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API测试分析文档生成器 · leoyeai | DecimalAI

Testingby leoyeai

API测试分析文档生成器

当用户需要根据接口规格生成API测试分析文档时使用此技能。接受OpenAPI/Swagger文档、简化接口定义或自然语言描述，基于140个常见API测试陷阱输出全面的测试分析文档，涵盖参数校验、业务逻辑、响应验证和安全测试分析点。

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

41%73%of test cases passed +32 pts

Without skill9 of 22

With skill16 of 22

7 fixed9 passed either way6 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 taskCreate the final summary section for a single API test analysis report for comment creation endpoint `POST /api/v1/comments`.

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-api-test-create
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-api-test-create")
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-api-test-create

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-api-test-create/SKILL.md https://app.decimal.ai/s/leoyeai-api-test-create/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-api-test-create/SKILL.md

This gets SKILL.md only. The skill's 4 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-api-test-create

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-api-test-create")
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-api-test-create/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-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/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-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create")
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-api-test-create/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-api-test-create/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 4 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-api-test-create

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-api-test-create/SKILL.md https://app.decimal.ai/s/leoyeai-api-test-create/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-api-test-create/SKILL.md

This gets SKILL.md only. The skill's 4 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-api-test-create/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-api-test-create.json

This gets SKILL.md only. The skill's 4 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

API测试分析文档生成器

目的

根据接口规格定义生成全面的API测试分析文档。将API定义转换为结构化的测试分析，确保覆盖参数校验、业务逻辑、响应验证和常见陷阱的测试点分析。

使用时机

在以下情况下使用此技能：

为新API端点设计测试策略时
审查API测试覆盖完整性时
需要测试分析文档供团队评审时
针对常见陷阱审计API质量时
生成测试方案和测试点清单时

工作流程

步骤1：解析接口定义

从输入中提取：

基本信息：端点路径、HTTP方法、描述
请求参数：名称、类型、约束、必填/可选
返回字段：名称、类型、描述
业务规则：校验规则、状态转换、权限

支持的输入格式：

OpenAPI/Swagger JSON/YAML
简化接口定义（Markdown格式）
自然语言描述

步骤2：生成参数校验测试点分析

为每个参数基于类型生成测试点分析：

参数类型	测试点分析维度
string	正常值、边界长度、空值、null、特殊字符、SQL注入风险、XSS风险、Unicode字符、编码问题
integer	正常值、最小/最大边界、零值、负数、小数（类型错误）、溢出、精度问题
number	正常值、精度、科学计数法、边界值、NaN、Infinity
enum	所有有效值、无效值、大小写敏感、null、空字符串、数字枚举
boolean	true/false、字符串转换、数字转换、null、其他值
array	空数组、单元素、多元素、最大元素数、重复元素、无效元素、嵌套数组
object	完整对象、缺少必填字段、额外字段、空对象、嵌套对象深度
datetime	当前时间、过去时间、未来时间、闰年、无效日期、时区、格式错误、时间精度

步骤3：生成业务逻辑测试点分析

识别规则模式并生成测试分析场景：

规则模式	测试点分析
存在性校验	数据存在、不存在、已删除、逻辑删除、缓存不一致
唯一性校验	首次创建、重复尝试、并发创建、大小写敏感、特殊字符
状态转换	合法转换、非法转换、循环状态、终态、初始状态
权限校验	已授权、未授权、未登录、权限变更、角色变更、越权访问
关联校验	关联数据存在、不存在、已删除、外键约束、级联操作
数量限制	达到上限、超过上限、低于下限、批量操作限制、分页限制
时间窗口	有效期内、已过期、即将过期、时区影响

