Ready to supercharge your AI agents?

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

Browser Trace · mxyhi | DecimalAI

Testingby mxyhi

Browser Trace

Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets.

SkillScore

SkillScore93/100

Test results 86 · Live runs 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%86%of test cases passed +64 pts

Without skill5 of 22

With skill19 of 22

15 fixed4 passed either way2 still failing1 broke

Measured on gemini-3.6-flash8/3/2026v120 comparable

Track record

Agent runs: 81; 5 in the last 30 days

See one task, with and without the skill

One test taskI want to list the top hostnames targeted by network requests in a completed trace run 'api-run'. What subcommand of the trace query script aggregates request counts by domain?

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 mxyhi-browser-trace
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("mxyhi-browser-trace")
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 mxyhi-browser-trace

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/mxyhi-browser-trace/SKILL.md https://app.decimal.ai/s/mxyhi-browser-trace/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/mxyhi-browser-trace/SKILL.md

This gets SKILL.md only. The skill's 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/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/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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("mxyhi-browser-trace")
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/mxyhi-browser-trace/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/mxyhi-browser-trace/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 8 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 mxyhi-browser-trace

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/mxyhi-browser-trace/SKILL.md https://app.decimal.ai/s/mxyhi-browser-trace/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/mxyhi-browser-trace/SKILL.md

This gets SKILL.md only. The skill's 8 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/mxyhi-browser-trace/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/mxyhi-browser-trace.json

This gets SKILL.md only. The skill's 8 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/3/2026
Source: mxyhi/ok-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

Browser Trace

Attach a second, read-only CDP client to a browser session that is already being driven by your main automation. The trace records the full DevTools firehose to NDJSON, polls for screenshots and DOM dumps in parallel, and slices everything into a directory tree that bash tools can search.

This skill does not drive pages — it only listens. Pair it with the browser skill, browse, Stagehand, Playwright, or anything else that speaks CDP.

When to use

The user wants to debug a browser-automation run (failing form, missing element, hung navigation, JS exception).
The user has a running automation and wants to attach a trace mid-flight without restarting it.
The user wants to split a CDP firehose into network / console / DOM / page buckets.
The user wants screenshots + DOM snapshots over time, joined to CDP events by timestamp.

If the user just wants to drive the browser, use the browser skill instead.

Setup check

node --version                                  # require Node 18+
which browse || npm install -g browse
which jq     || true                                # optional — used only for ad-hoc querying

Verify browse cdp exists:

browse --help | grep -q "^\s*cdp " || echo "browse cdp not available — update browse"

How it works

Every Chrome DevTools target accepts multiple concurrent CDP clients. Your main automation is one client; this skill adds a second one that only enables observation domains (Network, Console, Runtime, Log, Page) and never sends action commands.

The tracer has three pieces:

Firehose: browse cdp <target> streams every CDP event as one JSON object per line to cdp/raw.ndjson.
Sampler: a polling loop calls browse screenshot --cdp <target> --path <file> and browse get html body --cdp <target> on an interval (default 2s). The helper passes --cdp when it samples so it can attach to the traced target from its own process; once a browse daemon session is attached to a CDP target, follow-up commands in that session do not need to repeat --cdp.
Bisector: after the run, bisect-cdp.mjs walks raw.ndjson once, slices it into per-bucket JSONL files keyed by CDP method, and additionally bisects per page using top-level Page.frameNavigated events as boundaries.

Quickstart

Local Chrome

# 1. Launch Chrome with a debugger port (any user-data-dir keeps it isolated).
"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" \
  --remote-debugging-port=9222 \
  --user-data-dir=/tmp/chrome-o11y \
  about:blank &

# 2. Start the tracer.
node scripts/start-capture.mjs 9222 my-run

# 3. Run your main automation against port 9222.
browse open https://example.com --cdp 9222
# ...whatever the run does...

# 4. Stop and bisect.
node scripts/stop-capture.mjs my-run
node scripts/bisect-cdp.mjs my-run

Browserbase remote

Two helpers wrap the platform-side bookkeeping: bb-capture.mjs creates or attaches to a session and starts the tracer; bb-finalize.mjs pulls platform artifacts (final session metadata, server logs, downloads) into the run dir at the end.

Browserbase ends a session as soon as its last CDP client disconnects. Create with --keep-alive, then attach automation to the session's connectUrl before or together with the tracer. bb-capture.mjs --new handles the keep-alive session and tracer setup; your automation still needs to attach.

export BROWSERBASE_API_KEY=...

