Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Generating Baseline Profiles · skydoves | DecimalAI

Testingby skydoves

Generating Baseline Profiles

Use this skill to generate and measure Jetpack Compose Baseline Profiles end-to-end with the AGP 8.2+ Baseline Profile Generator module and the Macrobenchmark harness. Covers writing the `BaselineProfileRule` journey for cold startup plus first-scroll, generating `baseline-prof.txt`, verifying it shipped at `assets/dexopt/baseline.prof`, measuring with `MacrobenchmarkRule` under `CompilationMode.Partial(BaselineProfileMode.Require)`, and emitting accurate time-to-fully-drawn via `ReportDrawn` /

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

55%100%of test cases passed +45 pts

Without skill12 of 22

With skill22 of 22

10 fixed12 passed either way0 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 taskWhen preparing our application manifest for Macrobenchmark measurements on release builds, what XML tag must be added under <application> to allow profiling tools to attach?

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-generating-baseline-profiles
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-generating-baseline-profiles")
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-generating-baseline-profiles

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-generating-baseline-profiles/SKILL.md https://app.decimal.ai/s/skydoves-generating-baseline-profiles/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/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-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/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-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles")
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-generating-baseline-profiles/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles

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-generating-baseline-profiles/SKILL.md https://app.decimal.ai/s/skydoves-generating-baseline-profiles/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/skydoves-generating-baseline-profiles/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-generating-baseline-profiles/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/skydoves-generating-baseline-profiles.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

Generating Baseline Profiles — ship the AOT compilation hint list and prove it moved the needle

Baseline Profiles ship an AOT compilation hint list inside the APK so ART pre-compiles hot Compose code paths on install instead of relying on JIT during the first runs. Cited gains: roughly 30% faster cold startup and 40% smoother first-scroll on the journeys that were profiled. Compose ships unbundled from the platform, so every Compose UI app benefits — there is no version of Android where Compose is already AOT-compiled by the system image.

This skill is the measurement spine for the rest of the performance work. Profiles are generated with BaselineProfileRule from androidx.benchmark:benchmark-macro-junit4 and measured with MacrobenchmarkRule from the same artifact. Generation and measurement are two distinct @Test files in a separate :baselineprofile module that AGP 8.2+ scaffolds via New Module → Baseline Profile Generator.

When to use this skill

Cold startup is slow and the developer wants AOT-compiled hot paths on first launch.
First-scroll on a LazyColumn / LazyVerticalGrid is janky on real devices even after stability fixes.
Preparing a release build and the developer needs a perf baseline before shipping.
Verifying that a stability or strong-skipping fix actually moved cold-startup or frame-timing numbers — Macrobenchmark is the ground truth.
The user mentions "baseline profile", "macrobenchmark", "TTFD", "time-to-fully-drawn", "StartupTimingMetric", "FrameTimingMetric", "CompilationMode", aosp_cf_x86_64_phone-userdebug, or "ReportDrawn".

When NOT to use this skill

Still in early prototyping with no release-ready user journeys yet — Baseline Profiles encode hot paths, and there are no hot paths to encode while screens are still churning.
The developer wants per-composable recomposition counts — that is ../../recomposition/debugging-recompositions/SKILL.md (debug Layout Inspector) or ../tracing-recompositions-at-runtime/SKILL.md (release @TraceRecomposition).
The developer wants a CI gate against stability regressions — that is ../../stability/enforcing-stability-in-ci/SKILL.md. Baseline Profiles measure runtime; stabilityCheck measures compile-time skippability.
Numbers are being collected from a debug build — debug builds run interpreted with Live Literals and produce non-representative numbers. See ../testing-compose-in-release-mode/SKILL.md.