步骤4：生成响应验证测试点分析

接口类型	验证点分析
列表查询	结构完整性、分页字段、字段类型、空列表返回[]、null处理、排序
详情查询	必填字段存在、字段类型正确、404处理、null字段、关联数据完整性
创建	返回新ID、数据库验证、默认值、时间戳、返回字段完整性、状态码
更新	成功状态、数据变更验证、未变更字段保持原值、部分更新、全量更新
删除	成功状态、记录删除验证、软删除/硬删除、后续查询返回404、级联删除
文件上传	文件大小限制、文件类型、空文件、超大文件、文件名、多文件
文件下载	内容类型、文件名、文件完整性、断点续传、大文件分片

步骤5：生成安全测试点分析

必须包含的安全测试分析：

SQL注入：参数拼接、特殊字符转义、ORM框架使用、存储过程
XSS攻击：输入过滤、输出转义、富文本处理、CSP策略
认证安全：Token强度、Token过期、Refresh机制、会话管理、暴力破解防护
授权安全：越权访问、水平越权、垂直越权、权限继承、权限缓存
敏感数据：密码加密存储、身份证号脱敏、日志脱敏、传输加密
CSRF防护：Token验证、Referer检查、SameSite Cookie、双重提交
信息泄露：错误信息、堆栈跟踪、调试模式、敏感接口暴露
速率限制：IP限流、用户限流、接口限流、突发流量、熔断降级

步骤6：生成性能测试点分析

适用时的性能测试分析：

响应时间阈值：P95、P99、平均响应、最大响应、超时设置
大数据量处理：分页深度、批量操作、大数据返回、内存占用
并发访问：锁竞争、死锁、连接池、线程池、异步处理
资源消耗：CPU、内存、磁盘IO、网络IO、文件句柄
缓存策略：缓存命中率、缓存穿透、缓存雪崩、缓存一致性
数据库性能：慢查询、索引使用、连接数、事务、锁等待

步骤7：对照常见陷阱检查

最终确定前，验证Top 20必检项的覆盖：

类别	必检项	分析重点
参数	缺少必填、类型/格式错误、边界值	验证是否考虑完整
权限	未认证访问、未授权访问	权限矩阵是否覆盖
安全	SQL注入、XSS、敏感数据暴露	安全场景是否分析
业务	幂等性、唯一性、状态转换	业务规则是否完整
响应	状态码一致、字段完整、空结果	响应契约是否验证
性能	响应阈值、分页限制、批量限制	性能指标是否定义

参考 references/common-pitfalls.md 获取完整的140点清单。

输出格式

单接口测试分析格式

## API: {接口名称}

### 基本信息
- **端点**：{METHOD} {路径}
- **描述**：{接口描述}
- **标签**：{Swagger标签}
- **认证**：{是否需要认证}

### 请求参数分析

#### 1. {参数名} ({类型}, {必填/可选})
**约束条件**：{长度、格式、枚举值等}

**参数校验测试点分析**：
| 测试场景 | 测试值 | 预期结果 | 优先级 | 测试目的 |
|----------|--------|----------|--------|----------|
| 正常值 | {valid} | 成功 | P0 | 验证基本功能 |
| 边界值-最小 | {min} | 成功 | P1 | 验证下边界 |
| 边界值-最大 | {max} | 成功 | P1 | 验证上边界 |
| 边界值-超过 | {max+1} | 参数错误 | P1 | 验证越界处理 |
| 空值 | "" | 参数错误 | P0 | 验证空字符串 |
| null | null | 参数错误 | P0 | 验证null处理 |
| 特殊字符 | {special} | 参数错误 | P0 | 验证特殊字符过滤 |
| SQL注入 | {sql} | 参数错误 | P0 | 验证SQL注入防护 |
| XSS攻击 | {xss} | 参数错误 | P0 | 验证XSS防护 |

**风险分析**：{高/中/低}

#### 2. {参数名} ({类型}, {必填/可选})
...

### 业务逻辑测试点分析

#### 场景1：{业务场景名称}
**前置条件**：{条件描述}

| 操作步骤 | 输入数据 | 预期结果 | 优先级 | 测试目的 |
|----------|----------|----------|--------|----------|
| 步骤1 | {data1} | {result1} | P0 | {purpose1} |
| 步骤2 | {data2} | {result2} | P1 | {purpose2} |

