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Deferring State Reads — Move Hot Reads from Composition to Draw · skydoves | DecimalAI

Frontendby skydoves

Deferring State Reads — Move Hot Reads from Composition to Draw

Use this skill to push frequently-changing Jetpack Compose state reads (scroll position, animation values, drag offsets) out of the Composition phase and down into Layout or Draw using lambda-based modifiers like Modifier.offset { }, Modifier.layout { }, Modifier.graphicsLayer { }, Modifier.drawBehind { }, and Modifier.drawWithCache { }. Covers the three-phase model (Composition, Layout, Draw), why a state read at phase N invalidates phase N and every phase below, the modifier-phase cheat sheet,

Never made a case worseMaintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

82%91%of test cases passed +9 pts

Without skill18 of 22

With skill20 of 22

2 fixed18 passed either way2 still failing0 broke

Measured on gemini-3.6-flash8/19/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI need to dynamically adjust the start inset of a composable based on an active gesture drag delta. `Modifier.padding` does not provide a lambda overload accepting `() -> Dp`. How can I defer this dynamic layout inset without causing composition invalidations?

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 skydoves-deferring-state-reads
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("skydoves-deferring-state-reads")
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 skydoves-deferring-state-reads

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/skydoves-deferring-state-reads/SKILL.md https://app.decimal.ai/s/skydoves-deferring-state-reads/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/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/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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("skydoves-deferring-state-reads")
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/skydoves-deferring-state-reads/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/skydoves-deferring-state-reads/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 skydoves-deferring-state-reads

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/skydoves-deferring-state-reads/SKILL.md https://app.decimal.ai/s/skydoves-deferring-state-reads/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/skydoves-deferring-state-reads/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/skydoves-deferring-state-reads/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/skydoves-deferring-state-reads.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/19/2026
Source: skydoves/compose-performance-skills ↗
License: Apache-2.0

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

Deferring State Reads — Move Hot Reads from Composition to Draw

Compose runs three phases per frame — Composition → Layout → Draw. A state read at phase N invalidates phase N and every phase below it. The single biggest perf win in any animation- or scroll-driven UI is moving a state read from Composition down to Layout or Draw via a lambda-based modifier. This skill teaches Claude how to spot the wrong-phase read and migrate it.

When to use this skill

An animation triggers full subtree recomposition on every frame (Modifier.alpha(progress.value), Modifier.offset(x.dp), Modifier.padding(state.dp) — padding has no lambda overload, so use Modifier.layout { ... } or Modifier.offset { IntOffset(...) } instead when the inset is animated).
A scroll position is read directly in a composable body (val y = scrollState.value) and the parent recomposes on every pixel of scroll.
The developer says "every frame", "scroll jank", "animation jank", "dropped frames", or reports the whole screen recomposing on drag.
A @TraceRecomposition log shows the parent's recomposition counter incrementing once per animation tick.
The developer is passing a hot value (animation progress, scroll offset, drag delta) as a Float parameter across composables.

When NOT to use this skill

The state changes once per user interaction (button click, dialog open) — the lambda-modifier rewrite buys nothing and adds noise.
The state read genuinely needs to drive Composition (show/hide a different composable, swap a different component tree). Lambda modifiers cannot decide which composable to emit.
The non-skippable parent is non-skippable for a different reason (unstable parameter). Diagnose with ../../stability/diagnosing-compose-stability/SKILL.md first.
The developer wants to filter many high-frequency inputs into one rare boolean — that is ../choosing-derivedstateof/SKILL.md.

Prerequisites

Familiarity with the three-phase model. If unsure which phase a read happens in, read references/three-phases.md before doing the migration.
Compose UI 1.7+ for rememberGraphicsLayer() and the modern Modifier.animateItem() shape. Lambda forms of Modifier.offset { }, Modifier.graphicsLayer { }, Modifier.drawBehind { }, and Modifier.drawWithCache { } exist on every supported Compose version.
Ability to confirm recomposition counts in Layout Inspector or via @TraceRecomposition (see ../debugging-recompositions/SKILL.md if it exists in this repo, otherwise the skydoves compose-stability-analyzer runtime).
A release build for any final measurement — debug builds run interpreted and lie about cost.

