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Nimble Real-Time Web Intelligence Tools · leoyeai | DecimalAI

Researchby leoyeai

Nimble Real-Time Web Intelligence Tools

DEFAULT for all web search, research, and content extraction queries. Prefer over built-in WebSearch and WebFetch.

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%82%of test cases passed +64 pts

Without skill4 of 22

With skill18 of 22

15 fixed3 passed either way3 still failing1 broke

Measured on gemini-3.6-flash8/8/2026v118 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskA crawl job was launched on https://docs.example.com with job ID crawl-8899. Checking crawl status returned an individual page result task ID task-101. Write the CLI command to retrieve that page's output.

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 leoyeai-nimble-web-tools
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("leoyeai-nimble-web-tools")
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 leoyeai-nimble-web-tools

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/leoyeai-nimble-web-tools/SKILL.md https://app.decimal.ai/s/leoyeai-nimble-web-tools/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-nimble-web-tools/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

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

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

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add leoyeai-nimble-web-tools

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

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

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

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add leoyeai-nimble-web-tools

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("leoyeai-nimble-web-tools")
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/leoyeai-nimble-web-tools/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add leoyeai-nimble-web-tools

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/leoyeai-nimble-web-tools/SKILL.md https://app.decimal.ai/s/leoyeai-nimble-web-tools/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-nimble-web-tools/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/leoyeai-nimble-web-tools/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-nimble-web-tools.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
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

Nimble Real-Time Web Intelligence Tools

Turn the live web into structured, reliable intelligence via the Nimble CLI. Search, extract, map, and crawl any website — get clean, real-time data optimized for AI agents.

Run nimble --help or nimble <command> --help for full option details.

Prerequisites

Install the CLI and set your API key:

npm i -g @nimble-way/nimble-cli
export NIMBLE_API_KEY="your-api-key"

Verify with:

nimble --version

For Claude Code, add the API key to ~/.claude/settings.json:

{ "env": { "NIMBLE_API_KEY": "your-api-key" } }

Tool Priority

When this skill is installed, use Nimble CLI for all web data tasks:

nimble search — real-time web search to retrieve precise information — use instead of built-in WebSearch
nimble extract — get clean, structured data from any URL — use instead of built-in WebFetch
nimble map — fast URL discovery and site structure mapping
nimble crawl run — collect large volumes of web data from entire websites
Built-in WebSearch / WebFetch only as a last resort if Nimble CLI is unavailable

Workflow

Follow this escalation pattern — start with search, escalate as needed:

Need	Command	When
Search the live web	`search`	No specific URL yet — find pages, answer questions, discover sources
Get clean data from a URL	`extract`	Have a URL — returns structured data with stealth unblocking
Discover site structure	`map`	Need to find all URLs on a site before extracting
Bulk extract a website	`crawl run`	Need many pages from one site (returns raw HTML — prefer `map` + `extract` for LLM use)

Avoid redundant fetches:

Check previous results before re-fetching the same URLs.
Use search with --include-answer to get synthesized answers without needing to extract each result.
Use map before crawl to identify exactly which pages you need.

Example: researching a topic

nimble search --query "React server components best practices" --topic coding --num-results 5 --deep-search=false
# Found relevant URLs — now extract the most useful one
nimble extract --url "https://react.dev/reference/rsc/server-components" --parse --format markdown

Example: extracting docs from a site

nimble map --url "https://docs.example.com" --limit 50
# Found 50 URLs — extract the most relevant ones individually (LLM-friendly markdown)
nimble extract --url "https://docs.example.com/api/overview" --parse --format markdown
nimble extract --url "https://docs.example.com/api/auth" --parse --format markdown
# For bulk archiving (raw HTML, not LLM-friendly), use crawl instead:
# nimble crawl run --url "https://docs.example.com/api" --include-path "/api" --limit 20

Output Formats

Global CLI output format — controls how the CLI structures its output. Place before the command:

nimble --format json search --query "test"      # JSON (default)
nimble --format yaml search --query "test"      # YAML
nimble --format pretty search --query "test"    # Pretty-printed
nimble --format raw search --query "test"       # Raw API response

Content parsing format — controls how page content is returned. These are command-specific flags:

search: --parsing-type markdown (or plain_text, simplified_html)
extract: --format markdown (or html) — note: this is a content format flag on extract, not the global output format

# Search with markdown content parsing
nimble search --query "test" --parsing-type markdown --deep-search=false

# Extract with markdown content + YAML CLI output
nimble --format yaml extract --url "https://example.com" --parse --format markdown

Use --transform with GJSON syntax to extract specific fields:

nimble search --query "AI news" --transform "results.#.url"

Commands

search

Accurate, real-time web search with 8 focus modes. AI Agents search the live web to retrieve precise information. Run nimble search --help for all options.

