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Android Static Analyzer — AI 测试先验知识生成器 · leoyeai | DecimalAI

Testingby leoyeai

Android Static Analyzer — AI 测试先验知识生成器

分析 Android 项目源码，用 LLM 从多维度生成 AI 自动化测试所需的先验知识文档，打包上报测试平台。核心价值：让 AI 测试 Agent 在运行前就知道「测什么、怎么断言、有哪些陷阱」。触发词：「分析我的 Android 项目」「生成测试画像」「理解这个 App 的业务」「提取测试先验知识」「帮我分析 Android 源码」

Never made a case worse

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%77%of test cases passed +55 pts

Without skill5 of 22

With skill17 of 22

12 fixed5 passed either way5 still failing0 broke

Measured on gemini-3.6-flash8/6/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen demonstrating how to send the generated `static-profile.json` to the local testing platform server via curl, what URL path and HTTP method should be specified?

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-android-static-analyzer
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-android-static-analyzer")
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-android-static-analyzer

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-android-static-analyzer/SKILL.md https://app.decimal.ai/s/leoyeai-android-static-analyzer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/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-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/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-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer")
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-android-static-analyzer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-android-static-analyzer/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 leoyeai-android-static-analyzer

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-android-static-analyzer/SKILL.md https://app.decimal.ai/s/leoyeai-android-static-analyzer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-android-static-analyzer/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/leoyeai-android-static-analyzer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-android-static-analyzer.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 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

Android Static Analyzer — AI 测试先验知识生成器

核心定位

AI 自动化测试要能"智能地"测试 App，需要知道以下 5 件事：

测什么（业务目标和范围）
怎么走（操作路径和前置状态）
预期什么（每一步操作后应该发生什么）
如何断言（怎么判断测试通过/失败）
有哪些陷阱（死路/外部依赖/不可逆操作）

本 Skill 从源码中提取上述所有信息，生成 9 份文档，打包成 static-profile。

与 AITestSDK 分工：

AITestSDK（运行期）→ 控件树、NavEdge、Activity 清单、实时 UI 状态
本 Skill（编码期）→ 业务语义、断言依据、状态依赖、测试策略

绝不重复 AITestSDK 已有的结构数据（Activity 数量、控件 ID 枚举等）。

执行步骤

Step 1：获取项目路径

询问用户项目路径。也接受：APK、GitHub 链接、直接粘贴的代码。

Step 2：读取核心源码

# 定位项目
find <path> -name "AndroidManifest.xml" -not -path "*/build/*" | head -3
find <path> -name "*Activity*.kt" -not -path "*/build/*"
find <path> -name "README*" -maxdepth 3 -not -path "*/build/*"
find <path> -name "build.gradle.kts" -not -path "*/build/*" | head -2

按优先级 read 每个文件前 300 行（优先读 Activity 源文件）。

读取时重点关注：

startActivity() / navigate() → 页面跳转
Toast.makeText() 的文本 → 操作反馈（成功/失败提示）
if/else + return → 约束条件和阻断逻辑
isEnabled / isVisible / visibility = View.GONE/VISIBLE → UI 状态变化
硬编码字符串（特殊校验码、账号、特定数值）
FLAG_ACTIVITY_CLEAR_TOP → 清栈跳转（影响回退路径）
异步回调（网络请求完成后跳转等）

Step 3：LLM 生成 8 份文档

拿到源码后，逐个生成以下文档。每份文档聚焦一个维度，独立分析。

📋 文档1：业务全景（Business Overview）

分析维度：
- App 属于什么业务领域
- 核心功能模块有哪些
- 哪些功能已实现 vs 暂未开放（仅 Toast）
- 技术栈（从 build.gradle 推断导航框架/网络库/DI 框架）

输出 JSON：
{
  "businessType": "移动电商/工具/社交/...",
  "summary": "一句话描述这个 App 是干什么的",
  "modules": ["模块名: 功能描述"],
  "unimplemented": ["功能名: 现状描述（如：点击仅弹Toast）"],
  "techStack": {
    "navigation": "startActivity / Jetpack Navigation",
    "network": "Retrofit / 无网络",
    "di": "Hilt / 无"
  }
}

🔄 文档2：用户旅程地图（User Journey Map）

分析维度：
- 用户从 Launcher Activity 开始的所有完整路径
- 每条路径的触发方式和前置条件
- 每个步骤的关键控件 ID
- 路径的终点和预期状态
- 是否存在快捷路径（绕过正常流程）

