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Minium 测试用例生成器 · LeoYeAI | DecimalAI

Testingby LeoYeAI

Minium 测试用例生成器

Minium 录制脚本转测试用例工具。自动解析录制脚本，生成符合规范的测试用例和页面对象，确保步骤完整、逻辑一致。

Never made a case worse

SkillScore

SkillScore70/100

Test results 70

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

9%70%of test cases passed +61 pts

Without skill2 of 23

With skill16 of 23

14 fixed2 passed either way7 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v1

Track record

Agent runs: 0; All time

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 minium-test-generator
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("minium-test-generator")
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 minium-test-generator

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/minium-test-generator/SKILL.md https://app.decimal.ai/s/minium-test-generator/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/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/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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("minium-test-generator")
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/minium-test-generator/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/minium-test-generator/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 minium-test-generator

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/minium-test-generator/SKILL.md https://app.decimal.ai/s/minium-test-generator/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/minium-test-generator/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/minium-test-generator/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/minium-test-generator.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: v1, tested 7/27/2026
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

Minium 测试用例生成器

📖 描述

本技能用于将 Minium 录制脚本自动转换为符合项目规范的测试用例，确保步骤一个不漏，逻辑完全一致。

核心能力:

✅ 解析录制脚本，提取所有步骤
✅ 生成符合规范的页面对象和测试用例
✅ 自动验证步骤完整性
✅ 避免漏掉步骤和简化逻辑

⚡ 核心规则（必须遵守！）

🚨 第一条：必须先确认目录，再生成！

当用户提供录制文件时，禁止直接生成！

正确流程：

第 1 步：询问目录
→ "好的，我来帮你生成测试用例。请问测试用例目录在哪里？（完整路径）"
→ 等待用户回复

第 2 步：检查目录是否存在
→ 如果目录不存在 → 走【新建项目】流程
→ 如果目录已存在 → 走【添加到现有目录】流程

第 3 步：确认创建方式
→ 向用户展示将要创建/添加的文件列表
→ 等待用户确认

第 4 步：生成内容
→ 新建：创建完整项目结构
→ 添加：仅新增文件，不覆盖已有文件

错误示范（禁止！）：

❌ 用户给录制脚本 → 直接生成（未询问目录）
❌ 假设目录结构
❌ 覆盖用户原有文件

🚨 第二条：已存在目录 → 先阅读现有代码结构！

当目录已存在时，禁止直接生成！

正确流程：

第 1 步：读取现有文件结构
→ 列出目录内容（pages/、test_cases/、*.py 等）

第 2 步：阅读已有代码
→ 阅读 pages/ 中的现有 Page Object
→ 分析命名规范、类命名、元素定位风格
→ 阅读 test_*.py 中的现有测试用例
→ 分析测试结构、断言风格、等待方式

第 3 步：总结现有规则
→ Page Object 命名规则（如：驼峰/下划线）
→ 元素定位规则（如：优先 selector 还是 xpath）
→ 测试用例结构（如：是否使用 mark_step_start）
→ 截图点命名规则（如：step_1、tap_1 等）

第 4 步：按现有规则生成新用例
→ 新 Page Object 遵循已有命名和结构
→ 新测试用例遵循已有风格和格式
→ 不引入与现有代码冲突的规范

示例：

我：检测到目录已存在，我先阅读一下现有代码结构...

[阅读 pages/ 和 test_*.py 后]

我：现有代码规则如下：
- Page Object 命名：大驼峰（如 GroupHomepage）
- 元素定位：优先 selector，xpath 备用
- 测试结构：使用 mark_step_start 和 capture
- 截图点命名：动作 + 序号（如 tap_1、scroll_2）

我将按此规则生成新用例，确认吗？

🚨 第三条：同一个页面 → 只需追加方法，不要新建文件！

当 Page Object 文件已存在时：

❌ 错误做法：
- 创建新文件：pages/xxx_new.py
- 或创建重复文件：pages/xxx/xxx_v2.py
- 或创建分散的多个小方法

✅ 正确做法：
- 在原有文件末尾追加新方法
- 保持原有代码不变
- 同一个页面的操作整合到一个方法里
- 使用 basedef.py 中封装的方法（tap()、send_key()）

示例：

原文件：publish_start_group.py (已有 1000 行)

❌ 错误：创建多个分散小方法
    def input_theme(self): ...
    def click_help_entry(self): ...
    def click_reward_switch(self): ...

