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Deepsearch Mpro · leoyeai | DecimalAI

Researchby leoyeai

Deepsearch Mpro

专业深度研究与报告生成技能。支持企业竞争分析、产品竞争分析、行业分析、市场规模/竞争格局、AI大模型厂商、AI工具学习指南等领域。整合17个搜索引擎，三阶段工作流（主题确认→框架生成→报告输出），运用PESTEL、SWOT、波特五力、商业模式画布等经典咨询研究模型，输出精美的深蓝色政务风格HTML与Markdown双格式咨询级报告。

Never made a case worse

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

86%100%of test cases passed +14 pts

Without skill19 of 22

With skill22 of 22

3 fixed19 passed either way0 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task在收集研报所需的关键数据时，为了保障搜索效率与覆盖面，多层搜索策略从优先尝试到最终备选的四层执行顺序是怎样的？

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-deepsearch-mpro
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-deepsearch-mpro")
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-deepsearch-mpro

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-deepsearch-mpro/SKILL.md https://app.decimal.ai/s/leoyeai-deepsearch-mpro/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-deepsearch-mpro/SKILL.md

This gets SKILL.md only. The skill's 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/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-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/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-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro")
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-deepsearch-mpro/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-deepsearch-mpro/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 9 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 leoyeai-deepsearch-mpro

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-deepsearch-mpro/SKILL.md https://app.decimal.ai/s/leoyeai-deepsearch-mpro/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-deepsearch-mpro/SKILL.md

This gets SKILL.md only. The skill's 9 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/leoyeai-deepsearch-mpro/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-deepsearch-mpro.json

This gets SKILL.md only. The skill's 9 other files are 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

专业研究报告技能

概述

该技能用于生成专业、咨询级别的研究报告，覆盖市场分析、AI 大模型厂商、SaaS 厂商、AI 工具学习指南、竞争情报、行业研究等领域。

核心能力

✅ 多源搜索引擎：17个搜索引擎（8个国内 + 9个国际），无需 API 密钥
✅ 三阶段工作流：主题理解与确认 + 分析框架生成 + 报告生成
✅ 交互式确认：支持用户确认、修改、提问等交互式反馈
✅ 双格式输出：Markdown + HTML，深蓝色政务风格

何时使用本技能

用户请求市场分析、消费者洞察报告、财务分析、行业研究
用户输入具体 AI 工具名称，需要生成学习指南（如"Cursor 学习指南"）
用户需要专业的咨询风格研究报告
用户提供研究主题，并在数据收集前需要结构化分析框架

整合的技能与能力

本技能整合了以下核心搜索能力，提供多源数据搜索与分析支持：

整合来源	路径	能力说明	版权归属
ddg-web-search	`ddg-web-search/`	DuckDuckGo 网络搜索能力，提供全球范围内的实时搜索支持	原作者所有
multi-search-engine	`multi-search-engine/`	多搜索引擎聚合能力，整合17个搜索引擎（8个国内 + 9个国际），无需 API 密钥	原作者所有

能力集成说明：

本技能已将上述搜索能力整合为核心功能

三阶段工作流

阶段 0：主题理解与确认

目的：确保正确理解用户的研究需求，避免方向偏差。

工作流程：

解析用户输入
- 识别研究主题、分析领域、搜索范围、特定角度
推断分析领域

根据主题关键词推断分析领域，加载对应的领域文档：

主题类型	关键词示例	领域文档
行业分析	"{行业名}行业分析"、"ERP行业趋势"、"AI Agent行业"	`references/domains/industry-analysis.md`
企业竞争分析	"{公司名}分析"、"{公司名}竞争分析"、"{公司名}研究"、金蝶、用友、SAP等	`references/domains/company-analysis.md`
产品竞争分析	"{产品名}分析"、"{产品名}研究"、"{产品名}竞争力"	`references/domains/product-analysis.md`
市场规模/竞争格局	"市场规模分析"、"竞争格局研究"、"市场机会评估"	`references/domains/market-analysis.md`
AI 大模型厂商	OpenAI、Anthropic、DeepSeek、智谱AI等	`references/domains/ai-vendor-analysis.md`
AI 工具学习指南	"AI工具使用指南"、"Gemini教程"	`references/domains/ai-tool-learning-guide-framework.md`