# 1. Create a keep-alive session AND start the tracer in one step.
#    Prints the session id, connectUrl prefix, and a live debugger URL you
#    can open in a browser to watch the run interactively.
node scripts/bb-capture.mjs --new my-run

# 2. Drive automation. bb-capture stamped the session id into the manifest.
SID=$(jq -r .browserbase.session_id .o11y/my-run/manifest.json)
CONNECT_URL="$(browse cloud sessions get "$SID" | jq -r .connectUrl)"
BROWSE_NAME=my-run-browser
browse open https://example.com --cdp "$CONNECT_URL" --session "$BROWSE_NAME"
browse open https://news.ycombinator.com --session "$BROWSE_NAME"

# 3. Stop the tracer, bisect, then pull platform artifacts and release.
node scripts/stop-capture.mjs my-run
node scripts/bisect-cdp.mjs my-run
node scripts/bb-finalize.mjs my-run --release

Attaching to a session that's already running (e.g. one your production worker created) — bb-capture.mjs accepts a session id instead of --new:

# Pick a running session (filter client-side; browse cloud sessions list has no --status flag)
browse cloud sessions list | jq -r '.[] | select(.status == "RUNNING") | .id'

node scripts/bb-capture.mjs <session-id> mid-flight-debug
# ...tracer runs alongside the existing automation client; no disruption...
node scripts/stop-capture.mjs mid-flight-debug
node scripts/bisect-cdp.mjs mid-flight-debug
node scripts/bb-finalize.mjs mid-flight-debug   # without --release: leave the session running

What you get from the Browserbase platform

bb-capture.mjs adds a browserbase block to manifest.json (session id, project, region, started_at, expires_at, debugger URL). bb-finalize.mjs writes:

<run>/browserbase/session.json — final browse cloud sessions get snapshot (proxyBytes, status, ended_at, viewport, …)
<run>/browserbase/logs.json — browse cloud sessions logs output. Often empty. The CDP firehose in cdp/raw.ndjson is the source of truth; this is a side channel.
<run>/browserbase/downloads.zip — files the session downloaded, if any (the script discards the empty 22-byte zip you get when there are none)

Session replay artifact fetching is deprecated and isn't fetched. Use the screenshots + DOM dumps in screenshots/ and dom/ for visual ground truth.

The live debugger_url in the manifest opens an interactive Chrome DevTools view served by Browserbase — handy for watching a long-running automation while the tracer captures the firehose to disk.

Filesystem layout

.o11y/<run-id>/
  manifest.json                 run metadata: target, domains, started_at, stopped_at
  index.jsonl                   one line per sample: {ts, screenshot, dom, url}
  cdp/
    raw.ndjson                  full CDP firehose (one JSON object per line)
    summary.json                {sessionId, duration, totalEvents, pages[]} — see shape below
    network/{requests,responses,finished,failed,websocket}.jsonl   session-wide buckets (always written)
    console/{logs,exceptions}.jsonl
    runtime/all.jsonl
    log/entries.jsonl
    page/{navigations,lifecycle,frames,dialogs,all}.jsonl
    dom/all.jsonl                                                  (only if O11Y_DOMAINS includes DOM)
    target/{attached,detached}.jsonl
    pages/                      per-page slices, indexed by top-level frameNavigated boundaries
      000/                      first concrete page
        url.txt                 the URL for this page
        summary.json            this page's domains/network/timing block (same shape as a pages[] entry)
        raw.jsonl               firehose scoped to this page
        network/, console/, page/, runtime/, log/, target/, dom/    same buckets, only non-empty files
  screenshots/<iso-ts>.png      one PNG per sample interval
  dom/<iso-ts>.html             one HTML dump per sample interval
  browserbase/                  added by bb-finalize.mjs (Browserbase runs only)
    session.json                final `browse cloud sessions get` snapshot (proxyBytes, status, ended_at, …)
    logs.json                   `browse cloud sessions logs` output (often [])
    downloads.zip               `browse cloud sessions downloads get` output (only if the session downloaded files)

When a run was started via bb-capture.mjs, manifest.json also carries a top-level browserbase block: session_id, project_id, region, started_at, expires_at, keep_alive, debugger_url.

Summary shape

cdp/summary.json is the entry point for any analysis: it has session-level totals and a pages[] array indexed by top-level Page.frameNavigated. Per-page entries are emitted in navigation order (page 0 = first concrete URL).

{
  "sessionId": "45f28023-…",
  "duration": { "startMs": 1777312533000, "endMs": 1777312609000, "totalMs": 76000 },
  "totalEvents": 420,
  "pages": [
    {
      "pageId": 0,
      "url": "https://example.com/",
      "startMs": 1777312533000, "endMs": 1777312538886, "durationMs": 5886,
      "eventCount": 60,
      "domains": {
        "Network": { "count": 18, "errors": 1 },
        "Console": { "count": 2 },
        "Page":    { "count": 24 },
        "Runtime": { "count": 13 }
      },
      "network": { "requests": 4, "failed": 1, "byType": { "Document": 2, "Script": 1, "Other": 1 } }
    }
  ]
}

startMs / endMs / durationMs are wall-clock ms, derived from manifest.started_at plus the offset of each event's CDP monotonic timestamp. domains[*] only includes errors/warnings keys when non-zero.