Prerequisites

AGP 8.2+ so the New Module → Baseline Profile Generator template is available (it scaffolds the androidx.baselineprofile Gradle plugin and a :baselineprofile module).
A physical low-end test device or a Cuttlefish emulator (aosp_cf_x86_64_phone-userdebug). High-end pixel devices mask perf wins; emulators with default API images are not representative.
A release variant configured (isMinifyEnabled = true, isShrinkResources = true, proguard-android-optimize.txt). Debug builds are not measurable. See ../../build/configuring-r8-for-compose/SKILL.md for R8 setup if missing.
<profileable android:shell="true"/> in the app AndroidManifest.xml under <application> so the Macrobenchmark process can attach simpleperf. The android: namespace prefix is required — without it, manifest merger fails.
At least one scroll journey identified beyond startup — a feed list, a paginated grid, opening a detail screen. Profiling startup alone leaves first-scroll uncompiled.
Familiarity with ../../stability/diagnosing-compose-stability/SKILL.md and ../../recomposition/debugging-recompositions/SKILL.md if startup is dominated by avoidable recomposition rather than initial composition.

Workflow

1. Add the Baseline Profile Generator module

In Android Studio: File → New → New Module → Baseline Profile Generator. Pick the target application module. AGP scaffolds:

A new :baselineprofile module with the androidx.baselineprofile Gradle plugin applied.
A BaselineProfileGenerator.kt skeleton with a BaselineProfileRule @Test.
A StartupBenchmarks.kt skeleton with a MacrobenchmarkRule @Test.
The androidx.baselineprofile plugin applied in the app module too, so ./gradlew :app:generateBaselineProfile is wired up.

If editing manually instead of using the template, add to :baselineprofile/build.gradle.kts:

plugins {
    id("com.android.test")
    id("org.jetbrains.kotlin.android")
    id("androidx.baselineprofile")
}
android { targetProjectPath = ":app" }
dependencies {
    implementation("androidx.benchmark:benchmark-macro-junit4:1.3.0")
    implementation("androidx.test.ext:junit:1.2.1")
    implementation("androidx.test.uiautomator:uiautomator:2.3.0")
    implementation("androidx.test.espresso:espresso-core:3.6.1")
}

And to :app/build.gradle.kts:

plugins { id("androidx.baselineprofile") }
dependencies { baselineProfile(project(":baselineprofile")) }

See references/macrobenchmark-harness.md for the full Gradle + manifest setup including <profileable>.

2. Write the generator with both startup AND scroll

The generator is a @Test using BaselineProfileRule.collect. MUST cover startup plus at least one scroll — startup-only profiles leave first-scroll cold.

@RunWith(AndroidJUnit4::class)
class BaselineProfileGenerator {
    @get:Rule val rule = BaselineProfileRule()

    @Test
    fun startupAndScroll() = rule.collect(packageName = "com.example") {
        startActivityAndWait()
        val feed = device.findObject(By.res("feed"))
        feed.setGestureMargin(device.displayWidth / 5)
        feed.fling(Direction.DOWN)
        feed.fling(Direction.DOWN)
        device.findObject(By.res("feed_item_0"))?.click()
        device.wait(Until.hasObject(By.res("detail")), 3_000)
        device.pressBack()
    }
}

Use Modifier.testTag("feed") in the Compose source so By.res("feed") resolves. A gesture margin of displayWidth / 5 keeps flings off the system gesture area.

3. Generate the profile

./gradlew :app:generateBaselineProfile

For a specific variant: ./gradlew :app:generateReleaseBaselineProfile.

Output lands at:

app/src/<variant>/generated/baselineProfiles/baseline-prof.txt

(e.g. app/src/release/generated/baselineProfiles/baseline-prof.txt. On older AGP layouts the file may instead land at app/src/main/generated/baselineProfiles/baseline-prof.txt — AGP writes the variant-specific path; merge to main for shipping if all variants share a profile.)