输出 JSON：
{
  "journeys": [
    {
      "name": "流程名（如：核心购买流程）",
      "priority": "P0必测/P1重要/P2可选",
      "steps": [
        {
          "step": 1,
          "page": "LoginFlowActivity",
          "action": "输入用户名和密码，点击登录",
          "keyControls": ["et_username", "et_password", "btn_login"],
          "waitCondition": "等待页面跳转或 Toast 出现"
        }
      ],
      "preconditions": ["前置状态：App 处于未登录状态"],
      "expectedFinalState": "页面跳转至 HomeFlowActivity"
    }
  ]
}

✅ 文档3：验证点与断言（Assertions）

这是最关键的文档。 AI 测试 Agent 需要知道每个操作后如何判断成功或失败。

分析维度：
- 每个关键操作后，UI 应该发生什么变化？
- 成功时：跳转到哪个页面 / 出现什么 Toast 文本 / 哪个控件变化
- 失败时：停在哪个页面 / 出现什么错误提示
- 操作后需要等待多久才能断言（同步/异步）

输出 JSON：
{
  "assertions": [
    {
      "scenario": "场景描述（如：空账号点登录）",
      "page": "LoginFlowActivity",
      "trigger": "btn_login",
      "successCondition": {
        "type": "pageSwitch / toast / controlChange",
        "target": "跳转到 HomeFlowActivity",
        "detail": "Activity 变为 HomeFlowActivity"
      },
      "failureCondition": {
        "type": "toast",
        "target": "Toast 文本包含「请输入手机号/邮箱」",
        "detail": "页面停留在 LoginFlowActivity"
      },
      "waitMs": 500,
      "note": "登录是同步操作，无需长等待"
    },
    {
      "scenario": "功能B前置条件未满足时触发操作",
      "page": "<TargetActivity>",
      "trigger": "<action_button>",
      "successCondition": {
        "type": "pageSwitch",
        "target": "跳转到 PageB"
      },
      "failureCondition": {
        "type": "toast",
        "target": "Toast 文本包含「请满足前置条件」"
      },
      "note": "前置条件：<checkbox_id> 等必要条件未满足时，操作应被阻断"
    }
  ]
}

🔗 文档4：状态依赖图（State Dependencies）

分析维度：
- 哪些功能需要 App 处于特定状态才能访问？
- 功能间的依赖关系（如：必须先登录才能访问核心功能）
- 操作会如何改变状态（如：核心操作完成后页面状态重置）
- 状态的持久化方式（内存 vs SharedPreferences vs 数据库）

输出 JSON：
{
  "stateDependencies": [
    {
      "feature": "进入功能模块B",
      "requires": ["用户已完成登录流程"],
      "howToReach": "从登录页完成登录后，进入 HomeFlowActivity 点击对应入口"
    },
    {
      "feature": "触发核心操作",
      "requires": ["已进入目标页面", "至少满足一项前置条件（如 <checkbox_id> 已勾选）"],
      "howToReach": "进入 <TargetActivity>，满足前置条件后点击 <action_button>"
    }
  ],
  "stateChanges": [
    {
      "action": "核心操作完成",
      "before": "已进入目标页面，操作前状态已满足",
      "after": "清栈跳转回 HomeFlowActivity，页面状态未持久化（内存数据）",
      "implication": "测试操作完成后，重新进入目标页面需要重新构造前置状态"
    }
  ]
}

⚠️ 文档5：边界与异常场景（Edge Cases）

分析维度：
- 数值边界：最小值、最大值、边界值±1
- 空值/非法输入：每个表单的验证规则
- 状态边界：某个功能恰好在满足/不满足条件时的行为
- 并发/重复操作：快速点击两次会怎样

输出 JSON：
{
  "edgeCases": [
    {
      "scenario": "数值输入最小值（边界）",
      "page": "PageA",
      "setup": "<value_display_id> 当前为最小值",
      "action": "点击减少数量按钮",
      "expected": "数量保持最小值，出现 Toast「已达最小值」",
      "testValue": "最小值时再减",
      "assertType": "toast + noChange"
    },
    {
      "scenario": "数值输入超上限（超界）",
      "page": "PageA",
      "setup": "<value_display_id> 当前为最大值",
      "action": "点击增加数量按钮",
      "expected": "数量保持最大值，出现 Toast「超出范围」",
      "assertType": "toast + noChange"
    },
    {
      "scenario": "输入框大小写敏感验证",
      "page": "PageA",
      "action": "输入框输入特殊值的小写形式，点击提交",
      "expected": "Toast 提示「操作成功」（大小写不敏感）",
      "assertType": "toast"
    }
  ]
}

🚫 文档6：陷阱与禁区（Traps & Forbidden）

分析维度：
- 死路：点了没跳转/只弹 Toast 的功能入口
- 不可逆操作：执行后会破坏测试状态
- 外部依赖：需要真实环境才能完成（支付/短信/网络）
- 危险操作：会删除数据、清空状态、影响其他测试

