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API Testing Skill · aiskillstore | DecimalAI

Testingby aiskillstore

API Testing Skill

Use when writing tests for backend APIs or frontend flows. Triggers for: unit tests, integration tests, E2E tests, pytest fixtures, TestClient setup, mock data factories, or test coverage analysis. NOT for: testing business logic that doesn't involve API endpoints.

Maintained

SkillScore

SkillScore78/100

Test results 78

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

61%78%of test cases passed +17 pts

Without skill14 of 23

With skill18 of 23

5 fixed13 passed either way4 still failing1 broke

Measured on gemini-3.6-flash9/3/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWrite a Playwright test step in TypeScript to select option '9' from a `<select name="gradeLevel">` dropdown and submit the form via a button with text 'Create'.

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 aiskillstore-api-testing
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("aiskillstore-api-testing")
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 aiskillstore-api-testing

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/aiskillstore-api-testing/SKILL.md https://app.decimal.ai/s/aiskillstore-api-testing/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aiskillstore-api-testing/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/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/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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("aiskillstore-api-testing")
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/aiskillstore-api-testing/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aiskillstore-api-testing/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 1 other file is 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 aiskillstore-api-testing

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/aiskillstore-api-testing/SKILL.md https://app.decimal.ai/s/aiskillstore-api-testing/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aiskillstore-api-testing/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/aiskillstore-api-testing/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aiskillstore-api-testing.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 9/3/2026
Source: aiskillstore/marketplace ↗
License: Not recorded

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 Testing Skill

Expert testing for FastAPI backends and React/Next.js frontends with unit, integration, and E2E test patterns.

Quick Reference

Test Type	Tool	Purpose	Scope
Unit	pytest	Pure functions, services	Isolated
Integration	pytest + TestClient	DB + auth + routes	Combined
E2E	Playwright/Cypress	Browser flows	Full stack

Project Structure

backend/
├── tests/
│   ├── __init__.py
│   ├── conftest.py              # Shared fixtures
│   ├── unit/
│   │   ├── test_services.py     # Business logic tests
│   │   └── test_utils.py        # Utility function tests
│   ├── integration/
│   │   ├── test_students.py     # Student API tests
│   │   ├── test_fees.py         # Fee API tests
│   │   └── test_auth.py         # Authentication tests
│   └── fixtures/
│       ├── students.json        # Test data
│       └── users.json
frontend/
├── e2e/
│   ├── specs/
│   │   ├── student.spec.ts
│   │   └── fee.spec.ts
│   ├── pages/
│   │   ├── DashboardPage.ts
│   │   └── StudentPage.ts
│   └── utils/
│       └── test-data.ts
└── playwright.config.ts

Backend: Pytest Setup

conftest.py (Shared Fixtures)

# backend/tests/conftest.py
import pytest
from typing import Generator
from fastapi.testclient import TestClient
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.pool import StaticPool
from app.main import app
from app.db.database import get_db, Base
from app.models import User, Student
from app.auth.jwt import create_access_token
from passlib.context import CryptContext


# Test database setup
SQLALCHEMY_DATABASE_URL = "sqlite:///:memory:"
engine = create_engine(
    SQLALCHEMY_DATABASE_URL,
    connect_args={"check_same_thread": False},
    poolclass=StaticPool,
)
TestingSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)


@pytest.fixture(scope="function")
def db_session():
    """Create a fresh database for each test."""
    Base.metadata.create_all(bind=engine)
    session = TestingSessionLocal()
    try:
        yield session
    finally:
        session.close()
        Base.metadata.drop_all(bind=engine)


@pytest.fixture(scope="function")
def client(db_session):
    """Create a test client with database override."""

    def override_get_db():
        try:
            yield db_session
        finally:
            pass

    app.dependency_overrides[get_db] = override_get_db
    with TestClient(app) as test_client:
        yield test_client
    app.dependency_overrides.clear()


