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AI QA Review · petrkindlmann | DecimalAI

Testingby petrkindlmann

AI QA Review

Review EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing.

Maintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

59%77%of test cases passed +18 pts

Without skill13 of 22

With skill17 of 22

5 fixed12 passed either way4 still failing1 broke

Measured on gemini-3.8-flash11/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe want to write a high-throughput load benchmark using k6 or Autocannon against our production checkout HTTP cluster to measure p99 latency under 10,000 requests per second. Can you generate the k6 ramp-up stages, virtual user executor scripts, and statistical histogram plots?

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 petrkindlmann-ai-qa-review
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("petrkindlmann-ai-qa-review")
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 petrkindlmann-ai-qa-review

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/petrkindlmann-ai-qa-review/SKILL.md https://app.decimal.ai/s/petrkindlmann-ai-qa-review/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/petrkindlmann-ai-qa-review/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/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/petrkindlmann-ai-qa-review/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add petrkindlmann-ai-qa-review

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("petrkindlmann-ai-qa-review")
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/petrkindlmann-ai-qa-review/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/petrkindlmann-ai-qa-review/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add petrkindlmann-ai-qa-review

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/petrkindlmann-ai-qa-review/SKILL.md https://app.decimal.ai/s/petrkindlmann-ai-qa-review/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/petrkindlmann-ai-qa-review/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/petrkindlmann-ai-qa-review/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/petrkindlmann-ai-qa-review.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/2/2026
Source: petrkindlmann/qa-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

Before starting: Check for .agents/qa-project-context.md in the project root. It contains test framework conventions, naming patterns, and project-specific quality standards that calibrate review feedback.

Quick Route

Three distinct entry paths. Pick the row, then jump to the named section.

Situation	Path	Jump to
PR with changed test files	Run the changed files, score them, check the diff against the PR checklist	Verification → PR Review Checklist
Whole suite needs a health pass	Quantify, sample, find the 3-5 systemic smells, propose lint/mutation gates	Batch Audit Process
Application code, "why is this hard to test?"	Flag DI / side-effect / pure-function / interface problems with before/after	Testability Analysis

All three share the same smell vocabulary (the six buckets below) and the same Verification commands.

Discovery Questions

First, read .agents/qa-project-context.md if present and skip any question it already answers.

Review scope: Reviewing test code for quality, application code for testability, or both? Each triggers a different Quick Route path.
Framework conventions: What test framework (Jest, Vitest, Playwright, pytest)? Conventions differ — describe/it nesting, fixture usage, assertion style — and the Verification commands are per-framework.
PR review or batch audit? A PR review runs and scores only the changed files. A batch audit scans the entire suite for systemic patterns.
Existing quality standards: Does the team have documented test conventions? Check for .eslintrc test rules, CONTRIBUTING.md test guidelines, or a test style guide.
Known pain points: Recurring flaky tests, slow suites, unclear failures? These prioritize which smells to focus on first.

Core Principles

Test code is production code. Apply the same quality standards: readability, maintainability, single responsibility. Test code that is hard to read is hard to trust.
Review what is asserted, not just what is executed. Coverage proves a line ran; it says nothing about whether a wrong value would be caught. A 95%-coverage suite of toBeTruthy assertions catches almost nothing. Mutation score (see Verification) measures the thing coverage can't.
Testability review prevents test debt. Reviewing application code for testability catches design problems before they force awkward test workarounds. If code is hard to test, it is usually hard to maintain.
Codify patterns, not just knowledge. Turn recurring review feedback into lint rules, custom ESLint plugins, or shared fixtures. Reviews that repeat the same feedback indicate missing automation.
Smells are symptoms, not verdicts. A test smell indicates a potential problem; context decides whether it is actually harmful. A long test for a complex workflow may be appropriate. A mock-heavy test for a boundary may be correct.
Actionable feedback only. Every review comment must include what is wrong, why it matters, and how to fix it. "This test is bad" is not actionable. "This test uses sleep-based waiting which causes flakiness — replace with an explicit wait condition" is.

Test Smell Buckets

Six dimensions. Each smell is categorized by the dimension it affects and links to a specific review action. Full SMELL/FIX code for every catalogued smell lives in references/smell-examples.md — keep the inline pointers prominent; the before/after code is the load-bearing part.

Readability Smells

Problems that make tests hard to understand at a glance.

Obscure Setup

What it looks like: 30+ lines of object construction with irrelevant fields drowning the test intent. The reader cannot tell which fields matter for the assertion.

Fix: Extract to factories. Only test-relevant data should appear in the test body: buildOrder({ items: [buildItem({ weight: 2.5, quantity: 2 })] }) instead of constructing full user/product/order objects inline.

Review action: Request factory extraction.