推断逻辑：

行业关键词（行业分析、行业趋势、行业研究）→ 行业分析
企业名称 + 分析/研究 → 企业竞争分析
产品名称 + 分析/研究 → 产品竞争分析
市场规模/竞争格局关键词（市场规模、竞争格局、市场机会）→ 市场规模/竞争格局分析
AI 厂商名（OpenAI、DeepSeek等）→ AI 厂商分析
生成确认提示

向用户展示以下确认项，等待用户确认或修改：

确认项	说明	示例
研究主题	解析后的核心研究对象	"金蝶企业竞争分析"
研究方向	分析领域及分析角度	"企业竞争分析：商业模式 + 竞争格局 + 近期动态"
时间范围	数据收集的时间窗口	"近一周" / "近一月" / "自定义（如2024Q1）"

时间范围说明：

近一周：聚焦最新动态、热点事件、近期发布
近一月：平衡时效性与深度，适合大多数分析
自定义：特定时间段（如财报季、产品发布周期）

时间参数传递：

时间范围确认后，记录为全局参数 search_time_range
在阶段1数据收集时，自动转换为搜索时间过滤条件
示例：search_time_range = "近一周" → 搜索引擎添加 &before=2026-03-18&after=2026-03-11

处理反馈
- 用户确认 → 记录时间参数，进入阶段 1
- 用户修改 → 更新确认项后重新确认
- 用户提问 → 解答后重新确认

详细流程和示例：见 references/workflow/phase0-details.md

阶段 1：分析框架生成

目的：生成完整的分析框架，作为后续数据收集与报告生成的蓝图。

工作流程：

理解研究主题
- 识别核心对象和分析领域
- 加载对应的领域文档（references/domains/*.md）
选择分析模型
- 根据领域文档推荐，按需加载模型文件（references/models/*.md）
- 每次研究只加载 2-4 个模型，避免堆砌
- 模型索引：references/models/README.md
设计章节骨架
- 每章包含：分析目标、分析逻辑、核心假设
- 典型结构：3-5 个主要章节
定义数据需求
- 每条需求包含：指标、类型、来源、搜索方法、优先级
- P0 数据必须收集，P1 重要，P2 补充
定义可视化方案
- 为每章定义图表类型和数据来源

详细流程和示例：见 references/workflow/phase1-details.md

阶段 2：报告生成

目的：将分析框架和数据整合为最终的咨询级报告。

工作流程：

接收并校验输入
- 确认分析框架和数据包齐全
- 检查 P0 数据完整性
映射报告结构
- Abstract → Introduction → Body Chapters → Conclusion → References
撰写报告
- 遵循"视觉锚点 → 数据对比 → 综合分析"流程
- 每个小节以充分的分析段落结尾（≥200字）
撰写摘要和结论
- 摘要：3-5 句话，200-300 字
- 结论：纯客观综合判断，不使用 bullet points
整理参考文献
- 按 GB/T 7714-2015 格式列出
生成输出文件
- Markdown 文件：assets/report-template.md
- HTML 文件：根据分析模型组合模块化模板

HTML 模板系统：

位于 assets/templates/ 目录，提供 10 个模块化模板：

模板文件	分析模型
`product-overview-template.html`	产品概览
`target-users-template.html`	目标用户分析
`core-features-template.html`	核心功能
`business-model-canvas-template.html`	商业模式画布
`porter-five-forces-template.html`	波特五力分析
`swot-analysis-template.html`	SWOT 分析
`pestel-analysis-template.html`	PESTEL 分析
`competitor-matrix-template.html`	竞品对比矩阵
`timeline-template.html`	关键时间线
`key-metrics-template.html`	关键指标

模板组合建议：

研究类型	推荐模板组合
AI 厂商/产品	产品概览 → PESTEL → 目标用户 → 竞品矩阵 → 商业模式画布 → SWOT → 核心功能
市场竞争	产品概览 → PESTEL → 波特五力 → 竞品矩阵 → 关键指标
商业模式	产品概览 → PESTEL → 商业模式画布 → SWOT
行业研究	产品概览 → PESTEL → 波特五力 → 关键指标 → 时间线

