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Hehe Competitive Landscape · hexiaofeier | DecimalAI

Researchby hexiaofeier

Hehe Competitive Landscape

盒子原创行业研究 Skill 组合中的竞争格局分析专项。行业研究梳理参与企业、份额、集中度及变化，按共同维度横向比较核心企业；企业研究比较目标企业与主要对手，并单独分析目标企业的地位、优劣势、问题、痛点、机会与策略。由 hehe-industry-researcher 派发任务卡时按组合模式执行；用户直接要求“分析竞争格局”“计算市场份额或集中度”“比较龙头、挑战者或竞品”“判断竞争优势、进入壁垒或格局变化”等问题时独立启动，自行确认范围、搜索材料、分析、自审并交付专项报告。

Never made a case worseMaintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

77%91%of test cases passed +14 pts

Without skill17 of 22

With skill20 of 22

3 fixed17 passed either way2 still failing0 broke

Model 11/5/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are comparing 6 SaaS providers that clearly segment along two independent strategic axes: 'Product Scope (Single-point vs All-in-one)' and 'Go-To-Market Channel (Direct Sales vs Partner Ecosystem)'. How should visual presentation be handled?

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 hexiaofeier-hehe-competitive-landscape
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("hexiaofeier-hehe-competitive-landscape")
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 hexiaofeier-hehe-competitive-landscape

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/hexiaofeier-hehe-competitive-landscape/SKILL.md https://app.decimal.ai/s/hexiaofeier-hehe-competitive-landscape/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/hexiaofeier-hehe-competitive-landscape/SKILL.md

This gets SKILL.md only. The skill's 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/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/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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("hexiaofeier-hehe-competitive-landscape")
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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/hexiaofeier-hehe-competitive-landscape/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 3 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 hexiaofeier-hehe-competitive-landscape

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/hexiaofeier-hehe-competitive-landscape/SKILL.md https://app.decimal.ai/s/hexiaofeier-hehe-competitive-landscape/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/hexiaofeier-hehe-competitive-landscape/SKILL.md

This gets SKILL.md only. The skill's 3 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/hexiaofeier-hehe-competitive-landscape/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/hexiaofeier-hehe-competitive-landscape.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 11/5/2026
Source: hexiaofeier/hehe-industry-research-skill-pack ↗
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

竞争格局分析

本 Skill 是盒子原创行业研究 Skill 组合中负责竞争格局分析的专项 Skill，回答“谁在竞争、谁在哪些方面领先、行业格局为什么形成并如何变化”。既可研究行业参与者、份额和核心企业横向比较，也可围绕目标企业分析其竞争位置、优劣势、问题、机会与策略。

启动与工作流程

由 hehe-industry-researcher 调用： 收到任务卡和材料索引后直接读取材料并执行，不重新启动路由。
用户直接调用本 Skill： 本 Skill 单独启动，在对话中确认对象、研究范围、地域、时间、用户口径要求及已有材料，再决定是否搜索。

开始执行前读取专项工作流程，按对应路径接任务、搜索、审核、分析、自审和交付。交付时读取竞争格局分析交付模板，按模板完成本专项成果；用户明确不落文件时，在对话中交付。当企业差异适合用二维定位图或能力矩阵表达时，读取竞争定位图与能力矩阵，由本 Skill 直接完成图表判断、制作和验收，不以其他制图 Skill 为前置依赖。

完整交付原则。 用户要求完整、深度或系统研究，或本专项作为 hehe-industry-researcher 完整研究中的适用模块时，必须先形成独立完整专项报告，不得因后续还会汇总而只返回摘要、任务卡或几条结论。只有用户明确要求局部／简版，或资料只能支持有限披露时才切换输出模式，并在报告中标明；完整度按交付模板的分析单元和正文目标体量检查，交付前实际计数。

核心分析方法

1. 行业与参与企业

明确研究什么行业或细分市场、包含哪些产品与客户，以及地域、时间和相关渠道。不同细分市场的客户与竞争方式差异较大时，分开分析。

分清竞争主要是争取新增客户，还是替换已有品牌或供应商；采购、渠道准入、续费等环节不同，实际对手也可能不同。

列出主要企业，包括头部企业、主要挑战者和有实际影响的新进入者。说明各自的相关业务、代表产品、客户与区域，以及市场角色或特点。企业研究围绕目标企业的相关业务选择主要对手。

影响格局的进入、退出、并购或业务转向，注明时间和当前状态。有实质准备动作的潜在进入者可列入观察，与已经经营的企业分开。

公司、品牌与具体业务分清；不能用集团整体收入代表某项业务。
是否竞争，看是否争夺相同客户的需求或预算；产品相似、产业链相邻、合作关系不等于竞争。
客户确实会选择其他技术、自建、人工或暂不购买时，说明这些替代做法的影响，不硬列成企业。