@pytest.fixture
def test_user(db_session):
    """Create a test user."""
    pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
    hashed_password = pwd_context.hash("testpassword123")

    user = User(
        email="[email protected]",
        hashed_password=hashed_password,
        full_name="Test User",
        is_active=True,
    )
    db_session.add(user)
    db_session.commit()
    db_session.refresh(user)
    return user


@pytest.fixture
def auth_token(test_user):
    """Generate JWT token for test user."""
    return create_access_token(data={"sub": test_user.email, "roles": ["admin"]})


@pytest.fixture
def auth_headers(auth_token):
    """Headers with authentication token."""
    return {"Authorization": f"Bearer {auth_token}"}

Unit Tests (Pure Functions)

# backend/tests/unit/test_services.py
import pytest
from app.services.fee_calculator import calculate_fee, FeeCalculationError


class TestCalculateFee:
    """Unit tests for fee calculation logic."""

    def test_basic_fee_calculation(self):
        """Test basic fee calculation without discounts."""
        result = calculate_fee(
            base_amount=1000.00,
            grade_level=9,
            has_sibling_discount=False,
            is_new_student=False,
        )
        assert result == 1000.00

    def test_sibling_discount(self):
        """Test 10% sibling discount."""
        result = calculate_fee(
            base_amount=1000.00,
            grade_level=9,
            has_sibling_discount=True,
            is_new_student=False,
        )
        assert result == 900.00

    def test_new_student_discount(self):
        """Test 15% new student discount."""
        result = calculate_fee(
            base_amount=1000.00,
            grade_level=9,
            has_sibling_discount=False,
            is_new_student=True,
        )
        assert result == 850.00

    def test_combined_discounts(self):
        """Test combined sibling and new student discounts."""
        result = calculate_fee(
            base_amount=1000.00,
            grade_level=9,
            has_sibling_discount=True,
            is_new_student=True,
        )
        # 10% + 15% = 25% discount
        assert result == 750.00

    def test_invalid_base_amount(self):
        """Test that negative amounts raise error."""
        with pytest.raises(FeeCalculationError):
            calculate_fee(
                base_amount=-100.00,
                grade_level=9,
                has_sibling_discount=False,
                is_new_student=False,
            )

    def test_grade_level_multipliers(self):
        """Test different grade level multipliers."""
        # Elementary (1-5): 1.0x
        assert calculate_fee(1000.00, grade_level=3) == 1000.00
        # Middle (6-8): 1.1x
        assert calculate_fee(1000.00, grade_level=7) == 1100.00
        # High (9-12): 1.2x
        assert calculate_fee(1000.00, grade_level=10) == 1200.00

Integration Tests (API Endpoints)

# backend/tests/integration/test_students.py
import pytest
from fastapi import status


class TestStudentEndpoints:
    """Integration tests for student CRUD endpoints."""

    @pytest.fixture
    def create_student_payload(self):
        """Sample student creation payload."""
        return {
            "first_name": "John",
            "last_name": "Doe",
            "email": "[email protected]",
            "date_of_birth": "2008-05-15T00:00:00Z",
            "grade_level": 9,
        }

    def test_create_student_success(self, client, auth_headers, create_student_payload):
        """Test successful student creation."""
        response = client.post(
            "/api/v1/students/",
            json=create_student_payload,
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_201_CREATED
        data = response.json()
        assert data["first_name"] == "John"
        assert data["last_name"] == "Doe"
        assert "id" in data
        assert data["is_active"] is True

    def test_create_student_unauthorized(self, client, create_student_payload):
        """Test that unauthenticated requests are rejected."""
        response = client.post(
            "/api/v1/students/",
            json=create_student_payload,
        )
        assert response.status_code == status.HTTP_401_UNAUTHORIZED

    def test_create_student_invalid_email(self, client, auth_headers, create_student_payload):
        """Test validation error for invalid email."""
        payload = {**create_student_payload, "email": "invalid-email"}
        response = client.post(
            "/api/v1/students/",
            json=payload,
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY

    def test_create_student_missing_required_field(self, client, auth_headers):
        """Test validation error for missing required field."""
        payload = {
            "first_name": "John",
            # Missing last_name, email, date_of_birth, grade_level
        }
        response = client.post(
            "/api/v1/students/",
            json=payload,
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY

    def test_get_student_success(self, client, auth_headers, create_student_payload):
        """Test retrieving a student by ID."""
        # Create student first
        create_response = client.post(
            "/api/v1/students/",
            json=create_student_payload,
            headers=auth_headers,
        )
        student_id = create_response.json()["id"]