输出 JSON：
{
  "deadEnds": [
    {
      "page": "LoginFlowActivity",
      "control": "tv_register / tv_forgot",
      "reason": "点击仅弹 Toast「功能暂未开放」，不跳转",
      "action": "测试时直接跳过，不要点击"
    }
  ],
  "externalDependencies": [
    {
      "feature": "微信/支付宝登录",
      "dependency": "需要手机安装对应 App 并已登录",
      "testStrategy": "Demo 中直接跳转 HomeActivity，无真实鉴权，可测试"
    }
  ],
  "irreversibleActions": [
    {
      "action": "提交订单",
      "consequence": "FLAG_ACTIVITY_CLEAR_TOP 清栈，返回首页，页面状态重置",
      "recovery": "重新走加购流程"
    }
  ],
  "dangerous": []
}

🎯 文档7：测试策略与优先级（Test Strategy）

分析维度：
- 哪些是 P0（核心链路，必须通过才能上线）
- 哪些是 P1（重要功能，尽量覆盖）
- 哪些是 P2（边界/次要，有时间再测）
- 建议的测试执行顺序（考虑状态依赖）
- 每轮测试的最小集合（Smoke Test）

输出 JSON：
{
  "smokeTest": ["最小可行测试集，5步内验证核心链路"],
  "priorities": {
    "P0": ["必测场景，任何一个失败都是 Blocker"],
    "P1": ["重要场景"],
    "P2": ["边界/次要场景"],
    "skip": ["明确跳过的场景和原因"]
  },
  "suggestedOrder": [
    {"step": 1, "action": "先完成登录，建立已登录状态"},
    {"step": 2, "action": "走核心购买链路验证主流程"}
  ],
  "riskAreas": ["容易漏测但很重要的场景"]
}

🔧 文档8：测试数据字典（Test Data）

分析维度：
- 有效数据（能触发成功路径的数据）
- 无效数据（能触发错误提示的数据）
- 边界数据（触发边界行为的数据）
- 硬编码的特殊值（校验码/魔法数字）

输出 JSON：
{
  "validInputs": [{"field": "校验码", "values": ["CODE123"], "effect": "校验通过"}],
  "invalidInputs": [{"field": "校验码", "values": ["WRONG"], "effect": "提示无效"}],
  "boundaryValues": [
    {"field": "数值型控件", "min": 1, "max": 10, "testValues": [0, 1, 10, 11]}
  ],
  "enumOptions": {
    "选项A": ["值1", "值2", "值3"]
  },
  "specialCases": ["空字符串", "超长输入", "特殊字符", "大小写变体"]
}

🧪 文档9：可执行测试用例（Executable Test Cases）

这是最关键的文档，为 AI Agent 提供可直接执行的测试步骤，包含完整前置状态设置。

分析维度：
- 每个关键场景的完整操作序列（含前置状态设置）
- 边界值测试（最小值/最大值/非法值）
- 负向测试（应该失败的场景）
- 每个步骤后的验证点

输出 JSON：
{
  "testCases": [
    {
      "id": "tc_001",
      "name": "表单提交成功跳转（正向）",
      "page": "PageA",
      "priority": "P0",
      "category": "happy_path",
      "setup": [
        {"action": "input", "target": "et_field1", "value": "有效值", "description": "填入有效数据"},
        {"action": "input", "target": "et_field2", "value": "有效值", "description": "填入有效数据"}
      ],
      "execute": {
        "action": "tap",
        "target": "btn_submit",
        "description": "点击提交"
      },
      "verify": {
        "type": "pageSwitch",
        "expected": "跳转到 PageB",
        "failSign": "停留在 PageA 或弹错误 Toast",
        "isBug": "有效输入无法提交是 Bug"
      },
      "waitMs": 500
    },
    {
      "id": "tc_002",
      "name": "数值控件减少操作有效性验证（边界）",
      "page": "PageA",
      "priority": "P1",
      "category": "boundary",
      "setup": [
        {"action": "readValue", "target": "tv_counter", "saveAs": "beforeVal", "description": "记录操作前的值"}
      ],
      "execute": {
        "action": "tap",
        "target": "btn_minus",
        "description": "点击减少按钮"
      },
      "verify": {
        "type": "valueChange",
        "readTarget": "tv_counter",
        "compareWith": "beforeVal",
        "expected": "值减少1",
        "failSign": "值未变化（按钮无响应）",
        "isBug": "减少按钮点击后值不变是 Bug"
      },
      "waitMs": 300
    },
    {
      "id": "tc_003",
      "name": "输入框大小写不敏感验证（负向）",
      "page": "PageA",
      "priority": "P1",
      "category": "boundary",
      "setup": [],
      "execute": {
        "action": "sequence",
        "steps": [
          {"action": "input", "target": "et_code", "value": "code123", "description": "输入小写形式"},
          {"action": "tap", "target": "btn_apply", "description": "点击应用"}
        ]
      },
      "verify": {
        "type": "toast",
        "expected": "Toast含「操作成功」（大小写不敏感）",
        "failSign": "Toast含「无效」或「格式错误」",
        "isBug": "小写输入被拒绝而大写接受，说明存在大小写敏感 Bug"
      },
      "waitMs": 300
    }
  ]
}