✅ 正确：整合到一个完整方法
    def pub_group_supply(self, theme_text="xxx"):
        """跟团供货模式发布完整流程"""
        self.send_key(self.theme_input, theme_text)
        self.tap(self.promise_btn)
        self.scroll_to(400)
        self.tap(self.help_entry)
        ...

🚨 第四条：根据页面路径选择 testcase 目录！

用例存放目录规则：

分析录制脚本中的页面路径
- 查看 URL 中的路径结构，如：
  - /pro/pages/homepage/group-homepage/... → 包含 homepage
  - /pro/pages/home/leaders-management/... → 包含 home
  - /pro/pages/seq-publish/... → 包含 seq-publish
检查现有 testcase 目录结构
- 列出 testcase/ 下的子目录
- 对比页面路径中的关键词，选择最匹配的目录
- 例如：页面路径包含 home → 找 testcase/home_page/ 或 testcase/home/
没有合适的目录时
- 根据页面路径中的关键词创建新目录
- 例如：页面路径是 /pro/pages/seq-publish/... → 创建 testcase/seq_publish/

核心原则：看项目的实际目录结构，不要硬编码映射关系！

🚨 第五条：先阅读用户代码，再决定使用什么方法！

Page Object 核心规则：

先阅读用户的基类代码（如 basedef.py）
- 检查用户是否封装了点击、输入等方法
- 如果有封装 → 使用用户的封装方法
- 如果没有封装 → 生成封装方法并使用
检查用户的封装方式
- 点击方法：可能是 tap(), click(), click_element() 等
- 输入方法：可能是 send_key(), input_text(), type() 等
- 滚动方法：可能是 scroll_to(), scroll() 等
元素定位策略
- 第一选择：CSS selector（如 view.btn-know.button）
- 用户反馈找不到元素时 → 补充 xpath
- 还找不到时 → 补充 text 匹配
- 不要一开始就生成一堆备用方案！
元素定位与操作分离
- 元素常量：使用中文注释说明用途
- 每个元素都必须有对应的操作方法
- 禁止只有元素定义没有方法
方法命名规范
- 动词开头：click_xxx, input_xxx, select_xxx
- 语义清晰：方法名能清楚表达操作意图
不要返回 self（除非需要链式调用）
- 大部分方法不需要返回 self
- 只有需要连续调用时才返回
不要照搬录制脚本的等待时间！
- 录制脚本中的 self.page.wait_for(x) 是录制工具自动生成的
- 用户的基类方法（如 tap(), send_key()）内部已有等待逻辑
- 禁止生成多余的 sleep() 或 wait_for()
- 除非用户明确要求添加等待
不需要生成 .bat 运行脚本
- 用户有自己的运行方式（如 run.py）
- 除非用户明确要求，否则不生成 .bat 文件

示例：

# 情况 1：用户已有封装方法（如 basedef.py 中有 tap() 和 send_key()）
class SelectFollowReward(BaseDef):
    reward_value_input = "..."
    confirm_btn = "..."
    
    def input_reward_value(self, value):
        self.send_key(self.reward_value_input, str(value))  # 使用用户的封装
    
    def click_confirm(self):
        self.tap(self.confirm_btn)  # 使用用户的封装

# 情况 2：用户没有封装方法
class SelectFollowReward(BaseDef):
    reward_value_input = "..."
    confirm_btn = "..."
    
    def input_reward_value(self, value):
        self.page.get_element(self.reward_value_input).input(str(value))
    
    def click_confirm(self):
        self.page.get_element(self.confirm_btn).tap()

新建项目结构：

tests/
├── pages/                  # Page Object 层
│   ├── __init__.py         # 导出所有页面对象
│   ├── [页面].py           # 每个页面一个类
│   └── README.md           # PO 模型说明
├── test_cases/             # 测试用例文档
│   └── [用例名].md         # Markdown 格式用例
├── conftest.py             # Pytest 配置
├── run_test.py             # 测试启动脚本
└── test_[用例名].py        # 可执行测试脚本