2. 市场集中度、份额与变化

选择指标并计算

优先选择能说明本行业格局的核心指标，不要求每种都算：

指标	如何使用
收入／销售额	比较相关业务取得的收入或销售额；企业确认收入、终端零售额与平台交易额分别使用，不混算
销量	比较同类产品的销售数量；产品差异大时分品类看
用户	比较同一时间范围的付费、活跃或其他明确类型的用户；多家重叠使用时，分别报告用户覆盖率，不直接加总成集中度
出货量、订单	前者反映供货，后者反映签约或需求；作为补充口径，说明出货不等于终端销量、订单不等于已实现收入

计算时列出企业原数值、市场总量及来源；产品、地域、时间、渠道和单位保持可比，集团与旗下品牌不重复计数。

市场份额＝企业指标值÷同口径市场总量。
CRn＝同一市场实际前 n 家企业的份额之和。 按问题选择 CR3、CR5 等，不用固定阈值直接判定行业性质。
HHI＝同一市场各企业份额平方之和。 份额使用百分数时结果范围为 0—10,000，使用小数时结果范围为 0—1，计算表必须注明单位。只有市场总量和参与者覆盖能够支持同一市场份额时才计算市场 HHI；覆盖不完整但已知份额可核对时，只称“已知企业 HHI 下限”，并注明未覆盖份额。阈值解释服从本题法域、行业和用途，不用固定阈值代替格局判断。
份额变化＝本期份额－基期份额，单位为百分点。 对照同口径历史数据，区分份额上升与企业自身收入或销量增长。

市场总量未知时比较企业已有数值，不把已知企业归一到 100%；无法确认实际前 n 家时，写“已知企业份额合计”，不称 CRn。数据误差可能改变名次时说明排序不确定。并购、品牌归属或统计覆盖变化造成的增减，单独解释。

解释格局及其变化

把客户看重的因素、企业能力差异与实际购买或经营结果联系起来，解释格局形成与变化：

当前由谁主导，是少数企业集中竞争，还是多个细分市场各有强者？
领先主要靠产品、价格、渠道、客户关系、交付能力还是其他优势？是长期积累，还是阶段性供需、补贴或资金投入带来的领先？高份额是否也带来了较好的利润或现金表现？
格局正在集中、分散还是重新分配？哪些企业上升、退出或转向，价格、技术、渠道、并购或新进入者起了什么作用？
接下来什么变化可能影响领先位置？说明发生条件、受影响企业及可观察信号。

已发生的变化用前后事实支持；解释原因时考虑其他可能因素。融资、新品发布或合作公告本身不等于竞争力提升；未来影响可以讨论，但与已发生结果分开。

判断对手扩张、新进入者或替代方案的影响时，看已具备哪些产品、客户、渠道和资金能力，可能切入哪些客户或区域，仍需跨过什么成本、认证或客户切换门槛，以及现有企业可能如何应对。客户能否同时采购多家、转向替代方案，也影响领先者能否守住位置。

说明重要变化改变了哪些需求、供给、成本或渠道条件，哪些企业因此受益或受损。分清发布、实际落地和经营效果出现的时间，考虑效果滞后。再问：没有这件事，结果是否仍可能这样变化？哪些反例或后续信号会推翻当前解释？

3. 核心企业比较

以企业在本行业的相关业务为比较对象，按同一组有效维度横向比较。行业研究不设标的企业，各家平行呈现；企业研究把目标企业与主要对手放在一起比较。

从下表选择影响本行业竞争的维度，也可补充行业特有维度，不要求全部使用。既看企业资源、能力与经营表现，也看客户购买、续购或更换供应商时看重什么。

常用方向	可比较的内容
企业背景与资本	创始人、发展背景、股东与投资者、融资及可用资源
团队与组织	核心团队经验、人员结构、组织与管理能力
业务与商业模式	业务组合及收入贡献、目标客户、收入来源、收费方式与业务协同；对单一产品或业务的依赖
产品与技术	已形成的产品线、定位、技术路线、核心技术、任务完成效果、质量与可靠性
经营表现	相关业务营收、增长、利润、盈利能力与现金表现；增长来自新增客户、老客户增购还是涨价
市场与用户	市场地位、用户或客户体量、客户结构、主要客户依赖、细分市场覆盖、留存与复购
品牌与用户评价	品牌认知、口碑、满意度、投诉及用户认可或不满之处
地域与渠道	优势区域、市场覆盖、渠道结构、控制力与客户关系；对单一区域或渠道的依赖
研发与创新	持续开发新能力的团队、投入与机制，技术成果、迭代速度及商业转化
销售与获客	销售组织、营销方式、获客成本与销售效率
价格、成本与供应链	可比产品售价、总使用成本、付款条件、采购制造、规模效率与供货稳定性
运营、交付与服务	运营效率、交付速度、实施、运维、售后与服务范围
合作生态与外部依赖	关键伙伴与合作内容、自有与外部能力；客户关系、技术、数据或渠道由谁掌握，能否替代，合作中断的影响
竞争策略与资源配置	定价、差异化、扩张方向；资金和人员投向、投入持续性、落地速度与效果，是否支持主要竞争方向

