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Stabilizing Compose Types — Three Tiers, In Order · skydoves | DecimalAI

Frontendby skydoves

Stabilizing Compose Types — Three Tiers, In Order

Use this skill to fix unstable Jetpack Compose types once a stability diagnosis has identified them. Covers the three-tier strategy — make the type truly stable with val plus immutable fields, mark with @Immutable or @Stable when the source is owned, and use stabilityConfigurationFiles for third-party or Java types. Explains the compiler-level difference between @Immutable and @Stable (static expression promotion), kotlinx.collections.immutable for List/Set/Map parameters, and the StableHolder w

Never made a case worseMaintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

73%95%of test cases passed +23 pts

Without skill16 of 22

With skill21 of 22

5 fixed16 passed either way1 still failing0 broke

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

Track record

Agent runs: 45; All time

See one task, with and without the skill

One test taskTo make our `Row` layout skippable, a developer wrapped `Row` inside a new custom `@Composable fun SkippableRow(item: UnstableItem)` function. Will wrapping `Row` in a custom composable make skipping work as expected?

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-stabilizing-compose-types
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-stabilizing-compose-types")
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-stabilizing-compose-types

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-stabilizing-compose-types/SKILL.md https://app.decimal.ai/s/skydoves-stabilizing-compose-types/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/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-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/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-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types")
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-stabilizing-compose-types/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types

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-stabilizing-compose-types/SKILL.md https://app.decimal.ai/s/skydoves-stabilizing-compose-types/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/skydoves-stabilizing-compose-types/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-stabilizing-compose-types/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/skydoves-stabilizing-compose-types.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

Stabilizing Compose Types — Three Tiers, In Order

Once a diagnosis (see ../diagnosing-compose-stability/SKILL.md) has named the unstable types, this skill walks Claude through fixing them. The strategy is a strict three-tier waterfall: (1) make the type truly stable by structural rewrite (val + immutable fields) — no annotation needed; (2) annotate with @Immutable or @Stable when the source is owned and the contract is honored; (3) use stabilityConfigurationFile for third-party or Java types. DO NOT invert this order — annotations are a contract, not a magic spell.

When to use this skill

The compiler report (composables.txt / classes.txt) shows one or more unstable parameters.
The developer asks how to stabilize a domain class, a List<Foo> parameter, java.time.LocalDateTime, or a third-party type.
The developer mentions @Immutable, @Stable, compose-stable-marker, compose-runtime-annotation, kotlinx.collections.immutable, stabilityConfigurationFiles, or stability_config.conf.
A @TraceRecomposition log shows recomposition happening because an argument is allocated fresh every recomposition.

When NOT to use this skill

The unstable types are not yet identified — run ../diagnosing-compose-stability/SKILL.md first.
The symptom is a wrong-phase state read (Modifier.alpha(state.value)); use ../../recomposition/deferring-state-reads/SKILL.md.
The symptom is a derivedStateOf problem; use ../../recomposition/choosing-derivedstateof/SKILL.md.
A Flow<T> parameter is the cause; the fix is to collect upstream — see ../../side-effects/collecting-flows-safely/SKILL.md rather than annotating Flow as stable.

Prerequisites

../diagnosing-compose-stability/SKILL.md has been run; the developer has a concrete list of unstable types.
Kotlin 2.0.0+ with org.jetbrains.kotlin.plugin.compose applied. Strong Skipping is on by default.
For pure-Kotlin / data modules without compose-runtime: either androidx.compose.runtime:runtime-annotation (official) or com.github.skydoves:compose-stable-marker (legacy) on the classpath. Both expose @Stable / @Immutable / @StableMarker without dragging in the full runtime.
For tier-3 fixes: Compose Compiler 1.5.5+ for stabilityConfigurationFiles DSL support (plural; the older singular stabilityConfigurationFile property is deprecated, see footnote below).

Workflow — decision tree

1. Do we own the source of the unstable type? If yes, use tier 1 or tier 2. If no (Java stdlib, third-party SDK), jump to tier 3.
2. Tier 1 — restructure to truly stable. Can every property be a val of an already-stable type? If yes, MUST make that change first. No annotation is needed and no contract is implied. The compiler will infer stability automatically.

// WRONG
data class Snack(
    var name: String,
    val tags: Set<String>,
)
// WRONG because: `var name` is an observable that does not notify Compose, and `Set<String>` is the standard library interface so its stability is Unknown — the data class is unstable on two axes.