**业务规则验证**：{规则描述}
**数据一致性检查**：{检查点}

#### 场景2：{业务场景名称}
...

### 响应验证测试点分析

#### 成功响应（2xx）
**状态码**：201 Created

**响应体验证**：
| 字段 | 类型 | 验证内容 | 优先级 |
|------|------|----------|--------|
| code | Integer | 等于200 | P0 |
| message | String | 包含"成功" | P0 |
| data | Object | 不为null | P0 |
| data.id | Long | 大于0 | P0 |
| data.createTime | DateTime | 不为null，格式正确 | P1 |

**数据完整性验证**：
- 数据库中记录已创建
- 时间戳字段自动填充
- 关联数据正确建立

#### 错误响应（4xx）
**状态码**：400 Bad Request

| 错误场景 | 响应体验证 | 优先级 |
|----------|------------|--------|
| 参数缺失 | code=400, message包含"必填" | P0 |
| 参数格式错误 | code=400, message包含"格式" | P0 |
| 业务规则违反 | code=409, message包含"已存在" | P0 |

#### 异常响应（5xx）
**状态码**：500 Internal Server Error

| 异常场景 | 响应体验证 | 日志验证 | 优先级 |
|----------|------------|----------|--------|
| 数据库异常 | code=500, message不包含敏感信息 | 记录错误堆栈 | P0 |
| 外部服务异常 | code=503, 触发熔断 | 记录熔断日志 | P1 |

### 安全测试点分析

#### 认证安全
| 测试场景 | 测试操作 | 预期结果 | 风险等级 |
|----------|----------|----------|----------|
| 未认证访问 | 不携带Token调用接口 | 返回401 Unauthorized | 高 |
| Token过期 | 使用过期Token | 返回401, message提示Token过期 | 高 |
| Token篡改 | 修改Token签名 | 返回401, message提示Token无效 | 高 |
| Token刷新 | 使用RefreshToken获取新Token | 返回新Token, 旧Token失效 | 中 |

#### 授权安全
| 测试场景 | 测试操作 | 预期结果 | 风险等级 |
|----------|----------|----------|----------|
| 越权访问 | 用户A访问用户B的数据 | 返回403 Forbidden | 高 |
| 垂直越权 | 普通用户访问管理员接口 | 返回403 Forbidden | 高 |
| 权限变更 | 权限变更后访问接口 | 按新权限控制 | 中 |

#### 数据安全
| 测试场景 | 测试操作 | 预期结果 | 风险等级 |
|----------|----------|----------|----------|
| SQL注入 | 输入SQL特殊字符 | 返回400, 不被注入 | 高 |
| XSS攻击 | 输入<script>标签 | 返回400, 内容被转义 | 高 |
| 敏感数据泄露 | 检查响应字段 | 密码、密钥不返回 | 高 |
| 日志脱敏 | 检查日志文件 | 敏感信息被脱敏 | 中 |

### 性能测试点分析

#### 响应时间要求
- **P0优先级接口**：< 200ms (95分位)
- **P1优先级接口**：< 500ms (95分位)
- **P2优先级接口**：< 1000ms (95分位)
- **P3优先级接口**：< 2000ms (95分位)

#### 压力测试场景
| 场景 | 并发数 | 持续时间 | 预期结果 | 关注点 |
|------|--------|----------|----------|--------|
| 基准测试 | 1 | 10分钟 | 响应稳定 | 基准响应时间 |
| 负载测试 | 10 | 30分钟 | 成功率>99.9% | 系统负载 |
| 压力测试 | 50 | 15分钟 | 成功率>99% | 系统瓶颈 |
| 峰值测试 | 100 | 5分钟 | 不崩溃 | 最大承载 |