4. Verify the profile shipped in the APK

Build → Analyze APK on the release .apk / .aab and confirm:

assets/dexopt/baseline.prof
assets/dexopt/baseline.profm

If those files are missing, the profile was generated but not packaged — usually because baselineProfile(project(":baselineprofile")) was not added to the app module's dependencies. Without these files in the APK, ART has nothing to AOT-compile and the perf gain is zero.

5. Measure with Macrobenchmark — startup

A separate @Test, in the same :baselineprofile module, using MacrobenchmarkRule. The compilation mode must be CompilationMode.Partial(BaselineProfileMode.Require) so the test fails loudly if the profile is missing rather than silently measuring an unprofiled build.

@RunWith(AndroidJUnit4::class)
class StartupBenchmarks {
    @get:Rule val rule = MacrobenchmarkRule()

    @Test
    fun startupCompilationBaselineProfiles() = rule.measureRepeated(
        packageName = "com.example",
        metrics = listOf(StartupTimingMetric()),
        iterations = 10,
        startupMode = StartupMode.COLD,
        compilationMode = CompilationMode.Partial(BaselineProfileMode.Require),
    ) {
        pressHome()
        startActivityAndWait()
    }
}

For an A/B comparison, add a sibling @Test with compilationMode = CompilationMode.None and compare medians. This is the only way to prove the profile moved the number.

6. Measure with Macrobenchmark — scroll

@Test
fun feedScrollPerformance() = rule.measureRepeated(
    packageName = "com.example",
    metrics = listOf(FrameTimingMetric()),
    iterations = 5,
    compilationMode = CompilationMode.Partial(BaselineProfileMode.Require),
) {
    startActivityAndWait()
    val feed = device.findObject(By.res("feed"))
    feed.setGestureMargin(device.displayWidth / 5)
    feed.fling(Direction.DOWN)
    feed.fling(Direction.DOWN)
}

FrameTimingMetric reports frameDurationCpuMs percentiles (P50 / P90 / P95 / P99) and frameOverrunMs (negative = on time, positive = missed deadline). The headline number is P95 frameOverrunMs — the worst-case missed-deadline frame in the high tail of a scroll.

7. Wire ReportDrawn for accurate TTFD

StartupTimingMetric reports timeToInitialDisplay by default. For timeToFullDisplay (TTFD) — the number that actually matches user perception — the app must call ReportDrawn once the first meaningful screen state is rendered. Without it, TTFD falls back to timeToInitialDisplay and undercounts.

import androidx.activity.compose.ReportDrawn
import androidx.activity.compose.ReportDrawnWhen
import androidx.activity.compose.ReportDrawnAfter

@Composable
fun Feed(state: FeedState) {
    ReportDrawnWhen { state.items.isNotEmpty() }
    LazyColumn { items(state.items, key = { it.id }) { SnackRow(it) } }
}

ReportDrawn — fire immediately on composition (use when the first frame is the meaningful frame).
ReportDrawnWhen { predicate } — fire when the predicate first turns true (typical case: state hydrated).
ReportDrawnAfter { suspend block } — fire after the suspend block completes (typical case: an explicit awaitFirstFrame()).

8. Run on a real device, compare medians, iterate

Run on a connected physical device or aosp_cf_x86_64_phone-userdebug Cuttlefish via ./gradlew :baselineprofile:connectedReleaseAndroidTest. Report medians across iterations, not means — Macrobenchmark logs both, and means are sensitive to a single thermal-throttled run. The IDE displays a result table; the JSON lives at :baselineprofile/build/outputs/connected_android_test_additional_output/.

See references/macrobenchmark-harness.md for parsing the JSON in CI.

Patterns

Pattern: profile startup AND scroll, never startup alone

// WRONG
@Test fun startup() = rule.collect(packageName = "com.example") {
    startActivityAndWait()
}
// WRONG because: only the startup code paths get AOT-compiled. First-scroll, list-item
// composition, and detail-screen entry stay interpreted on first run, which is exactly
// where the user perceives jank. Profile every hot user journey, not just launch.