Mystery Guest

What it looks like: loadFixture('report.json') — the test depends on external data the reader cannot see. They must open another file to understand the assertion.

Fix: Inline the test-relevant data or use descriptively named fixtures. The reader should understand the test without opening other files.

Review action: Request inline data or descriptive fixture names.

Duplicate Assertions

What it looks like: Multiple tests assert the same behavior with varying specificity (toBe('Alice'), toHaveProperty('name'), toBeDefined()). Three tests, one behavior.

Review action: Request consolidation. Keep the most specific assertion. Redundant tests increase maintenance cost without increasing confidence.

Reliability Smells

Problems that cause tests to fail intermittently or in unexpected environments.

Sleep-Based Waiting

What it looks like: setTimeout, sleep(), waitForTimeout() used for synchronization. See references/smell-examples.md for the SMELL/FIX pair (replace waitForTimeout with an explicit toBeVisible wait).

Review action: Reject. Sleep-based waiting is never acceptable. Require explicit wait conditions.

Order Dependency

What it looks like: Tests pass when run together but fail in isolation or different order. See references/smell-examples.md for the SMELL/FIX pair (each test creating its own preconditions).

Review action: Request data isolation. Each test must create its own preconditions.

External Service Coupling

What it looks like: Tests call real external APIs (payment gateways, email providers, third-party services). See references/smell-examples.md for the SMELL/FIX pair (mocking the service boundary).

Review action: Request mock or fake at the service boundary. External calls belong in integration/contract tests, not unit tests.

Diagnostic Smells

Problems that make test failures hard to understand and debug.

Weak Assertion Messages

What it looks like: Assertion fails with no context about what was expected or why. See references/smell-examples.md for the SMELL/FIX pair (replacing toBe(true) with specific assertions like expect(result.errors).toEqual([]) that surface the offending value).

Review action: Request stronger assertions with diagnostic value. The failure message should explain the problem without reading the test source.

Multiple Failure Causes Per Test

What it looks like: A single test covers multiple independent behaviors. When it fails, you do not know which behavior broke. See references/smell-examples.md for the SMELL/FIX pair (splitting a lifecycle test into one-behavior-per-test).

Review action: Request test splitting. Each test should have one reason to fail.

Design Smells

Problems in test architecture that increase maintenance cost.

Conditional Test Logic

What it looks like: if/else, switch, ternaries, or for loops inside test bodies. Branching logic in a test is itself untested — you cannot tell which cases actually ran. See references/smell-examples.md for the SMELL/FIX pair (converting a branching loop into it.each).

Review action: Request parameterized tests (it.each / test.each). Conditional logic in tests hides which cases are actually verified.

Giant Fixtures

What it looks like: A beforeEach or fixture that sets up 20+ objects for every test, even though each test uses 2-3 of them. See references/smell-examples.md for the SMELL/FIX pair (replacing a monolithic beforeEach with per-test inline setup).

Review action: Request inline setup. Move shared setup to factories, not monolithic beforeEach blocks.

Over-Mocking

What it looks like: Every collaborator is mocked, including simple value objects and pure functions. See references/smell-examples.md for the SMELL/FIX pair (dropping a mock of the very function under test).

Review action: Request removal of unnecessary mocks. Mock boundaries, not internals.

AI-Generated Test Smells

When the test code came from a coding agent (Claude Code, Codex, Cursor, Copilot), the smell taxonomy is the same — but a few signature failures recur often enough to deserve their own pass.

Smell	Detection
Hallucinated locator	Run the test against a real page once. If the locator never matches, the LLM invented a `data-testid` that doesn't exist.
Fabricated import	Static-check every imported symbol — does the file or package actually export it? LLMs invent plausible APIs (`@testing-library/something-that-doesnt-exist`).
Generic test data	`example.com`, `[email protected]`, `Lorem ipsum`, `John Doe` — boilerplate the agent generated because it had no project-specific factory. Replace with the project's data factory.
Closed AI loop	Both implementation and tests authored by the same agent in the same session. The tests just describe what the agent produced; they don't constrain it. Pair the agent's tests with at least one human-authored boundary test, or use TDD (test-first) per `shift-left-testing`. A low mutation score (see Verification) is the objective tell.
Project-convention drift	Page Object, fixture, naming, or assertion style different from the rest of the suite. AI-generated code rarely matches local conventions out of the box.

For first-time test generation patterns and the Step-7 review checklist, cross-link ai-test-generation. For AI-system eval suites (the equivalent of ESLint for prompts), wire each tool's CLI runner — promptfoo eval, deepeval test run (Apache 2.0), Ragas ragas evaluate / experiments (Apache 2.0) — as quality gates parallel to your test runner.

Promptfoo ownership note: Promptfoo was acquired by OpenAI (announced 9 Mar 2026). The core stays MIT-licensed, open source, and model-agnostic; red-team capabilities are being folded into OpenAI Frontier. promptfoo eval is still the correct quality-gate command — just expect the vendor to be OpenAI going forward.