Step 4：生成 quickSummary（最重要）

基于 9 份文档，用自然语言写一段 300 字以内的总结，直接给 AI 测试 Agent 读：

这是一个【业务类型】的 App（包名：xxx）。

【核心流程】：从 XxxActivity 开始，依次经过...最终到达...。
关键步骤：①先完成前置条件（如登录）→ ②进入目标页面 → ③满足操作前置状态 → ④执行核心操作 → ⑤验证结果。

【断言要点】：
- 登录成功：页面跳转到 HomeFlowActivity，出现「登录成功」Toast
- 核心操作成功：页面跳转到目标页面，出现「操作成功」Toast
- 流程完成：操作成功后状态更新，视情况清栈返回上一页面

【必须注意】：
- 前置条件未满足时操作会被阻断（Toast提示），需先确认 <checkbox_id> 等必要条件
- 数值型控件有范围限制，超界有 Toast 提示但不改变数值
- 死路入口（如仅弹 Toast 的功能）不要点击，避免测试中断

【测试数据】：校验码等特殊值参见文档8；账号密码任意非空字符串均可登录（Demo 无真实验证）。

【测试顺序建议】：完成登录等前置状态 → 进入目标页面 → 执行核心操作 → 验证断言。

【关键测试用例】：①必填项为空提交被阻断②数值增减按钮操作有效③大小写不敏感校验④空状态下数值显示合理

Step 5：打包 static-profile

{
  "appPackage": "com.xxx",
  "appVersion": "x.x.x",
  "generatedAt": "ISO时间戳",
  "source": "android-static-analyzer-skill",
  "quickSummary": "...（上面的自然语言总结）",
  "docs": {
    "businessOverview": {},
    "userJourneys": {},
    "assertions": {},
    "stateDependencies": {},
    "edgeCases": {},
    "trapsAndForbidden": {},
    "testStrategy": {},
    "testData": {},
    "testCases": { "testCases": [] }
  }
}

Step 6：保存到本地文件

将 static-profile.json 保存到项目根目录：

echo '<json>' > <project_path>/static-profile.json

然后告诉用户：

文件路径：<project_path>/static-profile.json
在测试平台「自动测试」页面，点「导入先验知识」按钮，选择此文件上传即可
或者平台 server 启动后执行：curl -X POST http://localhost:2601/api/v1/sdk/static-profile -H "Content-Type: application/json" -d @<project_path>/static-profile.json

不要自动 POST 到平台，由用户手动导入。

质量检查清单

生成完成后，对照检查：

quickSummary 读完能直接开始测试，不需要再查其他文档
每个关键操作都有成功和失败两种断言条件
所有边界值都有对应的测试数据和预期行为
死路已明确列出并说明跳过原因
状态依赖图能指导测试的执行顺序
不可逆操作有对应的恢复策略
外部依赖已声明（测试前需要什么环境）

输出原则

可执行 > 可描述：每条信息都要说清楚「AI 测试 Agent 拿到这个怎么用」
断言要具体：不说「登录成功跳转」，说「Activity 变为 HomeFlowActivity 且出现含「欢迎」的 Toast」
陷阱要预警：明确说「不要点 tv_register」，不只说「注册未开放」
数据要可直接用：校验码、账号、边界值都是可以直接填入输入框的值

About this skill

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/android-static-analyzer/SKILL.md

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

Illustrative Want to see what leoyeai/android-static-analyzer 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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Android Static Analyzer — AI 测试先验知识生成器

核心定位

执行步骤

Step 1：获取项目路径

Step 2：读取核心源码

Step 3：LLM 生成 8 份文档

📋 文档1：业务全景（Business Overview）

🔄 文档2：用户旅程地图（User Journey Map）

✅ 文档3：验证点与断言（Assertions）

🔗 文档4：状态依赖图（State Dependencies）

⚠️ 文档5：边界与异常场景（Edge Cases）

🚫 文档6：陷阱与禁区（Traps & Forbidden）

🎯 文档7：测试策略与优先级（Test Strategy）

🔧 文档8：测试数据字典（Test Data）

🧪 文档9：可执行测试用例（Executable Test Cases）

Step 4：生成 quickSummary（最重要）

Step 5：打包 static-profile

Step 6：保存到本地文件

质量检查清单

输出原则

SkillSafety

Source

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