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Category Selection · leoyeai | DecimalAI

Industryby leoyeai

Category Selection

亚马逊品类自动化选品分析技能。通过五维评分模型对亚马逊品类进行深度市场调研，生成Markdown分析报告。当用户使用 /category-selection 命令或提出'分析XX品类'、'XX品类市场调研'、'XX品类选品'等需求时触发此技能。支持配置分析数量，默认Top20。

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%86%of test cases passed +50 pts

Without skill8 of 22

With skill19 of 22

11 fixed8 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/6/2026v119 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen invoking the category_trend API tool for Amazon market metrics, how many months of historical sales, price, and brand records are retrieved?

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-category-selection
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-category-selection")
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-category-selection

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-category-selection/SKILL.md https://app.decimal.ai/s/leoyeai-category-selection/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-category-selection/SKILL.md

This gets SKILL.md only. The skill's 23 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-category-selection

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-category-selection")
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-category-selection/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-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/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-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection")
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-category-selection/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-category-selection/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 23 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-category-selection

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-category-selection/SKILL.md https://app.decimal.ai/s/leoyeai-category-selection/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-category-selection/SKILL.md

This gets SKILL.md only. The skill's 23 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-category-selection/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-category-selection.json

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/6/2026
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

快速参考

一键执行工作流 (推荐)

# 使用品类名称
python .claude/skills/category-selection/scripts/workflow.py "Sofas" US 20

# 直接使用 NodeID (推荐，避免类目搜索问题)
python .claude/skills/category-selection/scripts/workflow.py 679394011 US 20

# 指定分析数量
python .claude/skills/category-selection/scripts/workflow.py "Kitchen" US 50

重要更新 (v4.0):

✅ 自动读取 API Key: 无需设置环境变量，自动从 .mcp.json 读取
✅ 修复控制字符: 自动处理 JSON 字符串值中的未转义换行符、制表符
✅ 改进类目搜索: 支持模糊匹配和关键词变体
✅ 详细日志: 执行日志保存到 execution.log

核心 API 工具

步骤	工具/操作	用途	返回数据大小
1. 搜索类目	`category_name_search`	获取类目 nodeId	小
2. 类目报告	`category_report`	获取 Top 产品列表和统计数据	大 (>25KB)
3. 产品详情	`product_detail`	获取单个产品详情	小
4. 类目关键词	`category_keywords`	获取类目核心关键词	大 (>25KB)
5. 类目趋势	`category_trend`	获取25个月历史趋势	中
6. 1688采购	`products_1688`	获取采购成本数据	小

调用格式

curl -s -X POST "https://mcp.sorftime.com?key=YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":N,"method":"tools/call","params":{"name":"TOOL_NAME","arguments":{"amzSite":"US","nodeId":"NODE_ID"}}}'

触发条件

当用户使用以下方式请求时，启动此分析流程：

命令: /category-selection {品类名称} {站点} [--limit N]
示例: /category-selection "Sofas" US --limit 20
自然语言: "分析Amazon美国站的Sofas品类"、"Sofas品类市场调研"、"Sofas品类选品"

角色设定

你是一位拥有10年经验的"亚马逊选品专家"和"市场分析师"。你精通品类分析方法论，能够通过数据洞察市场机会、竞争格局和进入壁垒，为用户提供可执行的选品建议。

五维评分模型 (标准版)

评分标准详解:

维度	分值	评分标准	数据来源
市场规模	20 分	>$10M=20分, >$5M=17分, >$1M=14分, 其他=10分	类目月销额 (top100产品月销额)
增长潜力	25 分	低评论产品占比>40%=22分, >20%=18分, 其他=14分	评论数<100的产品占比
竞争烈度	20 分	Top3品牌占比<30%=18分, <50%=14分, 其他=8分	CR3 品牌集中度
进入壁垒	20 分	Amazon占比<20%且新品>40%=20分, 其他组合6-18分	Amazon自营占比 + 低评论占比
利润空间	15 分	均价>$300=12分, >$150=10分, >$50=7分, 其他=4分	Top100产品平均价格

评级标准:

总分	评级	建议
80-100	优秀	强烈推荐进入
70-79	良好	可以考虑进入
50-69	一般	谨慎进入
0-49	较差	不建议进入

完整标准请参考: scoring-standard.md

完整分析流程

阶段一: 数据收集

步骤 1: 搜索类目获取 nodeId

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"category_name_search","arguments":{"amzSite":"US","searchName":"品类关键词"}}}'