#### 性能监控指标
- **响应时间**：平均、P95、P99、最大值
- **吞吐量**：TPS、QPS
- **错误率**：4xx、5xx比例
- **资源使用率**：CPU、内存、磁盘IO、网络IO
- **数据库指标**：连接数、慢查询、锁等待

### 兼容性测试点分析

#### 客户端兼容性
| 客户端类型 | 版本范围 | 测试重点 | 优先级 |
|------------|----------|----------|--------|
| Web浏览器 | Chrome, Firefox, Safari, Edge | 最新+前2个版本 | P1 |
| 移动端 | iOS, Android | 主流版本 | P2 |
| 桌面应用 | Windows, macOS, Linux | 主流版本 | P2 |

#### 数据格式兼容性
| 格式类型 | 测试内容 | 预期结果 | 优先级 |
|----------|----------|----------|--------|
| JSON | 标准JSON、嵌套对象、数组 | 正确解析 | P0 |
| XML | 支持情况 | 正确解析或返回415 | P2 |
| Form | application/x-www-form-urlencoded | 正确解析 | P1 |
| Multipart | multipart/form-data | 正确解析 | P1 |

### 测试数据设计

#### 正向测试数据
```json
{
  "username": "testuser",
  "password": "Test123456",
  "email": "[email protected]",
  "phone": "13800138000"
}

边界测试数据

{
  "username": "a",  // 最小长度
  "username": "a" * 20,  // 最大长度
  "username": "a" * 21  // 超过最大长度
}

异常测试数据

{
  "username": null,  // null值
  "username": "",  // 空字符串
  "username": "<script>alert(1)</script>",  // XSS
  "username": "admin'--",  // SQL注入
  "username": "../../etc/passwd"  // 路径遍历
}

测试执行建议

执行优先级

P0测试点（核心功能）：100%执行，必须全部通过
P1测试点（重要功能）：100%执行，通过率>95%
P2测试点（一般功能）：80%执行，通过率>90%
P3测试点（次要功能）：50%执行，通过率>80%

执行阶段

冒烟测试：所有P0测试点，30分钟
功能测试：P0+P1测试点，2天
深度测试：P0+P1+P2测试点，3天
回归测试：全量测试点，1天

执行环境

开发环境：功能验证、快速反馈
测试环境：完整测试、集成测试
预生产环境：性能测试、安全测试
生产环境：冒烟测试、监控验证

风险分析

高风险

Token认证机制
权限控制体系
敏感数据处理
支付/交易接口

中风险

业务数据完整性
第三方服务集成
文件上传下载
批量操作

低风险

查询统计接口
只读数据接口
静态资源配置

总结

测试点统计

参数校验测试点：X个
业务逻辑测试点：X个
响应验证测试点：X个
安全测试点：X个
性能测试点：X个
总计：X个测试点

测试覆盖

功能覆盖：100%
参数覆盖：100%
业务规则覆盖：100%
安全场景覆盖：100%
性能指标覆盖：80%

建议

优先执行P0和P1测试点
重点关注安全测试点
在生产环境执行冒烟测试
建立自动化测试回归套件
定期评审测试点有效性


### 多接口测试分析格式

```markdown
# {系统名称} API测试分析报告

## 文档信息
- **生成日期**：{date}
- **接口总数**：{count}
- **测试点总数**：{total_points}
- **生成工具**：api-test-create v{version}

## 接口清单

| 序号 | 接口名称 | 路径 | 方法 | 优先级 | 测试点数 |
|------|----------|------|------|--------|----------|
| 1 | 用户注册 | /api/users | POST | P0 | 32 |
| 2 | 用户登录 | /api/auth/login | POST | P0 | 28 |
| ... | ... | ... | ... | ... | ... |