        # Retrieve student
        response = client.get(
            f"/api/v1/students/{student_id}",
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_200_OK
        assert response.json()["id"] == student_id

    def test_get_student_not_found(self, client, auth_headers):
        """Test 404 for non-existent student."""
        response = client.get(
            "/api/v1/students/99999",
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_404_NOT_FOUND

    def test_list_students_pagination(self, client, auth_headers, db_session):
        """Test student list with pagination."""
        # Create multiple students
        for i in range(5):
            payload = {
                "first_name": f"Student{i}",
                "last_name": "Test",
                "email": f"student{i}@test.edu",
                "date_of_birth": "2008-05-15T00:00:00Z",
                "grade_level": 9,
            }
            client.post("/api/v1/students/", json=payload, headers=auth_headers)

        # Get first page
        response = client.get(
            "/api/v1/students/?skip=0&limit=3",
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_200_OK
        data = response.json()
        assert len(data["data"]) == 3
        assert data["total"] == 5
        assert data["has_more"] is True

    def test_update_student(self, client, auth_headers, create_student_payload):
        """Test partial update of student."""
        # Create student
        create_response = client.post(
            "/api/v1/students/",
            json=create_student_payload,
            headers=auth_headers,
        )
        student_id = create_response.json()["id"]

        # Update student
        update_payload = {"first_name": "Jane", "grade_level": 10}
        response = client.patch(
            f"/api/v1/students/{student_id}",
            json=update_payload,
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_200_OK
        data = response.json()
        assert data["first_name"] == "Jane"
        assert data["grade_level"] == 10

    def test_delete_student(self, client, auth_headers, create_student_payload):
        """Test soft delete of student."""
        # Create student
        create_response = client.post(
            "/api/v1/students/",
            json=create_student_payload,
            headers=auth_headers,
        )
        student_id = create_response.json()["id"]

        # Delete student
        response = client.delete(
            f"/api/v1/students/{student_id}",
            headers=auth_headers,
        )
        assert response.status_code == status.HTTP_204_NO_CONTENT

        # Verify student is not in active list
        list_response = client.get(
            "/api/v1/students/",
            headers=auth_headers,
        )
        student_ids = [s["id"] for s in list_response.json()["data"]]
        assert student_id not in student_ids

Test Fixtures (JSON Data)

// backend/tests/fixtures/students.json
{
  "valid_student": {
    "first_name": "John",
    "last_name": "Doe",
    "email": "[email protected]",
    "date_of_birth": "2008-05-15T00:00:00Z",
    "grade_level": 9
  },
  "invalid_students": [
    {
      "description": "Missing first_name",
      "data": {
        "last_name": "Doe",
        "email": "[email protected]",
        "date_of_birth": "2008-05-15T00:00:00Z",
        "grade_level": 9
      }
    },
    {
      "description": "Invalid email format",
      "data": {
        "first_name": "John",
        "last_name": "Doe",
        "email": "not-an-email",
        "date_of_birth": "2008-05-15T00:00:00Z",
        "grade_level": 9
      }
    },
    {
      "description": "Grade level out of range",
      "data": {
        "first_name": "John",
        "last_name": "Doe",
        "email": "[email protected]",
        "date_of_birth": "2008-05-15T00:00:00Z",
        "grade_level": 15
      }
    }
  ]
}