处理多个类目结果时：

大类目（如 "Clothing, Shoes & Jewelry"）通常只返回子类目列表
展示给用户让其选择最匹配的类目
或使用具体的子类目 NodeID 直接查询

常见类目 NodeID 参考:

Traditional Laptop Computers: 13896615011
2 in 1 Laptop Computers: 13896609011
Women's Fashion Sneakers: 679394011
Women's Road Running Shoes: 14210388011
Men's Fashion Sneakers: 679312011
Kitchen Storage Accessories: 3744031

步骤 2: 获取类目报告 (Top100 + 统计)

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"category_report","arguments":{"amzSite":"US","nodeId":"NODE_ID"}}}'

关键: category_report 返回数据通常>25KB，会保存到临时文件

响应处理:

# 使用 workflow.py 自动处理 (推荐)
python .claude/skills/category-selection/scripts/workflow.py "Sofas" US 20

# 或手动解码 SSE 响应
python .claude/skills/category-selection/scripts/sse_decoder.py {temp_file} {output_dir} 20

步骤 3: 获取 Top N 产品详情 (并发)

# 并发获取 Top3 产品详情
curl ... '{"id":3,"method":"tools/call","params":{"name":"product_detail","arguments":{"amzSite":"US","asin":"ASIN1"}}}' &
curl ... '{"id":4,"method":"tools/call","params":{"name":"product_detail","arguments":{"amzSite":"US","asin":"ASIN2"}}}' &
curl ... '{"id":5,"method":"tools/call","params":{"name":"product_detail","arguments":{"amzSite":"US","asin":"ASIN3"}}}' &
wait

步骤 4: 获取类目关键词 (可选)

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":6,"method":"tools/call","params":{"name":"category_keywords","arguments":{"amzSite":"US","nodeId":"NODE_ID","page":1}}}'

处理关键词数据:

python .claude/skills/category-selection/scripts/keywords_parser.py \
  {temp_file} \
  {output_dir} \
  20

步骤 5: 获取历史趋势数据 (可选)

curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":7,"method":"tools/call","params":{"name":"category_trend","arguments":{"amzSite":"US","nodeId":"NODE_ID"}}}'

趋势数据类型:

类目月销量趋势 (25个月)
平均售价趋势
平均星级趋势
品牌数量趋势

阶段二: 数据分析

核心分析指标

1. 市场集中度分析

# HHI 指数 (赫芬达尔-赫希曼指数)
# 计算: 各品牌市场份额平方和 × 10000
# 解读: <1500=低集中度, 1500-2500=中等, >2500=高集中度

# CR3/CR5 (前N品牌集中度)
# 计算: 前N大品牌销量占比
# 解读: <30%=分散, 30-50%=中等, >50%=集中

2. 品牌分析

# 品牌分布: 按销量/销额排序
# 品牌数量: 统计独立品牌数
# 品牌多样性: HHI 指数评估

3. 卖家来源分析

# Amazon 自营占比
# 中国卖家占比
# 美国本土卖家占比
# 其他国际卖家占比

4. 价格分析

# 价格区间分布
# 平均价格
# 价格中位数
# 价格标准差

5. 新品分析

# 新产品定义: 上架时间 < 90天
# 新品占比: 新品数量 / 总数量
# 新品表现: 新品平均销量、评论数

阶段三: 报告生成

生成完整报告

# 一键生成所有报告格式
python .claude/skills/category-selection/scripts/workflow.py "Sofas" US 20

# 或分步骤生成
python .claude/skills/category-selection/scripts/generate_reports.py {data_json}

输出文件结构:

category-reports/
└── {Category}_{Site}_{YYYYMMDD}/
    ├── report.md                      # Markdown 分析报告
    ├── data.json                      # 完整解码数据 (中文键)
    ├── top_products.json              # Top N 产品列表
    ├── scores.json                    # 五维评分结果
    ├── execution.log                  # 执行日志 (v4.0 新增)
    ├── keywords.json                  # 类目关键词
    ├── trend_data.json                # 25个月趋势数据
    ├── adapted_data.json              # Excel 适配数据 (英文键)
    ├── category_analysis_report.xlsx  # Excel 报告
    ├── dashboard.html                 # HTML 可视化仪表板
    ├── data/                          # 原始数据目录
    │   ├── statistics.csv             # 统计数据
    │   ├── products.csv               # 产品列表
    │   └── scores.csv                 # 评分详情
    └── *_raw.txt                      # 原始 SSE 响应

