant task file's detection patterns. Run detection for every task in the Task Registry that applies to this codebase, not just one — see Task Registry below.

Phase 2: Analysis

For each occurrence, read surrounding context to understand:

Class hierarchy (UIView/UIViewController subclass vs plain NSObject vs non-view class)
Method type (instance, static, free function, cached dispatch_once helper)
Lifecycle phase (init, viewDidLoad, viewWillAppear, layoutSubviews)
Code intent (layout, rendering, display scale, full screen dimensions)

The active task's reference file may add task-specific bullets to this list.

Use subagents to identify code that needs to be updated to keep your context window small.

Phase 3: Decision & Validation

Condition	Action
Safe 1:1 replacement exists	Apply it. No added commentary (no `// TODO: FIXME`, no `// TODO`, no `// FIXME` — just the replacement). Use the replacement specified by the active task's reference file.
Multiple valid approaches or code relocation >10 lines	Ask the user.
No safe replacement possible (extremely rare)	Add todo with an explicit task outlined for the user. Never produce a silent empty diff. Re-check every pattern with a subagent before concluding nothing applies.

Use subagents to validate against the active task's Post-file Checklist before any code change.

Phase 3b: File Processing Completeness

Process EVERY file that contains the target deprecated API. Do not stop early, skip files, or silently drop files from the work queue. A file that was identified in Phase 1 but produces no diff and no skip explanation is a processing failure.

Explicit file tracking: At the start of processing, write out the complete list of files to be modified using available task / todo tools or a markdown file. As you process each file, mark it done. Before finishing, compare this list against your output — any file without a diff or an explicit skip reason is a failure that must be addressed before completing.

Context size: If you are concerned about context size, use subagents to process individual files or tasks.

Silent-drop prevention: Before finishing, use subagents to compare the list of files you were given against the list of files you produced output for. If any file is missing from your output, go back and process it. Common causes of silent drops:

File size: Large files (1000+ lines) are not exempt. Process them with the same approach.
Complexity: Files with preprocessor macros, complex class hierarchies, or unusual code patterns still need changes.
Project grouping: Do not skip all files from a specific project or directory. If you notice you've dropped multiple files from the same project, that indicates a systematic issue — investigate and fix.
Ambiguity: If you're unsure how to fix a file, ask the user — do not silently produce an empty diff.

Large or complex files: Files with heavy preprocessor usage (#if/#ifdef nesting), 1000+ lines, or less common patterns (C++ interop, dispatch_once caching, deeply nested macros) are not exempt from processing. If the target API appears in such a file, apply the same decision tree. If the file is too large to edit in one pass, process the deprecated API usages one at a time. Use subagents if helpful. If you genuinely cannot determine a safe replacement due to macro expansion or preprocessor complexity, ask the user — never silently skip it.

Batch processing discipline: When processing a list of files, do NOT attempt to analyze all files first and then produce all diffs at once. Instead, process files one at a time or in small batches (3–5 files): read context, decide, produce the diff, then move to the next batch. This prevents the tail end of the file list from being silently dropped due to output limits or context exhaustion. If you notice you have produced output for fewer files than you were given, STOP and process the remaining files before finishing.

If you find empty diffs for files that should have straightforward replacements, go back and process them — straightforward files are fast to handle and should never be dropped.

Phase 4: Implementation

Apply the active task's implementation gates, rules, and post-file checklist from its reference file. The pattern-specific decision tree, gate questions, and validation rules live alongside the patterns they govern in each task file. Use subagents for verification.

Phase 5: Final Verification

File coverage audit: Use subagents to compare the list of files you were given (or detected in Phase 1) against the files you actually produced diffs for. Every input file must have a non-empty diff. If any file is missing changes, go back and process it now.

The active task's reference file may add task-specific verification steps.

Task Registry

Apply every task in this registry to the codebase unless the developer's request explicitly scopes to a subset. Each task is independent and has its own detection patterns, decision tree, and verification rules in its reference file. Run them in order from top to bottom.

Task	File	Description
UIScreen.main modernization	uiscreen-task.md	Replace `UIScreen.main` with context-appropriate APIs
userInterfaceOrientation modernization	orientation-task.md	Replace layout-related orientation checks with size classes or window bounds
Scene lifecycle migration	scene-lifecycle-task.md	Migrate AppDelegate to SceneDelegate
Safe Area Insets	safe-area-task.md	Replace hard coded values for insets with safe area references and ensure that existing references work with asymetric safe areas

About this skill

Automatic

Frontend / UI pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

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

superagents-lab / xcode27-skills

uikit-app-modernization/SKILL.md

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Imported 8/3/2026Version v1Bundled 4 files

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UIKit App Modernization Skill

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Phase 0: Fast Path for Simple Cases

Phase 1: Detection

Phase 2: Analysis

Phase 3: Decision & Validation

Phase 3b: File Processing Completeness

Phase 4: Implementation

Phase 5: Final Verification

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