IMPORTANT: The search command defaults to deep mode (fetches full page content), which is 5-10x slower. Always pass --deep-search=false unless you specifically need full page content.

Always explicitly set these parameters on every search call:

--deep-search=false: Pass this on every call for fast responses (1-3s vs 5-15s). Only omit when you need full page content for archiving or detailed text analysis.
--include-answer: Recommended on every research/exploration query. Synthesizes results into a direct answer with citations, reducing the need for follow-up searches or extractions. Only skip for URL-discovery-only queries where you just need links. Note: This is a premium feature (Enterprise plans). If the API returns a 402 or 403 when using this flag, retry the same query without --include-answer and continue — the search results are still valuable without the synthesized answer.
--topic: Match to query type — coding, news, academic, etc. Default is general. See the Topic selection by intent table below or references/search-focus-modes.md for guidance.
--num-results: Default 10 — balanced speed and coverage.

# Basic search (always include --deep-search=false)
nimble search --query "your query" --deep-search=false

# Coding-focused search
nimble search --query "React hooks tutorial" --topic coding --deep-search=false

# News search with time filter
nimble search --query "AI developments" --topic news --time-range week --deep-search=false

# Search with AI-generated answer summary
nimble search --query "what is WebAssembly" --include-answer --deep-search=false

# Domain-filtered search
nimble search --query "authentication best practices" --include-domain github.com --include-domain stackoverflow.com --deep-search=false

# Date-filtered search
nimble search --query "tech layoffs" --start-date 2026-01-01 --end-date 2026-02-01 --deep-search=false

# Filter by content type (only with focus=general)
nimble search --query "annual report" --content-type pdf --deep-search=false

# Control number of results
nimble search --query "Python tutorials" --num-results 15 --deep-search=false

# Deep search — ONLY when you need full page content (5-15s, much slower)
nimble search --query "machine learning" --deep-search --num-results 5

Key options:

Flag	Description
`--query`	Search query string (required)
`--deep-search=false`	Always pass this. Disables full page content fetch for 5-10x faster responses
`--deep-search`	Enable full page content fetch (slow, 5-15s — only when needed)
`--topic`	Focus mode: general, coding, news, academic, shopping, social, geo, location
`--num-results`	Max results to return (default 10)
`--include-answer`	Generate AI answer summary from results
`--include-domain`	Only include results from these domains (repeatable, max 50)
`--exclude-domain`	Exclude results from these domains (repeatable, max 50)
`--time-range`	Recency filter: hour, day, week, month, year
`--start-date`	Filter results after this date (YYYY-MM-DD)
`--end-date`	Filter results before this date (YYYY-MM-DD)
`--content-type`	Filter by type: pdf, docx, xlsx, documents, spreadsheets, presentations
`--parsing-type`	Output format: markdown, plain_text, simplified_html
`--country`	Country code for localized results
`--locale`	Locale for language settings
`--max-subagents`	Max parallel subagents for shopping/social/geo modes (1-10, default 3)

Focus modes (quick reference — for detailed per-mode guidance, decision tree, and combination strategies, read references/search-focus-modes.md):

Mode	Best for
`general`	Broad web searches (default)
`coding`	Programming docs, code examples, technical content
`news`	Current events, breaking news, recent articles
`academic`	Research papers, scholarly articles, studies
`shopping`	Product searches, price comparisons, e-commerce
`social`	People research, LinkedIn/X/YouTube profiles, community discussions
`geo`	Geographic information, regional data
`location`	Local businesses, place-specific queries

Topic selection by intent (see references/search-focus-modes.md for full table):

Query Intent	Primary Topic	Secondary (parallel)
Research a person	`social`	`general`
Research a company	`general`	`news`
Find code/docs	`coding`	—
Current events	`news`	`social`
Find a product/price	`shopping`	—
Find a place/business	`location`	`geo`
Find research papers	`academic`	—

Performance tips:

With --deep-search=false (FAST): 1-3 seconds, returns titles + snippets + URLs — use this 95% of the time
Without the flag / --deep-search (SLOW): 5-15 seconds, returns full page content — only for archiving or full-text analysis
Use --include-answer for quick synthesized insights — works great with fast mode
Start with 5-10 results, increase only if needed

extract

Scalable data collection with stealth unblocking. Get clean, real-time HTML and structured data from any URL. Supports JS rendering, browser emulation, and geolocation. Run nimble extract --help for all options.