Workflow

1. Identify the state read site and the modifier consuming it. Look for state.value, by state, animatedFloat.value, scrollState.value, dragOffset.value in a composable body. Note which modifier consumes it.
2. Classify the modifier — value form or lambda form. Most positioning/sizing/drawing modifiers ship in two flavors:

Value form (read in Composition)	Lambda form (read in Layout or Draw)
`Modifier.offset(x = dp, y = dp)`	`Modifier.offset { IntOffset(x, y) }`
`Modifier.padding(start = dp, …)`	`Modifier.layout { measurable, constraints -> … }` (no lambda overload of `padding`; use a custom layout to inset with hot state)
`Modifier.size(dp)`	`Modifier.layout { … }` (custom)
`Modifier.alpha(float)`	`Modifier.graphicsLayer { alpha = … }`
`Modifier.rotate(float)`	`Modifier.graphicsLayer { rotationZ = … }`
`Modifier.scale(float)`	`Modifier.graphicsLayer { scaleX = …; scaleY = … }`
`Modifier.background(color)`	`Modifier.drawBehind { drawRect(color) }`

If a value form is being fed a hot state, swap to the lambda form. The lambda is invoked on every Layout (or Draw) pass without re-running Composition.

3. Migrate value-form modifiers to lambda form. The lambda body re-executes when the read state changes, but it does so during Layout or Draw — Composition is never invalidated.
4. For multiple per-frame transforms, hoist into a single Modifier.graphicsLayer { … } block. It covers translationX/translationY, scaleX/scaleY, rotationZ, alpha, cameraDistance, clip, and shape in one Draw-phase node, and is preferred over chaining alpha + offset + rotate.
5. Hoist hot values across composables as lambda providers. Pass () -> Float instead of Float so the receiving composable can defer the read into its own lambda modifier without invalidating its caller. This is the single most important cross-composable perf trick after lambda modifiers themselves.
6. Verify in Layout Inspector / @TraceRecomposition. The parent composable's recomposition count MUST stop incrementing per animation frame. If it still climbs, the wrong-phase read survived elsewhere — re-grep the file for .value and by state.
7. For per-frame Draw work that is more than a transform, use Modifier.drawBehind { } or Modifier.drawWithCache { }. drawBehind re-runs every Draw pass; drawWithCache caches the build step (e.g. paths, brushes) and only re-runs the onDraw block on state change.

Patterns

Pattern: Animated offset

// WRONG
val animatedX = animateFloatAsState(targetX, label = "x")
Box(Modifier.offset(x = animatedX.value.dp))
// WRONG because: reading .value in Composition phase invalidates the whole subtree on every animation frame.

// RIGHT
val animatedX = animateFloatAsState(targetX, label = "x")
Box(Modifier.offset { IntOffset(animatedX.value.toInt(), 0) })

The lambda runs in Layout, so Composition is never invalidated. The animation still drives the visual position via Layout-only invalidation.

Pattern: Animated alpha (and other transforms)

// WRONG
val animatedAlpha by animateFloatAsState(targetAlpha, label = "alpha")
Box(Modifier.alpha(animatedAlpha))
// WRONG because: Modifier.alpha(Float) reads the value in Composition; the entire Box subtree recomposes every frame.

// RIGHT
val animatedAlpha by animateFloatAsState(targetAlpha, label = "alpha")
Box(Modifier.graphicsLayer { alpha = animatedAlpha })

graphicsLayer { } reads inside the Draw phase only. For combined transforms, fold them all into the same block:

// RIGHT — one Draw-phase node, three transforms
Box(
    Modifier.graphicsLayer {
        alpha = progress
        scaleX = 1f + 0.2f * progress
        scaleY = 1f + 0.2f * progress
        translationX = progress * 32.dp.toPx()
    }
)

Pattern: Scroll-driven sticky header

// WRONG
val offset = scrollState.value
Header(Modifier.offset(y = offset.dp))
// WRONG because: scrollState.value updates every pixel of scroll, and reading it in the parent body recomposes Header (and every sibling) per scroll tick.