// RIGHT
@Immutable
data class Snack(
    val name: String,
    val tags: ImmutableSet<String>,
)

3. Is the unstable property a collection? Replace kotlin.collections.List, Set, Map with kotlinx.collections.immutable.ImmutableList, ImmutableSet, ImmutableMap. Build with persistentListOf(), persistentSetOf(), persistentMapOf() factories or .toImmutableList() adapters. The kotlinx-collections-immutable artifact ships a known-stable bitmask 0b1 recognized by the Compose Compiler. PREFERRED: prefer this over whitelisting kotlin.collections.* in stability_config.conf because the type system enforces immutability.
4. Is the source a pure-Kotlin / data module that does not depend on compose-runtime? Use androidx.compose.runtime:runtime-annotation (official, recommended) or com.github.skydoves:compose-stable-marker (legacy). Both let the data module annotate types without pulling in the full Compose runtime. Skydoves hot take #6: pure-Kotlin / data modules can use compose-stable-marker (or newer official compose-runtime-annotation) without pulling in full compose-runtime.

// shared data module — build.gradle.kts
dependencies {
    // Official, preferred:
    compileOnly("androidx.compose.runtime:runtime-annotation:<version>")
    // Or legacy skydoves:
    // implementation("com.github.skydoves:compose-stable-marker:<version>")
}

5. Tier 2 — choose @Immutable or @Stable. If every property is val AND every nested value is itself immutable AND equals() is structural, use @Immutable. Otherwise — for types whose values can change but whose mutations are observed by Compose (e.g. holders containing MutableState) — use @Stable.

The compiler treats @Immutable more aggressively than @Stable. For @Immutable types, when every constructor argument at a call site is a compile-time constant (e.g. literal numbers, top-level vals of stable types, or other @Immutable-with-constant-args), the compiler performs static expression promotion: the constructed instance is hoisted into a singleton, the lambda capture sites are de-duplicated, and the @Immutable parameter is marked @static in composables.txt. Most articles describe @Stable and @Immutable as interchangeable — they are not.

// RIGHT — @Immutable: every property val, every property type immutable
@Immutable
data class ThemeColors(
    val primary: Color,
    val onPrimary: Color,
    val background: Color,
)

// RIGHT — @Stable: mutable but mutations notify Compose via Snapshot
@Stable
class CartState {
    var total: Money by mutableStateOf(Money.ZERO)
    val lines: SnapshotStateList<Line> = mutableStateListOf()
}

6. Tier 3 — third-party or Java types. Use stabilityConfigurationFiles (plural). Create stability_config.conf at the project root listing exact-class or wildcard patterns; wire it into the composeCompiler { } block via stabilityConfigurationFiles.add(...). Full grammar lives in references/stability-config-syntax.md.

// build.gradle.kts (root or composable module)
composeCompiler {
    stabilityConfigurationFiles.add(
        rootProject.layout.projectDirectory.file("stability_config.conf")
    )
}

Footnote — legacy form. Older projects may still wire the file via the singular property stabilityConfigurationFile = .... That property is @Deprecated("Use the stabilityConfigurationFiles option instead") in modern Compose Compiler Gradle plugin releases — prefer the plural stabilityConfigurationFiles.add(...) shown above.

# stability_config.conf — opt-in contract
java.time.LocalDateTime
java.time.LocalDate
kotlin.collections.List
kotlin.collections.Set
kotlin.collections.Map
com.example.thirdparty.**

7. Nothing in tiers 1-3 fits? Wrap in a @Stable class StableHolder<T>(val item: T). This is the escape hatch — the holder's identity is stable, equality delegates to item, and Compose can skip on it.

@Stable
class StableHolder<T>(val item: T) {
    override fun equals(other: Any?) = other is StableHolder<*> && other.item == item
    override fun hashCode() = item?.hashCode() ?: 0
}

8. Flow<T> parameters — DO NOT annotate as stable. Skydoves hot take #4: Flow parameters are unstable. Don't pass flows to composables; collect them in a ViewModel or with collectAsStateWithLifecycle.

// WRONG
@Composable
fun Feed(items: Flow<List<Item>>) { /* ... */ }
// WRONG because: Flow has no observable identity; the composable cannot skip on it, and a downstream `collectAsState` here detaches from lifecycle.

// RIGHT — collect upstream and pass the resolved value
@Composable
fun FeedRoute(viewModel: FeedViewModel = viewModel()) {
    val items by viewModel.items.collectAsStateWithLifecycle()
    Feed(items = items)
}

@Composable
fun Feed(items: ImmutableList<Item>) { /* ... */ }

9. Re-run the diagnostic skill to verify the previously unstable params are now stable or runtime.

Patterns

Pattern: data class with `var` and `Set` field

// WRONG
data class Snack(
    var name: String,
    val tags: Set<String>,
)
// WRONG because: `var` blocks compile-time stability inference; `Set<String>` is an interface with unknown implementations.

