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ego-browser · kwakseongjae | DecimalAI

Testingby kwakseongjae

ego-browser

When you need a browser, read this Skill by default. Use it to open and operate websites, fill forms, click buttons, take screenshots, extract page data, sign in, and perform other browser automation tasks, as well as web app testing, dogfooding, QA, bug investigation, and app-quality review. ego-browser (ego-lite) is a Chromium browser designed for both human users and AI Agents. Agents can use the user's logged-in websites and personal context to complete tasks and collaborate smoothly with th

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%100%of test cases passed +73 pts

Without skill6 of 22

With skill22 of 22

16 fixed6 passed either way0 still failing0 broke

Measured on gemini-3.8-flash11/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWrite an ego-browser command to select the option displaying visible text 'California' in the dropdown 'select[name=state]'.

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 kwakseongjae-ego-browser
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("kwakseongjae-ego-browser")
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 kwakseongjae-ego-browser

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/kwakseongjae-ego-browser/SKILL.md https://app.decimal.ai/s/kwakseongjae-ego-browser/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/kwakseongjae-ego-browser/SKILL.md

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

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

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

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

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

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

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

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

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

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/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/kwakseongjae-ego-browser/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

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

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

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

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

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

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

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

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add kwakseongjae-ego-browser

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("kwakseongjae-ego-browser")
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/kwakseongjae-ego-browser/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/kwakseongjae-ego-browser/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

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

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add kwakseongjae-ego-browser

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/kwakseongjae-ego-browser/SKILL.md https://app.decimal.ai/s/kwakseongjae-ego-browser/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/kwakseongjae-ego-browser/SKILL.md

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

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/kwakseongjae-ego-browser/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/kwakseongjae-ego-browser.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/2/2026
Source: kwakseongjae/oh-my-design ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

ego-browser

For installation, connection, or runtime problems, read references/install.md. Use help() or references/api.md for signatures and uncommon options of APIs named below.

Run browser scripts

Run JavaScript through a heredoc:

ego-browser nodejs <<'EOF'
const task = await taskSpace("inspect example page");
const page = task.page("p1");
await page.goto("https://example.com");

console.log({ taskSpaceId: task.spaceId, page: page.label });
console.log(await page.snapshot());
EOF

In some sandbox environments, heredoc input may not work; use -e instead:

ego-browser nodejs -e '
const task = await taskSpace("inspect example page");
const page = task.page("p1");
await page.goto("https://example.com");
console.log({ taskSpaceId: task.spaceId, page: page.label });
console.log(await page.snapshot());
'

In Bash/Zsh, use single quotes around the code and double quotes for JavaScript strings. Single quotes within the code require shell quoting.

The script always runs in Node.js, not in the web Page. Browser helpers and Node.js APIs belong in the script; Page globals such as window, document, location, and DOM APIs do not. Put browser-side JavaScript inside page.evaluate(). Do not import Playwright or launch another browser.

The Node.js runtime uses ESM. When a script needs local files, load built-ins with dynamic imports such as await import("node:fs/promises").

Ego-browser deliberately exposes a small custom API. It is not Playwright, even where method names and options look similar. Use only the TaskSpace, Page, FileChooser, mouse, and keyboard APIs explicitly listed in this Skill. Do not infer Playwright methods such as locator(), getByRole(), context(), expect(), or route(). When the listed API does not cover an operation, use the documented page.evaluate() or page.cdp() escape hatches instead of guessing another method.

Pointer actions accept an optional label with a concise 3-6 word description. Pass it with clicks, hovers, drags, or scrolling to keep the action text next to the visible agent cursor in sync with the action.

When the user explicitly asks for ego-browser, start with a real browser command and diagnose the CLI or installation only if it fails.

Spaces, rounds, and pages

Use exactly one TaskSpace for the entire user goal. Create it once, print its spaceId, and resume that same space in later rounds. Use multiple spaces only when the user explicitly requests them.
Never use a new TaskSpace to recover from a stuck, blocked, timed-out, or unexpected Page. Recover within the existing space; if it cannot continue, stop and ask the user.
Every invocation starts a new Node.js process. Task spaces, tabs, and Page labels persist; JavaScript variables do not.
A new task space starts with Page p1; navigate it instead of opening another Page.
Reuse a Page with goto() instead of opening a new Page for every URL.
All time values are milliseconds.

// Later round: use the space id and Page label printed earlier.
const resumed = await taskSpace(7);
const source = resumed.page("p1");
await source.goto("https://example.com/releases");

Do not inspect or select profiles unless the user explicitly requests a particular Ego Lite profile. A profileId applies only when creating a space; use help("profiles") for the exact workflow.