Frontend: E2E Tests (Playwright)

playwright.config.ts

// frontend/playwright.config.ts
import { defineConfig, devices } from "@playwright/test";

export default defineConfig({
  testDir: "./e2e/specs",
  fullyParallel: true,
  forbidOnly: !!process.env.CI,
  retries: process.env.CI ? 2 : 0,
  workers: process.env.CI ? 1 : undefined,
  reporter: "html",
  use: {
    baseURL: "http://localhost:3000",
    trace: "on-first-retry",
  },
  projects: [
    {
      name: "chromium",
      use: { ...devices["Desktop Chrome"] },
    },
    {
      name: "firefox",
      use: { ...devices["Desktop Firefox"] },
    },
    {
      name: "webkit",
      use: { ...devices["Desktop Safari"] },
    },
  ],
  webServer: {
    command: "npm run dev",
    url: "http://localhost:3000",
    reuseExistingServer: !process.env.CI,
  },
});

E2E Test Specification

// frontend/e2e/specs/student.spec.ts
import { test, expect } from "@playwright/test";

test.describe("Student Management", () => {
  test.beforeEach(async ({ page }) => {
    // Navigate to login page
    await page.goto("/login");

    // Login as admin
    await page.fill('input[name="email"]', "[email protected]");
    await page.fill('input[name="password"]', "adminpassword");
    await page.click('button[type="submit"]');

    // Verify login success
    await expect(page).toHaveURL(/\/dashboard/);
    await expect(page.locator("text=Admin")).toBeVisible();
  });

  test("should create a new student successfully", async ({ page }) => {
    // Navigate to students page
    await page.click('a[href="/students"]');
    await expect(page).toHaveURL(/\/students/);

    // Click add student button
    await page.click('button:has-text("Add Student")');

    // Fill in student form
    await page.fill('input[name="firstName"]', "John");
    await page.fill('input[name="lastName"]', "Doe");
    await page.fill('input[name="email"]', "[email protected]");
    await page.fill('input[name="dateOfBirth"]', "2008-05-15");

    // Select grade level
    await page.selectOption('select[name="gradeLevel"]', "9");

    // Submit form
    await page.click('button:has-text("Create")');

    // Verify student was created
    await expect(page.locator("text=Student created successfully")).toBeVisible();

    // Verify student appears in list
    await expect(page.locator("text=John Doe")).toBeVisible();
  });

  test("should show validation errors for invalid input", async ({ page }) => {
    await page.click('a[href="/students"]');
    await page.click('button:has-text("Add Student")');

    // Submit empty form
    await page.click('button:has-text("Create")');

    // Verify validation errors
    await expect(page.locator("text=First name is required")).toBeVisible();
    await expect(page.locator("text=Last name is required")).toBeVisible();
    await expect(page.locator("text=Invalid email address")).toBeVisible();
  });

  test("should filter students by grade level", async ({ page }) => {
    await page.click('a[href="/students"]');

    // Filter by grade 9
    await page.selectOption('select[name="gradeFilter"]', "9");
    await page.click('button:has-text("Apply")');

    // Verify only grade 9 students shown
    const rows = page.locator("table.student-list tbody tr");
    await expect(rows).toHaveCount(3); // Assuming 3 grade 9 students
  });

  test("should view student details", async ({ page }) => {
    await page.click('a[href="/students"]');

    // Click on first student
    await page.click('table.student-list tbody tr:first-child a');

    // Verify details page
    await expect(page).toHaveURL(/\/students\/\d+/);
    await expect(page.locator("h1")).toContainText("Student Details");
  });
});