⚡ 工作流程

场景 A：新建项目（目录不存在）

当用户提供的目录不存在时：

我：好的，我来帮你生成测试用例。

检测到目录不存在，我将为你创建完整的测试项目结构：

📁 [用户提供的路径]/
├── pages/                  # Page Object 层
│   ├── __init__.py
│   ├── [页面 1].py
│   └── [页面 2].py
├── test_cases/             # 测试用例文档
│   └── [用例名].md
├── conftest.py             # Pytest 配置
├── run_test.py             # 测试启动脚本
└── test_[用例名].py        # 可执行测试脚本

确认开始创建吗？

用户确认后：

创建所有目录和文件
生成 Page Object 框架
生成第一个测试用例

当用户提供的目录已存在时：

我：好的，我来帮你生成测试用例。

检测到目录已存在，我将在现有目录下添加新用例：

📁 [用户提供的路径]/
├── pages/                  # 新增 Page Object
│   └── [新页面].py
├── test_cases/             # 新增测试用例文档
│   └── [新用例名].md
└── test_[新用例名].py      # 新增测试脚本

⚠️ 不会覆盖已有文件

确认开始创建吗？

用户确认后：

仅创建新文件
不修改已有文件
如需更新已有 Page Object，先询问用户

📋 目录检查方法

使用 exec 检查目录是否存在：

# Windows PowerShell
Test-Path "<用户提供的路径>"

# 返回 True → 目录存在 → 走添加流程
# 返回 False → 目录不存在 → 走新建流程

检查子目录结构：

# 检查是否有 pages 目录
Test-Path "<路径>/pages"

# 检查是否有 test_cases 目录
Test-Path "<路径>/test_cases"

# 检查是否有 conftest.py
Test-Path "<路径>/conftest.py"

根据检查结果判断：

如果 pages/ 存在 → 追加新 Page Object
如果 pages/ 不存在 → 创建 pages/ 目录
如果 test_cases/ 存在 → 追加新用例文档
如果 test_cases/ 不存在 → 创建 test_cases/ 目录

📝 完整对话示例

示例 1：新建项目

你：帮我生成测试用例
[粘贴录制脚本]

我：好的，我来帮你生成测试用例。

首先，请告诉我：
1. 测试用例目录在哪里？（完整路径）

你：D:\projects\minium-tests\scenario-a

我：好的，我来检查目录...

检测到目录不存在，我将为你创建完整的测试项目结构：

📁 D:\projects\minium-tests\scenario-a\
├── pages/
│   ├── __init__.py
│   ├── homepage.py
│   └── detail_page.py
├── test_cases/
│   └── scenario_a.md
├── conftest.py
├── run_test.py
└── test_scenario_a.py

确认开始创建吗？

你：确认

我：✅ 项目结构已创建完成！

[输出所有文件内容]

示例 2：添加到现有目录

你：帮我生成测试用例
[粘贴录制脚本]

我：好的，我来帮你生成测试用例。

首先，请告诉我：
1. 测试用例目录在哪里？（完整路径）

你：C:\workspace\expression-trainer-miniprogram\tests

我：好的，我来检查目录...

检测到目录已存在，我将在现有目录下添加新用例：

📁 C:\workspace\expression-trainer-miniprogram\tests\
├── pages/                  # 新增 Page Object
│   └── [新页面].py
├── test_cases/             # 新增测试用例文档
│   └── [新用例名].md
└── test_[新用例名].py      # 新增测试脚本

⚠️ 不会覆盖已有文件

确认开始创建吗？

你：确认

我：✅ 测试用例已添加完成！

[输出新增文件内容]

⚡ 快速触发

当你有以下需求时，直接说即可:

"我给你一个录制文件，帮我生成测试用例" → 触发流程
"帮我生成测试用例" + 录制脚本内容 → 触发流程
"验证一下测试用例是否完整" → 直接验证
"解析这个录制脚本" → 直接解析

示例:

你：我给你一个录制文件，帮我生成测试用例
[从你的录制脚本目录找到 Python 文件，复制内容粘贴]

我：好的，我来帮你生成测试用例。

首先，请告诉我：
1. 测试用例目录在哪里？（完整路径）
2. 是否有现有的测试项目结构？

📁 录制脚本获取方式

🎯 触发条件

核心触发场景: 用户提供 Minium 录制脚本，要求生成或验证测试用例

1. 用户提供录制文件时 ⭐ 主要触发场景

触发词:

"我给你一个录制文件"
"我有一份录制脚本"
"这是我录制的测试脚本"
"录制文件如下"
用户直接粘贴录制脚本代码

文件路径示例:

<你的录制脚本目录>\[场景名称]\[时间戳]\xxx.py

响应流程:

你：我给你一个录制文件，帮我生成测试用例
[粘贴录制脚本内容]

我：好的，我来帮你生成测试用例。

首先，请告诉我：
1. 测试用例目录在哪里？（完整路径）
2. 是否有现有的测试项目结构？

2. 要求生成测试用例时 ⭐ 主要触发场景

触发词:

"帮我生成测试用例"
"把这个录制脚本转成测试用例"
"根据录制脚本生成代码"
"生成 Minium 测试代码"
"生成页面对象和测试用例"

响应流程:

你：帮我生成测试用例
[粘贴录制脚本内容]

我：好的，我来帮你生成测试用例。

首先，请告诉我：
1. 测试用例目录在哪里？（完整路径）
2. 是否有现有的测试项目结构？

3. 要求验证测试用例时

触发词:

"验证一下测试用例是否完整"
"检查有没有漏掉步骤"
"验证步骤是否一致"
"看看有没有漏掉什么步骤"

示例:

你：帮我验证一下这个测试用例有没有漏掉步骤
录制脚本：<你的录制脚本目录>\xxx.py
测试用例：<你的项目目录>\testcase\xxx\test_xxx.py

我：好的，我来对比验证...

4. 要求解析录制脚本时

触发词:

"解析这个录制脚本"
"提取录制脚本的步骤"
"生成步骤清单"
"看看有多少步"

示例:

你：帮我解析这个录制脚本，看看有多少步
[从你的录制脚本目录找到 Python 文件，复制内容粘贴]

我：好的，我来运行 parse_steps.py 解析...

🛠️ 技能工具

parse_steps.py

功能: 解析录制脚本，生成步骤清单

用法:

python <技能包路径>/scripts/parse_steps.py \
    --input <录制脚本路径> \
    --output <输出路径>

示例:

python scripts/parse_steps.py \
    --input recorded_script.py \
    --output steps_checklist.md

generate_pages.py

功能: 生成页面对象和测试用例

用法:

python <技能包路径>/scripts/generate_pages.py \
    --input <录制脚本路径> \
    --output <测试用例输出目录> \
    --pages <页面类目录>

示例:

python scripts/generate_pages.py \
    --input recorded_script.py \
    --output testcase/xxx/ \
    --pages pages/pages/

validate_steps.py

功能: 验证测试用例是否漏掉步骤

用法:

python <技能包路径>/scripts/validate_steps.py \
    --input <录制脚本路径> \
    --test <测试用例路径> \
    --report <验证报告路径>

检查项:

步骤数量是否一致
相邻步骤是否使用了相同的元素
特殊逻辑是否保留
页面跳转是否都有等待

📋 工作流程

阶段 1: 解析录制脚本

# 1. 运行解析脚本
python scripts/parse_steps.py \
    --input recorded_script.py \
    --output steps_checklist.md

# 2. 查看生成的步骤清单
cat steps_checklist.md

输出:

普通步骤列表
特殊逻辑步骤列表（带⚠️标记）
循环步骤列表（带🔄标记）
步骤对照表模板

阶段 2: 生成测试用例

# 运行生成脚本
python scripts/generate_pages.py \
    --input recorded_script.py \
    --output testcase/xxx/ \
    --pages pages/pages/

输出:

页面对象类（追加到已有文件）
测试用例（新建文件）
步骤对照表

阶段 3: 验证

# 运行验证工具
python scripts/validate_steps.py \
    --input recorded_script.py \
    --test testcase/xxx/test_xxx.py \
    --report validation_report.md

输出:

步骤数量验证
相邻步骤检查
特殊逻辑检查
页面跳转等待检查

⚠️ 质量控制

核心原则

不漏掉任何步骤 - 录制脚本的每一步都要在测试用例中有对应
不简化特殊逻辑 - 三层判断、else 判断必须完整保留
不混淆相邻步骤 - 相邻步骤的元素 XPath 必须不同
页面跳转必须等待 - 每个页面跳转后都要添加 wait_for_page_success()