Supported TaskSpace API:

State: spaceId, name, ownership, page(label), userPage()
Pages: await task.pages(), await task.tabs(), newPage(), adopt(page, { as? }), release(label)
Control: waitForControl(options), handOff(), finish({ keep })
Advanced: cdp(method, params, options)

Pages receive permanent labels such as p1, p2, and p3. Prefer these labels to custom { as } values. Reuse or close Pages as the task proceeds; the runtime reports the configured Page budget when it is reached.

task.newPage() creates another blank Page when multiple Pages must stay open. Navigate it separately with page.goto().

await task.pages() returns managed Pages. await task.tabs() returns every tab in the space as { label?, page, targetId, title, url, active, openedBy }. A tab without a label is unmanaged; adopt it before operating:

const active = (await task.tabs()).find((item) => item.active);
if (active && !active.label) {
  const page = await task.adopt(active.page);
  console.log({ page: page.label, url: await page.url() });
}

release(label) returns an unknown-origin Page to the user without closing its tab. Close Agent-created Pages with page.close(). Treat openedBy: "unknown" as user-owned when deciding whether a Page may be closed.

Page operations

ego-browser provides the following Page API:

State and observation: label, spaceId, openedBy, targetId, url(), title(), info(), snapshot(), screenshot()
Navigation and waits: goto(), reload(), waitForURL(), waitForEvent(), waitForSelector(), waitForLoadState(), waitForFunction(), waitForTimeout()
Elements: click(), dblclick(), hover(), dragAndDrop(), fill(), selectOption(), focus(), press(), setInputFiles(), waitForFileChooser(), close()
Dialogs: acceptDialog(promptText?), dismissDialog()
Pointer: mouse.click(), move(), down(), up(), wheel()
Keyboard: keyboard.down(), up(), press(), type(), insertText(), paste()
Page code and protocols: evaluate(fnOrString, argument), fetch(url, options), cdp(method, params, options)

page.evaluate() callbacks run only inside the Page; they cannot read variables or Node.js modules from the surrounding script. Define browser-side helpers inside the callback or pass one JSON-serializable value as its second argument.

Work efficiently:

Each time you observe, collect only the cheapest page state sufficient to choose the next action. Use a snapshot for semantic or locator ground truth and a screenshot for visual confirmation; do not request both by default.
If an action does not produce the expected result, inspect the current page before deciding whether to retry. Do not blindly repeat it or immediately fall back to coordinates or raw CDP.
Once the page clearly shows the requested result, stop; do not confirm the same result through multiple surfaces.

Semantic pages: snapshot and selectors

Prefer snapshots and semantic selectors for ordinary DOM pages. Use screenshots and coordinates only when useful DOM semantics are unavailable.

Before choosing an unfamiliar target, take a snapshot. When the current state is sufficient to plan several actions on the same Page, complete them in one script invocation, then observe the result once. Observe between actions only when an intermediate result changes what should happen next. Keep the action sequence, the wait for its final expected state, and the next snapshot in the same script invocation. Print the snapshot last so the next round can act on it directly. The final snapshot is the next round's starting view of the changed page; without it, that round usually has to spend a separate browser call observing before it can choose the next target, which wastes compute.

Wait for the expected result: use waitForURL() for navigation, waitForSelector() for element state, or waitForFunction() for application state. Avoid fixed delays when an observable condition exists. A snapshot captures the current moment; it does not wait for the page to become stable. page.snapshot() captures the current viewport. For content outside it, use page.snapshot({ scope: "full_page" }).

The default viewport snapshot includes visible iframe content returned by the browser. To focus on a frame's subtree, reuse the ref printed on its iframe line:

console.log(await page.snapshot({ scope: "subtree", root: "@12" }));

Use the refs returned by the subtree for actions inside the iframe. A subtree snapshot does not scope later locator actions; they still prefer actionable matches in the top document before searching frames.

waitForLoadState() defaults to load. waitForFunction() follows the Playwright argument order; pass undefined before options when there is no Page argument:

await page.waitForFunction(() => window.appReady, undefined, {
  timeout: 10_000,
});

// Round 1: inspect and choose targets from this output.
const page = task.page("p1");
console.log(await page.snapshot());

// Next round: act using the previous output, verify, then prepare the next round.
const page = task.page("p1");
await page.fill("@21", "[email protected]");
await page.click("loc=role:button[name='Sign in']");
await page.waitForSelector("loc=css:#account-home", { state: "visible" });
console.log(await page.snapshot());

Element actions accept:

snapshot refs such as @21 or ref=21
text=... for page content
loc=css:, loc=role:, and loc=href: locators
xpath=...
raw CSS selectors

Selector actions require exactly one match. Unquoted text normalizes whitespace, ignores case, and matches a substring; quoted text such as text="Save changes" is exact and case-sensitive.