用“维度｜企业 A｜企业 B｜企业 C｜差异与优劣势”的比较表填写实际数值、做法或特征；企业或维度较多时可分表，但同一维度应覆盖各家。每项差异说明谁相对更强或更弱、适用于什么市场或客户，再归纳各家最突出的特点、优势与短板。不能只列待查字段、逐家简介，或每家挑一款产品就代表整家企业。

比较各家在客户、价位、应用场景或地域中的强弱分布，分清已经布局与确有优势。用可查到的赢单、失单、客户迁移、渠道进入或退出等检验竞争判断，说明实际得失与原因；个别案例不代表整体胜率。

背景、投资者和投入规模不直接等于优势，要说明资源怎样转化为产品、获客、效率或经营结果，以及能否持续。用户评价注明平台、时间和样本范围，不把可见评论当成全体用户意见。

企业较多、竞争方式有明显差异时，可按产品定位、直销／渠道、单点产品／一体化方案等关键差异归类。写明各类企业及归类依据，再解释这种方式如何影响获客、成本、交付或客户切换。同一企业不同业务可以分属不同类型。

满足以下条件时，本 Skill 可自主生成竞争定位图或能力矩阵：

两个相互独立、具有业务意义且有证据支持的维度可以解释企业位置差异时，生成二维竞争定位图；
多家企业能够按同一组客户相关能力比较时，先形成 Markdown 能力矩阵；企业或维度较多、视觉识别能明显提升判断效率时，再生成热力图；
定位图和能力矩阵回答不同问题且互相补充时，可以配套交付：前者呈现战略位置，后者拆解具体能力差异。

不为凑图强画象限，不把缺失信息填成零分，也不把定性位置、能力等级或标记大小伪装成市场份额或精确排名。具体触发、制图、数据口径和验收规则见竞争定位图与能力矩阵。

价格与性能比较注明对应产品和条件，宣传主张与实测、承诺与履约分开；缺失值写明未知，不算零分，也不以披露多寡判断强弱。

比较后归纳各家的竞争特点，再判断哪些优势可持续：成本效率、规模、技术积累、关键资源、认证、切换成本、生态或网络效应为何难以复制，依赖什么条件；复用前面的事实，不另抄一遍各维度。

4. 标的企业分析（企业研究时）

在企业横向比较的基础上，单拉一章分析目标企业，不以突出表中一行代替：

内容	要回答的问题
地位	在行业及主要细分市场处于什么位置？相对哪些对手领先、落后或正在变化？谁正威胁它的哪块业务，通过什么方式，有哪些实际动作？
优势与劣势	相对主要对手，强项与短板是什么？各有什么依据，在哪些客户、区域或业务上成立，能否持续？
问题与痛点	当前出了什么问题、原因是什么？哪些持续约束造成成本、效率、客户流失或增长损失？区分企业自身问题、行业共性压力与尚未发生的风险
关键风险与承压能力	什么变化会使当前优势或策略失效，影响哪些业务？面对价格战、关键客户流失、渠道变化或技术替代等压力，有哪些资金、客户、供应和组织上的应对余地？列关键触发条件与观察信号
机会	哪些需求、技术、渠道或竞争变化有利于它？为什么它可能拿得到，现有基础与缺少的能力是什么，还有谁在争夺？说明进入代价、必要条件与风险
策略	企业正在采取或已宣布什么行动，针对什么问题或机会，效果如何？研究建议另行标明；对有实际意义的选择比较收益、代价、资源取舍和适用条件，说明优先方向及需验证的结果，不硬给唯一答案

只做行业研究时不设本章；行业与企业一起研究时，保留行业的平行比较，再独立分析标的企业，共用前文事实，不重复抄表。局部比较或单一问题只展开相关内容。

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skills/hehe-competitive-landscape/SKILL.md

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竞争格局分析

启动与工作流程

核心分析方法

1. 行业与参与企业

2. 市场集中度、份额与变化

选择指标并计算

解释格局及其变化

3. 核心企业比较

4. 标的企业分析（企业研究时）

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