## 模块划分

### 1. 认证管理模块
**接口数量**：6个
**风险等级**：🔴 高
**测试重点**：Token认证、密码安全、输入验证
**测试点数**：150+

### 2. 产品管理模块
**接口数量**：6个
**风险等级**：🟡 中
**测试重点**：CRUD操作、数据一致性、分页逻辑
**测试点数**：120+

## 测试策略

### 测试优先级
- **P0（Critical）**：核心功能，必须测试通过
- **P1（High）**：重要功能，影响用户体验
- **P2（Medium）**：一般功能，优化体验
- **P3（Low）**：次要功能，可后期补充

### 测试类型
1. **功能测试**：验证业务逻辑正确性
2. **参数测试**：验证输入验证机制
3. **安全测试**：验证安全防护能力
4. **性能测试**：验证系统性能指标
5. **兼容性测试**：验证多环境兼容性

## 详细测试分析

### 1.1 用户注册接口

#### 基本信息
- **端点**：POST /api/users
- **描述**：创建新用户账号
- **认证**：否
- **模块**：认证管理

#### 参数校验测试点分析
...

#### 业务逻辑测试点分析
...

（后续内容同单接口格式）

## 测试数据设计

### 正向数据
```json
{
  "username": "valid_user",
  "password": "Valid123",
  "email": "[email protected]"
}

边界数据

{
  "username": "a",          // 最小长度
  "username": "a" * 20,     // 最大长度
  "username": "a" * 21      // 超过最大长度
}

异常数据

{
  "username": null,         // null值
  "username": "",           // 空字符串
  "username": "<script>",   // XSS
  "username": "admin'--"    // SQL注入
}

测试执行建议

第一阶段：冒烟测试（2小时）

执行所有P0测试点，验证核心功能

第二阶段：功能测试（2天）

执行P0+P1测试点，覆盖主要业务场景

第三阶段：深度测试（3天）

执行P0+P1+P2测试点，包括性能和安全

第四阶段：回归测试（1天）

全量测试点回归，确保修改不影响已有功能

测试环境要求

硬件要求

CPU：4核以上
内存：8GB以上
硬盘：50GB可用空间

软件要求

JDK 11+
MySQL 8.0+
Redis 6.0+
Git 2.0+

风险分析

高风险接口

认证相关接口（登录、注册、Token刷新）
支付/交易接口
权限管理接口

中风险接口

核心业务数据操作
第三方服务集成
文件上传下载

低风险接口

查询统计接口
只读数据接口
静态资源配置

总结

测试点统计

参数校验：XXX个
业务逻辑：XXX个
响应验证：XXX个
安全测试：XXX个
性能测试：XXX个
总计：XXXX个测试点

建议

建立测试用例管理系统
实施自动化测试回归
定期进行安全扫描
监控生产环境性能指标
持续优化测试策略

文档生成日期：{date} 生成工具：api-test-create v{version} 下次评审：建议每月评审一次


## 参考资源

| 文档 | 用途 |
|------|------|
| `references/test-case-design.md` | 详细的测试用例设计方法和代码示例 |
| `references/common-pitfalls.md` | 完整的140点API测试陷阱清单 |
| `examples/openapi-example.yaml` | OpenAPI格式示例 |
| `examples/simple-example.md` | 简化定义格式示例 |

About this skill

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skills/apitestcreate/SKILL.md

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API测试分析文档生成器

目的

使用时机

工作流程

步骤1：解析接口定义

步骤2：生成参数校验测试点分析

步骤3：生成业务逻辑测试点分析

步骤4：生成响应验证测试点分析

步骤5：生成安全测试点分析

步骤6：生成性能测试点分析

步骤7：对照常见陷阱检查

输出格式

单接口测试分析格式

边界测试数据

异常测试数据

测试执行建议

执行优先级

执行阶段

执行环境

风险分析

高风险

中风险

低风险

总结

测试点统计

测试覆盖

建议

边界数据

异常数据

测试执行建议

第一阶段：冒烟测试（2小时）

第二阶段：功能测试（2天）

第三阶段：深度测试（3天）

第四阶段：回归测试（1天）

测试环境要求

硬件要求

软件要求

风险分析

高风险接口

中风险接口

低风险接口

总结

测试点统计

建议

SkillSafety

Source

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