A small Playwright-compatible selector subset is also accepted: css=..., terminal :has-text("...") and :text-is("..."), >> nth=N after a CSS, text, or href selector (N is -1 or non-negative), plus loc=role:...[name*="..."] for accessible-name substrings. Other Playwright selector syntax is not supported.

When a selector identifies a wrapper, focus() and press() may use its interactive ancestor or unique editable descendant; fill() and setInputFiles() only continue to a unique compatible control.

click(), fill(), hover(), and dragAndDrop() automatically bring their target into view with browser wheel input. Do not pre-scroll solely to make a DOM target actionable.

Snapshot node names are accessibility roles. Use a ref now or loc=... to find the element again. After the page changes, take a new snapshot. When a useful node has no ref, construct a selector from its role, text, or surrounding context. CSS searches nested open shadow roots. Actions use an actionable match in the top document first, then search frames when the top document has none. Multiple actionable matches in the selected document or frame are ambiguous.

Select options by value, visible label, or zero-based index. A string matches either value or label; pass an array for a multiple select:

await page.selectOption("select[name=month]", { label: "October" });

Pass null or [] to clear the current selection.

Visual pages: screenshot, mouse, and keyboard

Use a screenshot with mouse and keyboard operations for canvas, rich-text, spreadsheets, maps, and other interfaces that lack useful DOM semantics:

const path = await page.screenshot({ path: "/absolute/path/before.png" });
await page.mouse.click(420, 260, { label: "open spreadsheet cell" });
await page.mouse.wheel(0, 600, { label: "scroll project board" });
await page.keyboard.paste("hello\tworld");
console.log({ screenshot: path });

Inspect the screenshot with an image-viewing tool. Coordinates use CSS pixels; keyboard names and +-separated chords follow Playwright syntax. Use ControlOrMeta for portable shortcuts and verify the resulting page state. mouse.wheel() performs a short wheel-input motion at the current mouse position and resolves when that motion completes. In each script invocation, move or click over the intended scrollable area before using it.

On macOS, keyboard.paste() sends the native paste shortcut and then restores the user's clipboard. Pass { text, html } when a rich editor needs structured clipboard content; text is the plain-text fallback. On other platforms, use keyboard.insertText() for plain text.

await page.keyboard.paste({
  text: "Name\tStatus",
  html: "<table><tr><td>Name</td><td>Status</td></tr></table>",
});

For rich-text editors and editable grids, validate a small edit before repeating it at scale. Canvas-backed editors may not expose visible content through DOM text or selectors; verify those results with a screenshot or an application-specific visible state.

Page JavaScript and CDP

Use page.evaluate() for bulk extraction or complex in-page work. It accepts one JSON-serializable argument and returns a JSON-serializable value:

const rows = await page.evaluate(
  ({ selector, limit }) =>
    [...document.querySelectorAll(selector)].slice(0, limit).map((node) => ({
      text: node.textContent?.trim(),
      href: node.querySelector("a")?.href,
    })),
  { selector: "article", limit: 20 },
);

page.evaluate() has no timeout option. Keep long work in bounded calls; on a safety timeout, use executionStopped and mayHaveLateEffects to decide whether an unsafe follow-up requires reloading or closing the Page first.

Use documented Page methods first. If a wrapper is missing or does not work reliably on the current page, use page.cdp() as a lower-level path for diagnosis or control. It accepts Page, Runtime, DOM, Network, Input, and similar commands; use task.cdp() for Target and Browser commands. Raw CDP invalidates refs. Do not persist page.targetId across rounds.

Action receipts, popups, and dialogs

When an action is expected to open a new Page, start the wait before the action:

const popupPromise = page.waitForEvent("popup");
await page.click('a[target="_blank"]');
const popupPage = await popupPromise;
await popupPage.waitForLoadState();

High-level actions also report immediately observed popups in receipt.popups as { label, targetId }. Resolve the Page with task.page(receipt.popups[0].label) and continue there; wait for its URL when the destination matters.

For uncommon protocol-event workflows, await page.events() returns and clears the buffered event array; it is not an EventEmitter.