// RIGHT
@Test fun startupAndScroll() = rule.collect(packageName = "com.example") {
    startActivityAndWait()
    val feed = device.findObject(By.res("feed"))
    feed.setGestureMargin(device.displayWidth / 5)
    feed.fling(Direction.DOWN)
    feed.fling(Direction.DOWN)
}

Pattern: assert the profile is applied with BaselineProfileMode.Require

// WRONG
compilationMode = CompilationMode.Partial(BaselineProfileMode.UseIfAvailable)
// WRONG because: UseIfAvailable silently falls back to no profile if assets/dexopt/baseline.prof
// is missing. The Macrobench then measures an unprofiled build and the developer thinks
// the profile is working when it is not. Use Require to fail loudly.

// RIGHT
compilationMode = CompilationMode.Partial(BaselineProfileMode.Require)

Pattern: TTFD undercount when ReportDrawn is missing

// WRONG
@Composable
fun Feed(state: FeedState) {
    LazyColumn { items(state.items) { SnackRow(it) } }
}
// WRONG because: TTFD is reported when the first frame draws. An empty LazyColumn draws
// a frame too — TTFD then matches timeToInitialDisplay and undercounts the time the user
// actually waited for content. Add ReportDrawnWhen { state.items.isNotEmpty() }.

// RIGHT
@Composable
fun Feed(state: FeedState) {
    ReportDrawnWhen { state.items.isNotEmpty() }
    LazyColumn { items(state.items, key = { it.id }) { SnackRow(it) } }
}

Pattern: never measure Compose perf in a debug build

# WRONG
"./gradlew :app:installDebug && adb shell am start … && record startup with systrace"
# WRONG because: debug builds run Compose interpreted, with Live Literals turning constants
# into getters that defeat compile-time folding. Cold-start numbers are inflated 2–4×;
# scroll FrameTiming numbers are dominated by the interpreter overhead, not by the code.
# Measurement only counts on release + R8 + real device. Cross-link
# ../testing-compose-in-release-mode/SKILL.md.

# RIGHT
"./gradlew :baselineprofile:connectedReleaseAndroidTest" on a physical device, with
release variant minified, baseline profile generated, BaselineProfileMode.Require asserting it.

Pattern: report medians, not means, across enough iterations

# WRONG
"Mean cold startup with profile: 412 ms (5 iterations)."
# WRONG because: a single thermal-throttled iteration drags the mean. Macrobenchmark
# reports min / median / max; the median is the resilient summary. 5 iterations is also
# thin for startup variance — use ≥10 for StartupTimingMetric, ≥5 for FrameTimingMetric.

# RIGHT
"Median cold startup, 10 iterations: BaselineProfile 318 ms vs None 462 ms (–31%).
 P95 frameOverrunMs scrolling 30 items: BaselineProfile –6 ms vs None +9 ms."

Pattern: gesture-margin so flings are not eaten by the system

// WRONG
val feed = device.findObject(By.res("feed"))
feed.fling(Direction.DOWN)
// WRONG because: on gesture-nav devices, a fling that starts inside the system gesture area
// is intercepted as a back-swipe or recents-swipe and the scroll never happens.
// Macrobenchmark then measures an idle screen and reports artificially-good numbers.

// RIGHT
val feed = device.findObject(By.res("feed"))
feed.setGestureMargin(device.displayWidth / 5)
feed.fling(Direction.DOWN)