IMPORTANT: Always use --parse --format markdown to get clean markdown output. Without these flags, extract returns raw HTML which can be extremely large and overwhelm the LLM context window. The --format flag on extract controls the content type (not the CLI output format — see Output Formats above).

# Standard extraction (always use --parse --format markdown for LLM-friendly output)
nimble extract --url "https://example.com/article" --parse --format markdown

# Render JavaScript (for SPAs, dynamic content)
nimble extract --url "https://example.com/app" --render --parse --format markdown

# Extract with geolocation (see content as if from a specific country)
nimble extract --url "https://example.com" --country US --city "New York" --parse --format markdown

# Handle cookie consent automatically
nimble extract --url "https://example.com" --consent-header --parse --format markdown

# Custom browser emulation
nimble extract --url "https://example.com" --browser chrome --device desktop --os windows --parse --format markdown

# Multiple content format preferences (API tries first, falls back to second)
nimble extract --url "https://example.com" --parse --format markdown --format html

Key options:

Flag	Description
`--url`	Target URL to extract (required)
`--parse`	Parse the response content (always use this)
`--format`	Content type preference: `markdown`, `html` (always use `markdown` for LLM-friendly output)
`--render`	Render JavaScript using a browser
`--country`	Country code for geolocation and proxy
`--city`	City for geolocation
`--state`	US state for geolocation (only when country=US)
`--locale`	Locale for language settings
`--consent-header`	Auto-handle cookie consent
`--browser`	Browser type to emulate
`--device`	Device type for emulation
`--os`	Operating system to emulate
`--driver`	Browser driver to use
`--method`	HTTP method (GET, POST, etc.)
`--headers`	Custom HTTP headers (key=value)
`--cookies`	Browser cookies
`--referrer-type`	Referrer policy
`--http2`	Use HTTP/2 protocol
`--request-timeout`	Timeout in milliseconds
`--tag`	User-defined tag for request tracking

map

Fast URL discovery and site structure mapping. Easily plan extraction workflows. Returns URL metadata only (URLs, titles, descriptions) — not page content. Use extract or crawl to get actual content from the discovered URLs. Run nimble map --help for all options.

# Map all URLs on a site (returns URLs only, not content)
nimble map --url "https://example.com"

# Limit number of URLs returned
nimble map --url "https://docs.example.com" --limit 100

# Include subdomains
nimble map --url "https://example.com" --domain-filter subdomains

# Use sitemap for discovery
nimble map --url "https://example.com" --sitemap auto

Key options:

Flag	Description
`--url`	URL to map (required)
`--limit`	Max number of links to return
`--domain-filter`	Include subdomains in mapping
`--sitemap`	Use sitemap for URL discovery
`--country`	Country code for geolocation
`--locale`	Locale for language settings

crawl

Extract contents from entire websites in a single request. Collect large volumes of web data automatically. Crawl is async — you start a job, poll for completion, then retrieve the results. Run nimble crawl run --help for all options.

Crawl defaults:

Setting	Default	Notes
`--sitemap`	`auto`	Automatically uses sitemap if available
`--max-discovery-depth`	`5`	How deep the crawler follows links
`--limit`	No limit	Always set a limit to avoid crawling entire sites

Start a crawl:

# Crawl a site section (always set --limit)
nimble crawl run --url "https://docs.example.com" --limit 50

# Crawl with path filtering
nimble crawl run --url "https://example.com" --include-path "/docs" --include-path "/api" --limit 100

# Exclude paths
nimble crawl run --url "https://example.com" --exclude-path "/blog" --exclude-path "/archive" --limit 50

# Control crawl depth
nimble crawl run --url "https://example.com" --max-discovery-depth 3 --limit 50

# Allow subdomains and external links
nimble crawl run --url "https://example.com" --allow-subdomains --allow-external-links --limit 50