// RIGHT
import kotlinx.collections.immutable.ImmutableSet
import kotlinx.collections.immutable.persistentSetOf

@Immutable
data class Snack(
    val name: String,
    val tags: ImmutableSet<String> = persistentSetOf(),
)

Pattern: `List<Item>` parameter on a composable

// WRONG
@Composable
fun ItemList(items: List<Item>) { /* ... */ }
// WRONG because: `kotlin.collections.List` is an interface; the compiler cannot prove every implementation is immutable, so the parameter is `unstable` and skipping is disabled (or pinned to `===` under Strong Skipping, which still fails on every fresh allocation).

// RIGHT
import kotlinx.collections.immutable.ImmutableList

@Composable
fun ItemList(items: ImmutableList<Item>) { /* ... */ }

Producer side:

val items: ImmutableList<Item> = repository.items().toImmutableList()

Pattern: `java.time.LocalDateTime` flagged unstable

The Java time types are separately compiled, no annotation, so the inference falls through to "unknown". Tier 3 is the right answer.

# stability_config.conf
java.time.LocalDateTime
java.time.LocalDate
java.time.Instant
java.time.ZonedDateTime
java.time.Duration

// WRONG — annotate a wrapper to "force" stability without honoring the contract
@Stable
class DateWrapper(var date: LocalDateTime)
// WRONG because: `var` field, no Snapshot notification — the @Stable contract is broken; recompositions will be silently missed.

// RIGHT — whitelist the immutable JDK type via stability config
// stability_config.conf:  java.time.LocalDateTime
@Immutable
data class Order(val placedAt: LocalDateTime, val total: Money)

Pattern: `Flow<T>` parameter on a composable

// WRONG
@Composable
fun Detail(productFlow: Flow<Product>) {
    val product by productFlow.collectAsState(initial = null)
    /* ... */
}
// WRONG because: `Flow` is a cold producer with no observable identity; the composable can never skip on it, and `collectAsState` (no `WithLifecycle`) keeps collecting in the background.

// RIGHT — hoist collection to the route
@Composable
fun DetailRoute(viewModel: DetailViewModel = viewModel()) {
    val product by viewModel.product.collectAsStateWithLifecycle()
    Detail(product = product)
}

@Composable
fun Detail(product: Product?) { /* ... */ }

Cross-reference: ../../side-effects/collecting-flows-safely/SKILL.md.

Pattern: inline composable wrap as a "fix"

// WRONG — wrapping a Row in an extracted composable to "force" skippability
@Composable
fun ItemRow(item: Item) {
    Row { /* ... */ }
}
// WRONG because: `Row`/`Column`/`Box` are inline composables — they are NOT restartable/skippable to begin with (skydoves hot take #3). Wrapping them creates a new restart scope, which can change behavior unpredictably without addressing the root unstable parameter.

// RIGHT — make the parameter stable, leave the inline composable alone
@Immutable
data class Item(val id: Long, val name: String)

@Composable
fun ItemRow(item: Item) {
    Row { /* ... */ }
}

Pattern: `@Immutable` versus `@Stable` — pick the stronger one

// SUFFICIENT but suboptimal
@Stable
data class ThemeColors(
    val primary: Color,
    val onPrimary: Color,
)
// Sufficient because: the compiler still treats it as stable.
// Suboptimal because: @Immutable would additionally enable static expression promotion when ThemeColors is constructed with all-constant args.

// PREFERRED
@Immutable
data class ThemeColors(
    val primary: Color,
    val onPrimary: Color,
)

In composables.txt, calls like ThemeColors(Color(0xFFFFFFFF), Color(0xFF000000)) will then be reported as stable @static themeColors: ThemeColors — the constructed instance is hoisted to a singleton. MUST prefer @Immutable whenever the type qualifies.