Page Object Model

// frontend/e2e/pages/StudentsPage.ts
import { Page, Locator, expect } from "@playwright/test";

export class StudentsPage {
  readonly page: Page;
  readonly addButton: Locator;
  readonly studentTable: Locator;
  readonly gradeFilter: Locator;
  readonly searchInput: Locator;

  constructor(page: Page) {
    this.page = page;
    this.addButton = page.locator('button:has-text("Add Student")');
    this.studentTable = page.locator("table.student-list");
    this.gradeFilter = page.locator('select[name="gradeFilter"]');
    this.searchInput = page.locator('input[name="search"]');
  }

  async goto() {
    await this.page.goto("/students");
  }

  async createStudent(data: {
    firstName: string;
    lastName: string;
    email: string;
    gradeLevel: string;
    dateOfBirth?: string;
  }) {
    await this.addButton.click();
    await this.page.fill('input[name="firstName"]', data.firstName);
    await this.page.fill('input[name="lastName"]', data.lastName);
    await this.page.fill('input[name="email"]', data.email);
    await this.page.selectOption('select[name="gradeLevel"]', data.gradeLevel);
    if (data.dateOfBirth) {
      await this.page.fill('input[name="dateOfBirth"]', data.dateOfBirth);
    }
    await this.page.click('button:has-text("Create")');
  }

  async getStudentNames(): Promise<string[]> {
    const rows = this.studentTable.locator("tbody tr");
    const names: string[] = [];
    for (const row of await rows.all()) {
      names.push(await row.locator("td:first-child").textContent());
    }
    return names;
  }

  async filterByGrade(grade: string) {
    await this.gradeFilter.selectOption(grade);
    await this.page.click('button:has-text("Apply")');
  }

  async searchByName(name: string) {
    await this.searchInput.fill(name);
    await this.page.keyboard.press("Enter");
  }
}

Test Pyramid

        /\
       /  \      E2E Tests (10%)
      /    \     - Critical user journeys
     /______\
    /        \
   /          \   Integration Tests (30%)
  /            \  - API endpoints with DB
 /______________\
/                \
/                  \ Unit Tests (60%)
/                    \ - Services, utilities
/______________________\

Quality Checklist

Happy path + edge cases: Test both success and error scenarios
CI compatible: Tests run in CI pipeline without manual setup
Deterministic: No flaky tests, no random failures
Coverage: 80%+ for core modules, 90%+ for critical paths
No real secrets: Use test credentials, never production keys
No production DB: Use test database or in-memory SQLite
Isolated: Tests don't depend on each other
Fast: Unit tests < 100ms, integration < 1s

Running Tests

# Backend tests
pytest                           # Run all tests
pytest tests/unit/              # Unit tests only
pytest tests/integration/       # Integration tests only
pytest -v                       # Verbose output
pytest --cov=app               # With coverage

# Frontend E2E tests
npx playwright install           # Install browsers
npx playwright test             # Run E2E tests
npx playwright test --reporter=line

CI Configuration

# .github/workflows/test.yml
name: Tests

on: [push, pull_request]

jobs:
  test-backend:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - run: pip install -r requirements.txt
      - run: pip install pytest pytest-cov
      - run: pytest --cov=app --cov-report=xml
      - uses: codecov/codecov-action@v3

  test-frontend:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: "20"
      - run: npm ci
      - run: npm run test
      - run: npm run build

Integration Points

Skill	Integration
`@sqlmodel-crud`	Test CRUD operations with test database
`@jwt-auth`	Test authenticated endpoints with test tokens
`@api-route-design`	Test all CRUD routes with various status codes
`@error-handling`	Test error responses and edge cases
`@data-validation`	Test validation error messages

About this skill

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0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

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LLM judge · does the skill do what it claims

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Source

Imported

aiskillstore / marketplace

skills/awais68/api-testing/SKILL.md

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Imported 8/10/2026Version v1Bundled 1 file · scripts

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API Testing Skill

Quick Reference

Project Structure

Backend: Pytest Setup

conftest.py (Shared Fixtures)

Unit Tests (Pure Functions)

Integration Tests (API Endpoints)

Test Fixtures (JSON Data)

Frontend: E2E Tests (Playwright)

playwright.config.ts

E2E Test Specification

Page Object Model

Test Pyramid

Quality Checklist

Running Tests

CI Configuration

Integration Points

SkillSafety

Source

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