A synchronous JavaScript dialog may appear as receipt.dialog or in page.info(). Handle it before continuing:

await page.acceptDialog("prompt response");
// Or: await page.dismissDialog();

A receipt describes only the dispatched action and immediate popup or dialog observations; it does not verify the resulting application state.

Files and requests

Set an existing file input with absolute paths:

await page.setInputFiles("input[type=file]", ["/absolute/path/report.pdf"]);

If a click creates the file input, start waiting before the click:

const chooserPromise = page.waitForFileChooser({ timeout: 10_000 });
await page.click("button.upload");
const chooser = await chooserPromise;
const result = await chooser.setFiles("/absolute/path/report.pdf");

An upload-triggered JavaScript dialog may be returned as result.dialog; when present, handle it with the dialog methods above.

For a browser download, arm the event before the triggering action and save the returned artifact to an absolute path in the same script:

const downloadPromise = page.waitForEvent("download", { timeout: 30_000 });
await page.click("button.download");
const download = await downloadPromise;
console.log({
  url: download.url(),
  suggestedFilename: download.suggestedFilename(),
});
await download.saveAs("/absolute/path/report.pdf");

download.saveAs() waits for completion and creates missing parent directories. download.path() returns the round-local temporary file; failure(), cancel(), and delete() manage its lifecycle. Temporary download files are removed when the SDK round is disposed, so call saveAs() before the script ends. Do not set a global download directory with raw CDP; each download wait configures and restores only the addressed Page session.

page.fetch() runs window.fetch() in the Page: relative URLs, cookies, and service workers use that Page, and browser CORS still applies. It returns { ok, status, statusText, url, headers, body }:

const response = await page.fetch("/api/items", {
  method: "POST",
  headers: { "content-type": "application/json" },
  body: JSON.stringify({ limit: 20 }),
  timeout: 10_000,
});

Save binary responses without converting them to text:

await page.fetch("/image.png", { saveAs: "/absolute/path/image.png" });

Use standard Node.js fetch() for background requests that do not need Page browser semantics.

User control and completion

Stop when the user takes control or the space is inactive or unassigned. Do not retry or route around the stop. Permission prompts, device choosers, and other browser-owned prompts require the user to handle them.

When the user must act in the browser, call await task.handOff(), end the round, and explain what they should do. After the user confirms, resume the same space:

const task = await takeOverTaskSpace(7);
const userPage = task.userPage();

Adopt userPage if it is unmanaged. Use waitForControl() only when the current script must wait in place. Claim a user-owned or inactive space only when the user explicitly asks. Find its numeric id first; names may be duplicated:

const spaces = await listTaskSpaces();
console.log(spaces.filter((space) => space.ownership === "user"));

const task = await claimTaskSpace(7);
const userPage = task.userPage();

When the task succeeds, close the TaskSpace by default with await task.finish({ keep: [] }). Call finish() exactly once and wait for it to resolve before reporting completion.

Keeping Pages is a rare exception: retain only necessary Pages when the user explicitly asks, or when the result must remain in the browser for the user to view or continue working with. Pages merely visited, search results, and intermediate steps do not need to remain open.

await task.finish({ keep: [] }); // Default: keep no Agent-managed Pages.
await task.finish({ keep: ["p2"] }); // Exception: keep only the result Page for the user.

User-created and unmanaged tabs are protected; if any remain, keep: [] does not close the whole space. Do not close unwanted Pages one by one at completion; list the Pages to keep instead. Use page.close() only while the task is still in progress. Do not call finish() when the task stops for user control or an error.

If the final output contains [ego-browser:notice], finish the current browser task, tell the user an Ego Lite update is available, and run ego-browser upgrade only with their approval. Re-read this Skill after the upgrade.

References

Installation and connection
API signatures and options
Clearing cookies, cache, and storage — read before clearing any cookie, cache, or storage; some clears reach the whole browser profile.

About this skill

Automatic

QA & Test Engineering pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/30/2026

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

kwakseongjae / oh-my-design

.agents/skills/ego-browser/SKILL.md

View on GitHub ↗

License MITImported 10/30/2026Version v1Bundled 4 files · scripts

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

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

code-yeongyu/dev-browserBrowser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.Close match+55 pts pass-rate lift

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SkillScore

Safety

What it changes

Track record

Use this skill

ego-browser

Run browser scripts

Spaces, rounds, and pages

Page operations

Semantic pages: snapshot and selectors

Visual pages: screenshot, mouse, and keyboard

Page JavaScript and CDP

Action receipts, popups, and dialogs

Files and requests

User control and completion

References

SkillSafety

Source

Other skills for the same job

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