检查清单

在生成测试用例后，必须检查：

步骤数量是否与录制脚本一致？
特殊逻辑步骤是否保留完整？
相邻步骤的元素是否不同？
页面跳转后是否都添加了等待？
是否运行了 validate_steps.py 验证？

📖 参考文档

文档	说明
core-skills/避免漏掉步骤.md	核心技能：如何避免漏掉步骤
docs/代码生成质量控制流程.md	完整的质量控制流程
docs/代码审查清单.md	提交前必须逐项检查
docs/代码规范.md	代码规范详细说明

🧪 使用示例

示例 1: 用户提供录制文件

你:

我给你一个录制文件，帮我生成测试用例

[从你的录制脚本目录找到 Python 文件，复制内容粘贴]

我:

好的，我来使用 minium-test-generator 技能为你生成测试用例。

第 1 步：解析录制脚本...
python scripts/parse_steps.py --input recorded_script.py --output steps_checklist.md

第 2 步：生成测试用例...
python scripts/generate_pages.py --input recorded_script.py --output testcase/xxx/ --pages pages/pages/

第 3 步：验证步骤完整性...
python scripts/validate_steps.py --input recorded_script.py --test testcase/xxx/test_xxx.py --report validation_report.md

✅ 生成完成！测试用例已保存到 testcase/xxx/test_xxx.py
验证报告：validation_report.md

示例 2: 验证测试用例

你:

帮我验证一下这个测试用例有没有漏掉步骤
录制脚本：<你的录制脚本目录>\recorded_script.py
测试用例：<你的项目目录>\testcase\xxx\test_xxx.py

我:

好的，我来运行 validate_steps.py 验证...

python scripts/validate_steps.py \
    --input <你的录制脚本目录>\recorded_script.py \
    --test <你的项目目录>\testcase\xxx\test_xxx.py \
    --report validation_report.md

验证结果:
✅ 所有步骤都已转换（X 步，一个不漏）
✅ 相邻步骤元素检查通过
✅ 特殊逻辑检查通过
✅ 页面跳转等待检查通过

📊 技能统计

指标	数值
支持的文件格式	Minium 录制脚本 (.py)
生成的文件格式	Python 测试用例 (.py)
验证检查项	4 项
支持的特殊逻辑	三层判断、else 判断、循环、页面跳转
典型录制脚本路径	`<你的录制脚本目录>\[场景名称]\[时间戳]\`

🚀 快速开始

# 1. 查看技能文档
cat <技能包路径>/README.md

# 2. 学习核心技能
cat <技能包路径>/core-skills/避免漏掉步骤.md

# 3. 使用工具
python <技能包路径>/scripts/parse_steps.py --help

最后更新：2026-03-23

About this skill

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QA & Test Engineering pack

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Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/minium-test-generator/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 3 files · scripts

Illustrative Want to see what minium-test-generator 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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Minium 测试用例生成器

📖 描述

⚡ 核心规则（必须遵守！）

🚨 第一条：必须先确认目录，再生成！

🚨 第二条：已存在目录 → 先阅读现有代码结构！

🚨 第三条：同一个页面 → 只需追加方法，不要新建文件！

🚨 第四条：根据页面路径选择 testcase 目录！

🚨 第五条：先阅读用户代码，再决定使用什么方法！

⚡ 工作流程

场景 A：新建项目（目录不存在）

场景 B：添加到现有目录（目录已存在）

📋 目录检查方法

📝 完整对话示例

示例 1：新建项目

示例 2：添加到现有目录

⚡ 快速触发

📁 录制脚本获取方式

🎯 触发条件

1. 用户提供录制文件时 ⭐ 主要触发场景

2. 要求生成测试用例时 ⭐ 主要触发场景

3. 要求验证测试用例时

4. 要求解析录制脚本时

🛠️ 技能工具

parse_steps.py

generate_pages.py

validate_steps.py

📋 工作流程

阶段 1: 解析录制脚本

阶段 2: 生成测试用例

阶段 3: 验证

⚠️ 质量控制

核心原则

检查清单

📖 参考文档

🧪 使用示例

示例 1: 用户提供录制文件

示例 2: 验证测试用例

📊 技能统计

🚀 快速开始

SkillSafety

Source

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