Mandatory rules

MUST NOT annotate a type as @Stable or @Immutable unless the contract is honored. Skydoves hot take #2: stability config is a contract, not a magic spell — break it and recompositions are silently missed.
MUST prefer @Immutable over @Stable whenever every property is a val of an already-immutable type. The compiler emits stronger optimizations (static expression promotion, lambda-singleton, compile-time default eval) for @Immutable.
MUST prefer kotlinx.collections.immutable over annotating a mutable collection as stable. The compiler ships a known-stable bitmask for these types.
MUST NOT wrap inline composables (Row/Column/Box) to "force" skippability. Inline composables are not restartable in the first place; wrapping changes scoping without addressing the root cause.
MUST NOT pass Flow<T> as a composable parameter. Collect upstream with collectAsStateWithLifecycle.
MUST re-run ../diagnosing-compose-stability/SKILL.md after applying fixes; the previously unstable params MUST now report stable or runtime.
PREFERRED: stabilityConfigurationFiles (plural; stabilityConfigurationFiles.add(...)) over scattering annotations across modules the team does not own.
PREFERRED: in pure-Kotlin / data modules, use androidx.compose.runtime:runtime-annotation (official) or com.github.skydoves:compose-stable-marker (legacy) so the annotation is available without a full compose-runtime dependency.

Verification

Re-run release-mode build with composeCompiler { reportsDestination = ... } enabled.
In composables.txt, every previously unstable parameter is now stable or runtime.
In classes.txt, every fixed class is now stable class … or runtime stable class … — no unstable remains for the targeted types.
If stabilityConfigurationFiles was used, every entry has been verified to honor the contract (no hidden mutation, structural equals, no observable that bypasses Snapshot).
@TraceRecomposition (skydoves/compose-stability-analyzer) on the previously hot composable shows recomposition counts dropping to the expected number per state change.
No @Stable / @Immutable annotation has been added to a type that still contains a var or an unstable nested type.

References

Android Developers — Fix stability: https://developer.android.com/develop/ui/compose/performance/stability/fix
Android Developers — Stability overview: https://developer.android.com/develop/ui/compose/performance/stability
Android Developers — Strong Skipping: https://developer.android.com/develop/ui/compose/performance/stability/strongskipping
Ben Trengrove — Stability Explained: https://medium.com/androiddevelopers/jetpack-compose-stability-explained-79c10db270c8
Ben Trengrove — New Ways of Optimizing Stability: https://medium.com/androiddevelopers/new-ways-of-optimizing-stability-in-jetpack-compose-038106c283cc
Ben Trengrove — Strong Skipping Mode Explained: https://medium.com/androiddevelopers/jetpack-compose-strong-skipping-mode-explained-cbdb2aa4b900
Manuel Vivo — Consuming Flows Safely: https://medium.com/androiddevelopers/consuming-flows-safely-in-jetpack-compose-cde014d0d5a3
skydoves — Optimize App Performance by Mastering Stability: https://medium.com/proandroiddev/optimize-app-performance-by-mastering-stability-in-jetpack-compose-69f40a8c785d
skydoves — 6 Jetpack Compose Guidelines: https://medium.com/proandroiddev/6-jetpack-compose-guidelines-to-optimize-your-app-performance-be18533721f9
skydoves — compose-stable-marker: https://github.com/skydoves/compose-stable-marker
kotlinx.collections.immutable: https://github.com/Kotlin/kotlinx.collections.immutable
references/stability-config-syntax.md — full grammar of stability_config.conf with worked examples.

About this skill

Automatic

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SkillSafety

✓ 3 of 3 passed

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Source

Imported

skydoves / compose-performance-skills

stability/stabilizing-compose-types/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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Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

skydoves/understanding-stability-inferenceUse this skill to explain why the Compose compiler classified a class or composable parameter as stable, runtime, unknown, or unstable. Covers the 12-phase inference algorithm, the five compiler-level stability types (Certain / Runtime / Unknown / Parameter / Combined), the generic bitmask encoding (Pair=0b11, ImmutableList=0b1), the Known Stable Constructs registry, and the runtime `$stable: Int` field generated by `@StabilityInferred`. Use when the developer asks "why is X classified as Y?", wClose match+64 pts pass-rate lift

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Safety

What it changes

Track record

Use this skill

Stabilizing Compose Types — Three Tiers, In Order

When to use this skill

When NOT to use this skill

Prerequisites

Workflow — decision tree

Patterns

Pattern: data class with var and Set field

Pattern: List<Item> parameter on a composable

Pattern: java.time.LocalDateTime flagged unstable

Pattern: Flow<T> parameter on a composable

Pattern: inline composable wrap as a "fix"

Pattern: @Immutable versus @Stable — pick the stronger one

Mandatory rules

Verification

References

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Source

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More in the registry

Pattern: data class with `var` and `Set` field

Pattern: `List<Item>` parameter on a composable

Pattern: `java.time.LocalDateTime` flagged unstable

Pattern: `Flow<T>` parameter on a composable

Pattern: `@Immutable` versus `@Stable` — pick the stronger one