数据处理工具

核心工具脚本

脚本	用途	版本
`workflow.py`	一键执行完整分析流程	v4.0
`sse_decoder.py`	解码 category_report SSE 响应	v6.0
`keywords_parser.py`	解码 category_keywords 响应	v3.0
`trend_parser.py`	解析趋势数据	v1.0
`data_adapter.py`	数据格式转换 (中文→英文键)	v1.0
`data_utils.py`	数据处理工具类	v2.0
`generate_reports.py`	统一报告生成器	v3.0
`generate_excel_report.py`	Excel 报告生成	v2.0
`generate_markdown_report.py`	Markdown 报告生成	v2.0
`fix_encoding.py`	编码修复工具	v1.0

数据字段映射

API 响应字段 → 标准化字段:

API 字段	标准化字段	说明
ASIN	asin	产品唯一标识
标题/title	title	产品标题
价格/price	price	当前售价
月销量/monthlySales	monthly_sales	月销量
月销额/monthlyRevenue	monthly_revenue	月销售额
评论数/reviews	review_count	评论数量
星级/rating	rating	平均评分
品牌/brand	brand	品牌名称
卖家/seller	seller	卖家名称
上架时间/daysOnline	days_online	上架天数

HTML 可视化仪表板

特性

基于 ECharts 的交互式图表
五维评分可视化进度条
KPI 指标卡片展示
7 个动态图表：销量趋势、价格趋势、价格分布、评分分布、品牌份额、卖家来源、品牌评分趋势
Top50 产品详细表格
关键发现智能分析

模板变量支持

变量类型	示例变量	说明
基础信息	`{{CATEGORY_NAME}}`, `{{SITE}}`, `{{DATA_DATE}}`	报告基本信息
五维评分	`{{MARKET_SIZE_SCORE}}`, `{{MARKET_SIZE_PERCENT}}`	各维度得分和进度条百分比
KPI指标	`{{TOTAL_PRODUCTS}}`, `{{AVG_PRICE}}`, `{{CR3}}`	关键指标数据
图表数据	`{{SALES_TREND_DATA}}`, `{{BRAND_SHARE_DATA}}`	JavaScript JSON 数据
分析结论	`{{CONCENTRATION_LEVEL}}`, `{{RECOMMENDATION}}`	智能分析文本

故障排查

常见问题与解决方案 (v4.0 更新)

1. API Key 未设置

问题: ❌ API Key 未设置 或 Authentication required

原因:

环境变量 SORFTIME_API_KEY 未设置
.mcp.json 文件不存在或格式错误

解决方案 (v4.0 已修复):

workflow.py v4.0 会自动从 .mcp.json 读取 API Key
确保项目根目录存在 .mcp.json 文件，格式如下：

{
  "mcpServers": {
    "sorftime": {
      "url": "https://mcp.sorftime.com?key=YOUR_API_KEY"
    }
  }
}

手动设置环境变量 (备用):

# Windows PowerShell
$env:SORFTIME_API_KEY="your_api_key"

# Linux/Mac
export SORFTIME_API_KEY="your_api_key"

2. JSON 解析失败 - 未转义的控制字符

问题: JSONDecodeError: Invalid control character at: line 1 column 3401

原因: API 返回的 JSON 字符串值中包含原始的换行符（\n）、制表符（\t）等控制字符，这些控制字符没有被正确转义

示例:

// 错误格式（API 返回的原始格式）
{"标题": "类目：Renewed Laptops，排名:2
类目：Traditional Laptops，排名:11"}

// 正确格式
{"标题": "类目：Renewed Laptops，排名:2\\n类目：Traditional Laptops，排名:11"}

解决方案 (v4.0 已修复):

escape_control_chars_in_json_strings() 函数自动转义字符串值内的控制字符
该函数只处理字符串值内部的控制字符，不影响 JSON 结构

3. 类目未找到

问题: 搜索类目时返回"未查询到对应类目"

原因:

大类目（如 "Computers & Accessories"）可能只返回子类目列表
类目名称不准确

解决方案 (v4.0 已改进):

workflow.py v4.0 会自动尝试多种搜索变体
使用更具体的子类目名称
推荐: 直接使用类目 NodeID 查询

获取 NodeID 的方法:

# 先用大类目搜索，查看返回的子类目列表
curl -s -X POST "https://mcp.sorftime.com?key={API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"category_name_search","arguments":{"amzSite":"US","searchName":"Laptop"}}}'

4. 数据解析失败 (Mojibake 编码问题)

问题: data.json 中的中文显示为 "Top100äº§å" 等乱码

原因: API 返回 Unicode-escape 格式 (\u4ea7\u54c1)，解码后产生 Mojibake

解决方案 (v4.0 已自动修复):

fix_mojibake() 函数自动修复编码问题
在 Unicode-escape 解码后立即应用 .encode('latin-1').decode('utf-8')

5. Python dict 格式问题

问题: "Expecting property name enclosed in double quotes"

原因: API 返回 Python dict 格式（单引号），不是标准 JSON

解决方案 (v4.0 已修复):

python_dict_to_json() 函数正确处理单引号转换
同时处理 True/False/None 字面量

6. 大类目搜索失败

问题: "Computers & Accessories" 等大类目搜索无结果

解决方案:

使用子类目名称（如 "Laptops", "Computer Accessories"）
先搜索大类目获取子类目列表，让用户选择
直接使用已知 NodeID

版本更新记录

脚本	版本	更新内容
`workflow.py`	v4.0	✅ 从 .mcp.json 自动读取 API Key✅ 修复 JSON 字符串中未转义的控制字符✅ 改进类目搜索策略✅ 新增执行日志✅ 更详细的错误信息
`sse_decoder.py`	v6.0	Mojibake 自动修复、括号匹配、Python dict 转换
`generate_reports.py`	v3.0	完整变量替换、分析文本生成

调试技巧

查看执行日志:

# workflow.py v4.0 会自动保存执行日志
cat category-reports/{Category}_{Site}_{YYYYMMDD}/execution.log

查看原始响应:

# workflow.py 会自动保存原始 SSE 响应
cat category-reports/{Category}_{Site}_{YYYYMMDD}/category_report_raw.txt

检查编码问题:

# 检查文件字节
with open('data.json', 'rb') as f:
    print(f.read(100))

验证 JSON 格式:

# 使用 Python 验证 JSON
python -m json.tool data.json

测试 API 连接:

curl -s -X POST "https://mcp.sorftime.com?key={YOUR_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"category_name_search","arguments":{"amzSite":"US","searchName":"Kitchen"}}}'

支持的站点

Amazon: US, GB, DE, FR, IN, CA, JP, ES, IT, MX, AE, AU, BR, SA TikTok: US, GB, MY, PH, VN, ID 1688: 中国批发平台

注意事项

API Key 配置:
- 推荐在 .mcp.json 中配置（v4.0 自动读取）
- 也可以使用环境变量 SORFTIME_API_KEY
参数名称: 使用 amzSite 而非 site
id 递增: 每个请求的 id 字段必须递增 (1, 2, 3...)
并发限制: 建议最多 3-5 个并发请求
数据时效: 数据可能有 1-7 天延迟
报告命名: 使用 {Category}_{Site}_{YYYYMMDD} 格式

参考文档

评分标准详解
API 快速参考
Sorftime MCP API 文档
类目 API 参考

本 Skill 由 Claude Code 维护 | 最后更新: 2026-03-05 (v4.0)

About this skill

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/amazon-sorftime-research-category-skill/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 23 files · scripts

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

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快速参考

一键执行工作流 (推荐)

核心 API 工具

调用格式

触发条件

角色设定

五维评分模型 (标准版)

完整分析流程

阶段一: 数据收集

步骤 1: 搜索类目获取 nodeId

步骤 2: 获取类目报告 (Top100 + 统计)

步骤 3: 获取 Top N 产品详情 (并发)

步骤 4: 获取类目关键词 (可选)

步骤 5: 获取历史趋势数据 (可选)

阶段二: 数据分析

核心分析指标

阶段三: 报告生成

生成完整报告

数据处理工具

核心工具脚本

数据字段映射

HTML 可视化仪表板

特性

模板变量支持

故障排查

常见问题与解决方案 (v4.0 更新)

1. API Key 未设置

2. JSON 解析失败 - 未转义的控制字符

3. 类目未找到

4. 数据解析失败 (Mojibake 编码问题)

5. Python dict 格式问题

6. 大类目搜索失败

版本更新记录

调试技巧

支持的站点

注意事项

参考文档

SkillSafety

Source

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