// RIGHT
Header(Modifier.offset { IntOffset(0, scrollState.value) })

The lambda captures scrollState (a stable holder), reads .value only when Layout runs, and isolates invalidation to the Layout phase of Header.

Pattern: Lambda providers across composables

// WRONG
@Composable
fun Parent(scrollOffset: Float) {
    Child(scrollOffset)
}

@Composable
fun Child(scrollOffset: Float) {
    Box(Modifier.offset { IntOffset(0, scrollOffset.toInt()) })
}
// WRONG because: scrollOffset is read in Parent's signature each frame; Parent and every sibling of Child recompose per frame even though only Child cares.

// RIGHT
@Composable
fun Parent(scrollOffset: () -> Float) {
    Child(scrollOffset)
}

@Composable
fun Child(scrollOffset: () -> Float) {
    Box(Modifier.offset { IntOffset(0, scrollOffset().toInt()) })
}

The lambda parameter is a stable function reference. Calling it inside Modifier.offset { } defers the read to Layout. Parent never re-reads the hot state, so Parent never recomposes.

Pattern: Per-frame Draw work — `drawBehind` / `drawWithCache`

// WRONG
val color = animatedColor.value
Box(Modifier.background(color))
// WRONG because: Modifier.background reads color in Composition; per-frame color animation invalidates the subtree.

// RIGHT — Draw phase only, no caching needed for a solid color
Box(Modifier.drawBehind { drawRect(animatedColor.value) })

// RIGHT — heavier work; cache the Path/Brush across frames
Box(
    Modifier.drawWithCache {
        val brush = Brush.linearGradient(
            colors = listOf(start, end),
            start = Offset.Zero,
            end = Offset(size.width, size.height),
        )
        onDrawBehind { drawRect(brush, alpha = animatedAlpha.value) }
    }
)

drawWithCache re-builds the cache only when the remember-keys captured by the lambda change; the onDrawBehind block re-runs on every Draw with the latest state.

Three phases reference

Compose runs every frame in this order. A read at phase N invalidates phase N and everything below it. Push reads as low as possible.

Phase	What it does	Cost when invalidated
Composition	Run @Composable functions, build/diff the UI tree	Highest — all skippability gates re-run, child composables potentially recompose
Layout	`measure()` and `placeRelative()` for each node	Medium — re-measure and re-place affected subtree
Draw	Record draw commands into the canvas	Lowest — single render pass

Modifier phase cheat sheet (most common cases)

Modifier	Phase the state is read in
`Modifier.offset(Dp)`	Composition
`Modifier.offset { IntOffset }`	Layout
`Modifier.padding(Dp)` / `padding(PaddingValues)`	Composition (no lambda overload exists)
`Modifier.size(Dp)`	Composition
`Modifier.layout { measurable, constraints -> … }`	Layout (escape hatch when a hot state must drive measurement, e.g. an animated inset — `padding` ships no lambda form)
`Modifier.alpha(Float)`	Composition
`Modifier.rotate(Float)`	Composition
`Modifier.scale(Float)`	Composition
`Modifier.graphicsLayer { … }`	Draw
`Modifier.background(Color)`	Composition
`Modifier.drawBehind { … }`	Draw
`Modifier.drawWithCache { … }`	Draw (cache rebuilds when its captured state changes)

For the deeper mechanics — invalidation propagation, the backwards-write rule, and a comprehensive per-modifier breakdown — see references/three-phases.md.