Drilling in with `query.mjs`

For interactive exploration, use scripts/query.mjs <run-id> <command> instead of remembering paths:

node scripts/query.mjs my-run list                    # one-line table of pages
node scripts/query.mjs my-run page 1                  # full summary for page 1
node scripts/query.mjs my-run page 1 network/failed   # cat failed.jsonl for page 1
node scripts/query.mjs my-run errors                  # all errors across pages, attributed by pid
node scripts/query.mjs my-run errors 2                # errors from page 2 only
node scripts/query.mjs my-run hosts                   # top hosts by request count
node scripts/query.mjs my-run host api.example.com    # all requests/responses for a host
node scripts/query.mjs my-run summary                 # full summary.json

Behind the scenes it just reads cdp/summary.json and the cdp/pages/<pid>/ tree — feel free to bypass it with raw jq/rg once you know the shape.

Top traversal recipes

# All failed network requests (use jq -c to keep it line-delimited)
jq -c '.params' .o11y/<run>/cdp/network/failed.jsonl

# Find requests to a specific host
jq -c 'select(.params.request.url | test("api\\.example\\.com"))' \
  .o11y/<run>/cdp/network/requests.jsonl

# 4xx/5xx responses
jq -c 'select(.params.response.status >= 400)
       | {status: .params.response.status, url: .params.response.url}' \
  .o11y/<run>/cdp/network/responses.jsonl

# Console errors only
jq -c 'select(.params.type == "error")' .o11y/<run>/cdp/console/logs.jsonl

# Sequence of URLs visited
jq -r '.params.frame.url' .o11y/<run>/cdp/page/navigations.jsonl

# Find the screenshot taken closest to a timestamp (e.g., when an exception fired)
ls .o11y/<run>/screenshots/ | sort | awk -v t=20260427T1714123NZ '
  $0 >= t { print; exit }'

See REFERENCE.md for the full jq recipe library and a method-by-method bisect map. See EXAMPLES.md for end-to-end debug scenarios.

Best practices

Use bb-capture.mjs on Browserbase: it enforces --keep-alive, fetches the connectUrl, captures the debugger URL, and stamps the manifest. Doing it manually invites mistakes.
Don't --release a session you don't own: bb-finalize.mjs --release is for sessions you created with --new. When attaching to a production session via bb-capture.mjs <session-id>, run bb-finalize.mjs without --release so the original automation keeps running.
Order matters for remote: on Browserbase, attach the main automation client before (or together with) the tracer, and create the session with --keep-alive. Otherwise the session ends as soon as the tracer's WS closes.
Don't poll faster than ~1s: each sample runs browser CLI read commands and screenshots Chrome. 2s is a good default.
Pick domains deliberately: defaults (Network Console Runtime Log Page) cover most debugging. Add DOM for DOM-tree mutations (very noisy) via O11Y_DOMAINS="$O11Y_DOMAINS DOM".
Reuse one Browserbase session for the automation client on remote by attaching to that session's connectUrl with browse open ... --cdp "$CONNECT_URL" --session <name>. The --session flag names the local browse daemon; it is not a Browserbase session attach flag.
Always run stop-capture.mjs, even after a crash, so background processes don't linger and the manifest gets stopped_at.
Bisect once per run: bisect-cdp.mjs is idempotent — it overwrites the per-bucket files from raw.ndjson each time.

Troubleshooting

browse cdp exited immediately: usually means the target is unreachable (wrong port) or the Browserbase session has already ended. For remote, verify with browse cloud sessions get <id> — if status is COMPLETED, recreate with --keep-alive and attach automation first.
Empty raw.ndjson even though processes are running: confirm a CDP client is actually driving the page. The tracer only emits events that the browser generates, so an idle browser produces ~5 lines of attach/discover messages and nothing else.
Screenshots all look identical: check index.jsonl — if url doesn't change, the page hasn't navigated yet. The polling loop runs independently of the main automation's pace.
Browserbase session ends mid-run: it likely hit --timeout. Recreate with a higher timeout (BB_SESSION_TIMEOUT=1800 node scripts/bb-capture.mjs --new ...) or remove the timeout flag.
bb-capture.mjs <id> says "not RUNNING": the session you tried to attach to ended. List candidates with browse cloud sessions list | jq '.[] | select(.status == "RUNNING")' and try again.
browserbase/logs.json is empty []: expected — browse cloud sessions logs is sparse in practice. The CDP firehose in cdp/raw.ndjson is the source of truth.
Where's the session recording (rrweb)?: session replay artifact fetching is deprecated; this skill doesn't fetch it. Use the screenshot stream in screenshots/ and DOM dumps in dom/.

For full reference, see REFERENCE.md. For example debug runs, see EXAMPLES.md.

When it activates

Automatically, when a task matches. Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page summaries back into an agent loop so its next iteration learns from the last one.

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 v4 — 8/1/2026

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

mxyhi / ok-skills

browser-trace/SKILL.md

View on GitHub ↗

License MITImported 8/1/2026Version v1Bundled 8 files · scripts

Illustrative Want to see what mxyhi/browser-trace 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

Browser Trace

When to use

Setup check

How it works

Quickstart

Local Chrome

Browserbase remote

What you get from the Browserbase platform

Filesystem layout

Summary shape

Drilling in with query.mjs

Top traversal recipes

Best practices

Troubleshooting

When it activates

SkillSafety

Source

Related skills

More in the registry

Drilling in with `query.mjs`