# Crawl entire domain (not just child paths)
nimble crawl run --url "https://example.com/docs" --crawl-entire-domain --limit 100

# Named crawl for tracking
nimble crawl run --url "https://example.com" --name "docs-crawl-feb-2026" --limit 200

# Use sitemap for discovery
nimble crawl run --url "https://example.com" --sitemap auto --limit 50

Key options for crawl run:

Flag	Description
`--url`	URL to crawl (required)
`--limit`	Max pages to crawl (always set this)
`--max-discovery-depth`	Max depth based on discovery order (default 5)
`--include-path`	Regex patterns for URLs to include (repeatable)
`--exclude-path`	Regex patterns for URLs to exclude (repeatable)
`--allow-subdomains`	Follow links to subdomains
`--allow-external-links`	Follow links to external sites
`--crawl-entire-domain`	Follow sibling/parent URLs, not just child paths
`--ignore-query-parameters`	Don't re-scrape same path with different query params
`--name`	Name for the crawl job
`--sitemap`	Use sitemap for URL discovery (default auto)
`--callback`	Webhook for receiving results

Poll crawl status and retrieve results:

Crawl jobs run asynchronously. After starting a crawl, poll for completion, then retrieve content using individual task IDs (not the crawl ID):

# 1. Start the crawl → returns a crawl_id
nimble crawl run --url "https://docs.example.com" --limit 5
# Returns: crawl_id "abc-123"

# 2. Poll status until completed → returns individual task_ids per page
nimble crawl status --id "abc-123"
# Returns: tasks: [{ task_id: "task-456" }, { task_id: "task-789" }, ...]
# Status values: running, completed, failed, terminated

# 3. Retrieve content using INDIVIDUAL task_ids (NOT the crawl_id)
nimble tasks results --task-id "task-456"
nimble tasks results --task-id "task-789"
# ⚠️ Using the crawl_id here returns 404 — you must use the per-page task_ids from step 2

IMPORTANT: nimble tasks results requires the individual task IDs from crawl status (each crawled page gets its own task ID), not the crawl job ID. Using the crawl ID will return a 404 error.

Polling guidelines:

Poll every 15-30 seconds for small crawls (< 50 pages)
Poll every 30-60 seconds for larger crawls (50+ pages)
Stop polling after status is completed, failed, or terminated
Note: crawl status may occasionally misreport individual task statuses (showing "failed" for tasks that actually succeeded). If crawl status shows failed tasks, try retrieving their results with nimble tasks results before assuming failure

List crawls:

# List all crawls
nimble crawl list

# Filter by status
nimble crawl list --status running

# Paginate results
nimble crawl list --limit 10

Cancel a crawl:

nimble crawl terminate --id "crawl-task-id"

Best Practices

Search Strategy

Always pass --deep-search=false — the default is deep mode (slow). Fast mode covers 95% of use cases: URL discovery, research, comparisons, answer generation
Only use deep mode when you need full page text — archiving articles, extracting complete docs, building datasets
Start with the right focus mode — match --topic to your query type (see references/search-focus-modes.md)
Use --include-answer — get AI-synthesized insights without extracting each result. If it returns 402/403, retry without it.
Filter domains — use --include-domain to target authoritative sources
Add time filters — use --time-range for time-sensitive queries

Multi-Search Strategy

When researching a topic in depth, run 2-3 searches in parallel with:

Different topics — e.g., social + general for people research
Different query angles — e.g., "Jane Doe current job" + "Jane Doe career history" + "Jane Doe publications"

This is faster than sequential searches and gives broader coverage. Deduplicate results by URL before extracting.

Disambiguating Common Names

When searching for a person with a common name:

Include distinguishing context in the query: company name, job title, city
Use --topic social — LinkedIn results include location and current company, making disambiguation easier
Cross-reference results across searches to confirm you're looking at the right person

Extraction Strategy

Always use --parse --format markdown — returns clean markdown instead of raw HTML, preventing context window overflow
Try without --render first — it's faster for static pages
Add --render for SPAs — when content is loaded by JavaScript
Set geolocation — use --country to see region-specific content