Mandatory rules

MUST prefer lambda-based modifiers for any state that changes more often than once per user interaction (animation values, scroll position, drag offsets, gesture deltas).
MUST use Modifier.graphicsLayer { … } for animated alpha, scale, rotate, translation. Never Modifier.alpha(state.value) / Modifier.rotate(state.value) / Modifier.scale(state.value) for hot state.
MUST NOT read frequently-changing state in a composable body when a lambda modifier could read it later. Every avoided .value read in a composable body is one fewer Composition invalidation per frame.
MUST NOT write to a MutableState already read in the same composition pass — that is a backwards write (see references/three-phases.md); the runtime aborts the recomposition with a cost.
PREFERRED: lambda providers (() -> T) over plain values when the value is hot and crosses composable boundaries. Pair with lambda modifiers at the receiving end.
PREFERRED: Modifier.drawWithCache { } over Modifier.drawBehind { } when the per-frame work involves rebuildable resources (paths, brushes, gradients) that depend on size or theme.

Verification

Layout Inspector shows the parent composable's recomposition count is 0 (or near 0) per animation frame. Only Layout/Draw counters increment.
@TraceRecomposition(traceStates = true) (skydoves/compose-stability-analyzer) confirms in release + R8 + real device that the wrapping composable does not recompose per frame; only the sub-node attached to the lambda modifier re-runs.
A grep over the migrated file finds no Modifier.alpha(/Modifier.rotate(/Modifier.scale(/Modifier.offset(<dp expr>) patterns fed by hot state.
Cross-composable hot values are passed as () -> T lambda providers, not as T values.
The release build still renders correctly — value-form to lambda-form is a behavioral no-op only when the lambda body matches the original arithmetic (watch for dp vs px confusion: Modifier.offset { IntOffset(x, y) } takes pixels).

References

Android Developers — Phases of Jetpack Compose: https://developer.android.com/develop/ui/compose/phases
Android Developers — Performance: phases: https://developer.android.com/develop/ui/compose/performance/phases
Android Developers — Graphics modifiers: https://developer.android.com/develop/ui/compose/graphics/draw/modifiers
Android Developers — Custom modifiers (Modifier.Node): https://developer.android.com/develop/ui/compose/custom-modifiers
Ben Trengrove — Debugging recomposition: https://medium.com/androiddevelopers/jetpack-compose-debugging-recomposition-bfcf4a6f8d37
Why test perf in release: https://medium.com/androiddevelopers/why-should-you-always-test-compose-performance-in-release-4168dd0f2c71
Chris Banes — Compose perf tag: https://chrisbanes.me/tags/jetpack-compose-performance/
skydoves — 6 Jetpack Compose Guidelines: https://medium.com/proandroiddev/6-jetpack-compose-guidelines-to-optimize-your-app-performance-be18533721f9
skydoves — Jetpack Compose Mechanism: https://speakerdeck.com/skydoves/jetpack-compose-mechanism
references/three-phases.md — detailed walkthrough of Composition / Layout / Draw, downward invalidation, the backwards-write rule, and the comprehensive modifier-phase cheat sheet.

About this skill

Automatic

Frontend / UI pack

SkillSafety

✓ 3 of 3 passed

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IntentSafe

LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

Source

Imported

skydoves / compose-performance-skills

recomposition/deferring-state-reads/SKILL.md

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License Apache-2.0. See LICENSE for complete terms.Imported 8/3/2026Version v1Bundled 1 file

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SkillScore

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What it changes

Track record

Use this skill

Deferring State Reads — Move Hot Reads from Composition to Draw

When to use this skill

When NOT to use this skill

Prerequisites

Workflow

Patterns

Pattern: Animated offset

Pattern: Animated alpha (and other transforms)

Pattern: Scroll-driven sticky header

Pattern: Lambda providers across composables

Pattern: Per-frame Draw work — drawBehind / drawWithCache

Three phases reference

Modifier phase cheat sheet (most common cases)

Mandatory rules

Verification

References

SkillSafety

Source

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More in the registry

Pattern: Per-frame Draw work — `drawBehind` / `drawWithCache`