Mandatory rules

MUST generate Baseline Profiles for both startup AND at least one scroll journey. Startup-only profiles leave first-scroll cold.
MUST measure on a real low-end physical device or aosp_cf_x86_64_phone-userdebug Cuttlefish. High-end devices mask the perf delta; default API emulator images are not representative.
MUST use CompilationMode.Partial(BaselineProfileMode.Require) in the measurement test so a missing or stale profile fails the test loudly. BaselineProfileMode.UseIfAvailable silently measures an unprofiled build.
MUST use ReportDrawn / ReportDrawnWhen / ReportDrawnAfter from androidx.activity.compose to mark the meaningful first-drawn moment. Without it timeToFullDisplay undercounts.
MUST NOT measure Compose perf in debug builds — debug runs interpreted with Live Literals, the numbers are not representative. Cross-link ../testing-compose-in-release-mode/SKILL.md.
MUST verify the profile shipped in the APK: assets/dexopt/baseline.prof must exist after ./gradlew :app:assembleRelease. If absent, the baselineProfile(project(":baselineprofile")) wiring on the app module is missing.
MUST report medians, not means, across iterations. Means are sensitive to a single thermal-throttled run.
PREFERRED: ≥10 iterations for StartupTimingMetric, ≥5 for FrameTimingMetric.
PREFERRED: keep an A/B sibling test pinned to CompilationMode.None so every PR can prove the profile is still moving the number.
PREFERRED: add Modifier.testTag("feed") (or whatever ID the journey uses) in the Compose source rather than relying on text matchers — text changes with localization, test tags do not.

Verification

References

Baseline Profiles overview — https://developer.android.com/topic/performance/baselineprofiles/overview
Baseline Profiles with Compose — https://developer.android.com/develop/ui/compose/performance/baseline-profiles
Benchmark Baseline Profiles with Macrobenchmark — https://developer.android.com/topic/performance/baselineprofiles/measure-baselineprofile
Macrobenchmark library — https://developer.android.com/topic/performance/benchmarking/macrobenchmark-overview
androidx.activity.compose.ReportDrawn reference — https://developer.android.com/reference/kotlin/androidx/activity/compose/package-summary
Ben Trengrove, "Why you should always test Compose performance in release" — https://medium.com/androiddevelopers/why-should-you-always-test-compose-performance-in-release-4168dd0f2c71
skydoves, "Improving Android App Performance with Baseline Profiles" (Stream blog) — https://getstream.io/blog/android-baseline-profile/
Compose performance overview — https://developer.android.com/develop/ui/compose/performance
references/macrobenchmark-harness.md — full Gradle + manifest harness setup, the metric catalogue (StartupTimingMetric, FrameTimingMetric, TraceSectionMetric, MemoryUsageMetric, PowerMetric), and how to parse the JSON output for CI dashboards.

For confirming that a generated profile actually moved the recomposition count of a specific composable in release, see ../tracing-recompositions-at-runtime/SKILL.md. For why debug numbers are not measurement evidence, see ../testing-compose-in-release-mode/SKILL.md. For the build-time R8 setup that release measurement assumes, see ../../build/configuring-r8-for-compose/SKILL.md. For preventing stability regressions between profile-generation runs, see ../../stability/enforcing-stability-in-ci/SKILL.md.

About this skill

Automatic

Frontend / UI pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

skydoves / compose-performance-skills

measurement/generating-baseline-profiles/SKILL.md

View on GitHub ↗

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

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

Related skills

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

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

Generating Baseline Profiles — ship the AOT compilation hint list and prove it moved the needle

When to use this skill

When NOT to use this skill

Prerequisites

Workflow

1. Add the Baseline Profile Generator module

2. Write the generator with both startup AND scroll

3. Generate the profile

4. Verify the profile shipped in the APK

5. Measure with Macrobenchmark — startup

6. Measure with Macrobenchmark — scroll

7. Wire ReportDrawn for accurate TTFD

8. Run on a real device, compare medians, iterate

Patterns

Pattern: profile startup AND scroll, never startup alone

Pattern: assert the profile is applied with BaselineProfileMode.Require

Pattern: TTFD undercount when ReportDrawn is missing

Pattern: never measure Compose perf in a debug build

Pattern: report medians, not means, across enough iterations

Pattern: gesture-margin so flings are not eaten by the system

Mandatory rules

Verification

References

SkillSafety

Source

Related skills

More in the registry