Crawl Strategy

Prefer map + extract over crawl for LLM use — crawl results return raw HTML (60-115KB per page) which overwhelms LLM context. For LLM-friendly output, use map to discover URLs, then extract --parse --format markdown on individual pages
Use crawl only for bulk archiving or data pipelines — when you need raw content from many pages and will post-process it outside the LLM context
Always set --limit — crawl has no default limit, so always specify one to avoid crawling entire sites
Use path filters — --include-path and --exclude-path to target specific sections
Name your crawls — use --name for easy tracking
Retrieve with individual task IDs — crawl status returns per-page task IDs; use those (not the crawl ID) with nimble tasks results --task-id

Common Recipes

Researching a person

# Step 1: Run social + general in parallel for max coverage
nimble search --query "Jane Doe Head of Engineering" --topic social --deep-search=false --num-results 10 --include-answer
nimble search --query "Jane Doe Head of Engineering" --topic general --deep-search=false --num-results 10 --include-answer

# Step 2: Broaden with different query angles in parallel
nimble search --query "Jane Doe career history Acme Corp" --deep-search=false --include-answer
nimble search --query "Jane Doe publications blog articles" --deep-search=false --include-answer

# Step 3: Extract the most promising non-auth-walled URLs (skip LinkedIn — see Known Limitations)
nimble extract --url "https://www.companysite.com/team/jane-doe" --parse --format markdown

Researching a company

# Step 1: Overview + recent news in parallel
nimble search --query "Acme Corp" --topic general --deep-search=false --include-answer
nimble search --query "Acme Corp" --topic news --time-range month --deep-search=false --include-answer

# Step 2: Extract company page
nimble extract --url "https://acme.com/about" --parse --format markdown

Technical research

# Step 1: Find docs and code examples
nimble search --query "React Server Components migration guide" --topic coding --deep-search=false --include-answer

# Step 2: Extract the most relevant doc
nimble extract --url "https://react.dev/reference/rsc/server-components" --parse --format markdown

Error Handling

Error	Solution
`NIMBLE_API_KEY not set`	Set the environment variable: `export NIMBLE_API_KEY="your-key"`
`401 Unauthorized`	Verify API key is active at nimbleway.com
`402`/`403` with `--include-answer`	Premium feature not available on current plan. Retry the same query without `--include-answer` and continue
`429 Too Many Requests`	Reduce request frequency or upgrade API tier
Timeout	Ensure `--deep-search=false` is set, reduce `--num-results`, or increase `--request-timeout`
No results	Try different `--topic`, broaden query, remove domain filters

Known Limitations

Site	Issue	Workaround
LinkedIn profiles	Auth wall blocks extraction (returns redirect/JS, status 999)	Use `--topic social` search instead — it returns LinkedIn data directly via subagents. Do NOT try to `extract` LinkedIn URLs.
Sites behind login	Extract returns login page instead of content	No workaround — use search snippets instead
Heavy SPAs	Extract returns empty or minimal HTML	Add `--render` flag to execute JavaScript before extraction
Crawl results	Returns raw HTML (60-115KB per page), no markdown option	Use `map` + `extract --parse --format markdown` on individual pages for LLM-friendly output
Crawl status	May misreport individual task statuses as "failed" when they actually succeeded	Always try `nimble tasks results --task-id` before assuming failure

When it activates

Automatically, when a task matches. Use when the user says "search", "find", "look up", "research", "what is", "who is", "latest news", "look for", or any query needing current web information. Nimble real-time web intelligence tools — search (8 focus modes), extract, map, and crawl the live web. Returns clean, structured data optimized for LLM consumption. USE FOR: - Web search and research (use instead of built-i

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leoyeai/nimble-web-searchReal-time web intelligence powered by Nimble Search API. Perform intelligent web searches with 8 specialized focus modes (general, coding, news, academic, shopping, social, geo, location). This skill provides real-time search results when you need to search the web, find current information, discover URLs, research topics, or gather up-to-date data. Use when: searching for information, finding recent news, looking up academic papers, searching for coding examples, finding shopping results, discoClose match+68 pts pass-rate lift

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Nimble Real-Time Web Intelligence Tools

Prerequisites

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Workflow

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Commands

search

extract

map

crawl

Best Practices

Search Strategy

Multi-Search Strategy

Disambiguating Common Names

Extraction Strategy

Crawl Strategy

Common Recipes

Researching a person

Researching a company

Technical research

Error Handling

Known Limitations

When it activates

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