详细流程和示例：见 references/workflow/phase2-details.md

数据收集策略

多层搜索方案（优先级顺序）

第一层：Agent 内置 web_search（优先使用）

快速获取基础信息
参数：query、max_results（建议 3-10）
适用场景：初步数据获取、验证数据是否存在

第二层：web_fetch 深度搜索

直接访问搜索引擎获取详细结果
中文市场：百度、微信、头条
国际市场：Google、Bing
政府数据：site:gov.cn
行业报告：filetype:pdf
详细使用方法：见 references/technical/search-engines.md

第三层：multi-search-engine 备用方案（如前两层无法获取数据）

整合 17 个搜索引擎（8个国内 + 9个国际）
无需 API 密钥
支持高级搜索操作符和时间过滤
适用场景：当 web_search 和 web_fetch 都无法获取数据时

第四层：ddg-web-search 最终备选

DuckDuckGo Lite 搜索
零依赖，仅需 web_fetch 工具
适用场景：当所有搜索引擎都无法访问时的最终备选

搜索策略执行逻辑

对于每个数据需求（P0/P1/P2）：

1. 先使用 web_search 快速获取
   - 成功：进入数据提取
   - 失败：进入第2层

2. 使用 web_fetch 深度搜索
   - 成功：进入数据提取
   - 失败：进入第3层

3. 使用 multi-search-engine 备用方案
   - 根据数据类型选择合适引擎：
     * 中文市场 → 百度、微信、头条
     * 国际市场 → Google、Bing
     * 政府数据 → site:gov.cn
     * 行业报告 → filetype:pdf
   - 成功：进入数据提取
   - 失败：进入第4层

4. 使用 ddg-web-search 最终备选
   - 成功：进入数据提取
   - 失败：标注"数据暂不可得"

数据提取：
- 从搜索结果中提取关键数据
- 多源交叉验证（至少2个来源）
- 标注数据可信度（high/medium/low）

内置搜索能力说明

本技能已内置整合以下搜索能力，用户无需额外安装任何子技能：

搜索层	能力来源	说明
第1层	Agent 内置 `web_search`	默认优先使用
第2层	Agent 内置 `web_fetch`	深度搜索
第3层	已整合 `multi-search-engine`	17个搜索引擎，无需安装
第4层	已整合 `ddg-web-search`	DuckDuckGo Lite，无需安装

子技能已整合：

./multi-search-engine/ 和 ./ddg-web-search/ 的能力已完全整合进本技能
所有搜索引擎的 URL 模板和使用方法见 references/technical/search-engines.md
四层搜索策略的详细流程见 references/technical/multi-layer-search-strategy.md

数据真实性协议

严格遵循规则：报告中呈现的所有数据，必须直接来源于提供的 Data Summary 或 External Search Findings。

禁止幻觉：不得编造、估算或模拟数据
可追溯来源：每个重要结论和图表都必须能够追溯到输入的数据包
数据缺失处理：如数据缺失，明确写出"数据暂不可得"

输出格式

Markdown 文件

文件名：{主题关键词}-report-{日期}.md
模板：assets/report-template.md

HTML 文件

文件名：{主题关键词}-report-{日期}.html
模板：assets/html-template.html
风格：深蓝色政务/企业内报风格
布局：左侧树状导航（固定）+ 右侧内容展示

格式与语气标准

语气：麦肯锡 / BCG 风格 —— 权威、客观、专业
数字格式：千位分隔使用英文逗号（1,000）
数据强调：重要观点和关键数字需加粗
标题编号：使用标准编号（1.、1.1）
参考文献：必须严格遵循 GB/T 7714-2015

参考文档体系

领域分析框架

文档	说明
`references/domains/industry-analysis.md`	行业分析框架（PESTEL + 波特五力 + 价值链）
`references/domains/company-analysis.md`	企业竞争分析框架（含 SaaS 厂商分析，商业模式画布 + SWOT + 竞品矩阵）
`references/domains/product-analysis.md`	产品竞争分析框架（目标用户 + 竞品矩阵 + 核心功能）
`references/domains/market-analysis.md`	市场规模/竞争格局分析框架（TAM-SAM-SOM + 波特五力）
`references/domains/ai-vendor-analysis.md`	AI 大模型厂商分析框架
`references/domains/ai-tool-learning-guide-framework.md`	AI 工具学习指南生成框架
`references/domains/hotspot-analysis.md`	热点分析公共模块（企业/产品/AI/SaaS/全球AI热点）

工作流程详细文档

文档	说明
`references/workflow/phase0-details.md`	阶段0 详细流程和示例
`references/workflow/phase1-details.md`	阶段1 详细流程和示例
`references/workflow/phase2-details.md`	阶段2 详细流程和示例
`references/workflow/examples-complete.md`	完整示例集（5个场景）

技术指南

文档	说明
`references/technical/multi-layer-search-strategy.md`	四层搜索策略详细指南
`references/technical/search-engines.md`	多源搜索引擎使用指南
`references/technical/data-quality-guidelines.md`	数据质量控制标准
`references/technical/format-conversion.md`	格式转换指南

HTML 模板

文档	说明
`assets/templates/README.md`	模板使用说明
`assets/templates/*.html`	10 个模块化 HTML 模板

领域分析框架（重复）

文档	说明
`references/domains/industry-analysis.md`	行业分析框架（PESTEL + 波特五力 + 价值链）
`references/domains/company-analysis.md`	企业竞争分析框架（商业模式画布 + SWOT + 竞品矩阵）
`references/domains/product-analysis.md`	产品竞争分析框架（目标用户 + 竞品矩阵 + 核心功能）
`references/domains/ai-vendor-analysis.md`	AI 大模型厂商分析框架
`references/domains/ai-tool-learning-guide-framework.md`	AI 工具学习指南框架
`references/domains/saas-vendor-analysis.md`	SaaS 厂商分析框架
`references/domains/market-analysis.md`	市场与竞争分析框架

分析模型库

文档	说明
`references/models/README.md`	模型索引和使用说明
`references/models/strategic/*.md`	战略模型（SWOT、PESTEL、波特五力、VRIO）
`references/models/market/*.md`	市场模型（STP、BCG、TAM-SAM-SOM）
`references/models/competitive/*.md`	竞争模型（商业模式画布、竞品矩阵、价值链）
`references/models/consumer/*.md`	消费者模型（决策旅程、AARRR、RFM）
`references/models/financial/*.md`	财务模型（杜邦分析、DCF、可比公司）

方法论

文档	说明
`references/methodology/deep-research-methodology.md`	深度研究方法论
`references/methodology/report-writing-guide.md`	报告撰写指南

质量检查清单

阶段 0（主题理解与确认）

研究主题已明确
分析领域已识别
搜索范围已确定
用户已确认

阶段 1（分析框架）

框架覆盖了该领域自然应有的全部分析维度
每章均有明确的分析目标、分析逻辑与核心假设
数据需求具体、可衡量，并指定了推荐搜索方法
每章至少有一个可视化方案

阶段 1→2（数据收集）

所有 P0 数据任务已完成
关键数据至少从 2 个来源验证
数据来源已记录
数据可信度已标注

阶段 2（报告生成）

Markdown 报告零幻觉
每个小节均遵循"数据对比 → 综合分析"
参考文献格式正确（GB/T 7714-2015）

配置项

output_locale = zh_CN  # zh/en
default_search_engines = ["baidu", "bing", "google"]
data_validation_required = true  # P0 数据必须验证
interactive_confirmation = true  # 交互式确认，支持用户修改研究参数

版本历史

v1.0.0: 正式版
- 三阶段工作流（主题确认→框架生成→报告输出）
- 整合 17 个搜索引擎（8个国内 + 9个国际）
- 支持市场分析、行业分析、AI/SaaS厂商分析、竞争情报、竞争分析等领域
- 交互式主题确认，支持用户修改研究参数
- 双格式输出（Markdown + HTML）
- 四层搜索策略（web_search → web_fetch → multi-search-engine → ddg-web-search）

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skills/deepsearch-mpro/SKILL.md

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专业研究报告技能

概述

核心能力

何时使用本技能

整合的技能与能力

三阶段工作流

阶段 0：主题理解与确认

阶段 1：分析框架生成

阶段 2：报告生成

数据收集策略

多层搜索方案（优先级顺序）

搜索策略执行逻辑

内置搜索能力说明

数据真实性协议

输出格式

Markdown 文件

HTML 文件

格式与语气标准

参考文档体系

领域分析框架

工作流程详细文档

技术指南

HTML 模板

领域分析框架（重复）

分析模型库

方法论

质量检查清单

阶段 0（主题理解与确认）

阶段 1（分析框架）

阶段 1→2（数据收集）

阶段 2（报告生成）

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