Coverage Smells

Problems that leave gaps in what is verified.

Happy Path Only

What it looks like: Every test provides valid input and expects success. No error paths tested. See references/smell-examples.md for the SMELL/FIX pair (adding zero, max, negative, and boundary cases to a discount calculator).

Review action: Request missing scenarios. Use the BOUNDARY framework: Boundary values, Null/empty, Duplicates, Ordering, Range limits.

Missing Boundary Cases

What it looks like: Tests for "normal" values (5 items) but not for 0, 1, max, or max+1. Use it.each to cover boundaries explicitly: empty collection, single item, exact page size, one over, large set.

Review action: Request boundary tests. Every numeric parameter, string length, and collection size has boundaries to test.

Missing Error/Negative Cases

What it looks like: No tests for what happens when things go wrong — network failures, invalid input, permission denied, concurrent modification.

Review action: For each happy path test, ask: "What is the corresponding failure mode?" Request tests for the failure.

Testability Analysis

When reviewing application code, assess whether it is structured for testability. Each subsection has a hard-to-test vs. testable before/after in references/testability-refactors.md.

Dependency Injection

Flag classes that instantiate dependencies directly (new PostgresDatabase(), new StripeClient() inside methods). Suggest constructor injection so tests can substitute mocks/fakes. See references/testability-refactors.md.

Side Effect Isolation

Flag functions that mix pure calculation with I/O (email, logging, analytics). Extract the calculation as a pure function, then call it from the side-effectful orchestrator. See the calcTotal before/after in references/testability-refactors.md.

Pure Function Extraction

Look for validation, transformation, and business rules buried inside request handlers. If logic is inline in app.post('/api/orders', ...), it cannot be unit-tested without spinning up an HTTP server. Extract it as a standalone function. See the shippingFor before/after in references/testability-refactors.md.

Interface Segregation

Flag classes that depend on broad interfaces (entire PrismaClient) when they only use 2-3 methods. Suggest a narrow interface with only the methods actually used, making test doubles trivial to implement. See references/testability-refactors.md.

Review Workflow

PR Review Checklist

For each test file in a PR, check systematically:

## Test Quality Review

### Readability
- [ ] Can I understand what each test verifies in under 10 seconds?
- [ ] Is setup minimal and test-relevant?
- [ ] Are test names descriptive: "should [behavior] when [condition]"?

### Reliability
- [ ] No sleep/waitForTimeout/setTimeout for synchronization?
- [ ] No shared mutable state between tests?
- [ ] No dependency on test execution order?
- [ ] No calls to real external services?

### Diagnostic Value
- [ ] Will failures produce messages that identify the problem?
- [ ] Does each test have one reason to fail?
- [ ] Are assertions specific (not toBeTruthy/toBeDefined)?

### Design
- [ ] No conditional logic (if/else/switch/for) in test bodies?
- [ ] Fixtures/setup proportional to what each test needs?
- [ ] Mocking limited to external boundaries?
- [ ] Parameterized tests (it.each) used for data-driven scenarios?

### AI-Generated (if applicable)
- [ ] Locators verified against a real page (no hallucinated data-testid)?
- [ ] Imports resolve (no fabricated APIs)?
- [ ] Project data factory used, not [email protected] / John Doe?

### Coverage
- [ ] Happy path AND error/negative paths tested?
- [ ] Boundary values tested (0, 1, max, max+1)?
- [ ] Edge cases: empty, null, duplicate, concurrent?

Batch Audit Process

For a full test suite audit:

Quantify: Count tests by type (unit/integration/E2E), framework, and directory.
Sample: Review 10-20% of test files, prioritizing the largest and most recently changed.
Pattern: Identify the 3-5 most common smells across the sample.
Prioritize: Rank by impact: reliability smells > diagnostic smells > design smells > readability smells.
Automate: For each common smell, determine if an ESLint rule or a mutation-score gate can catch it automatically.
Report: Document findings (one row per file reviewed) with specific examples, suggested fixes, severity (high/medium/low), and estimated effort.

Prompt Templates

Three prompt patterns for AI-assisted review:

Review test quality: "Check this test file for readability, reliability, diagnostic, design, AI-generated, and coverage smells. For each issue: name the smell, cite the line, explain why it matters, and provide a fix."
Identify coverage gaps: "Given this application code and its existing tests, identify missing scenarios across happy path, error handling, boundaries, edge cases, concurrency, and security. Prioritize as P0/P1/P2."
Testability improvements: "Review this application code for hard-coded dependencies, mixed side effects, extractable pure functions, and overly broad interfaces. Show current vs. refactored code."

Anti-Patterns

Reviewing test code with production-code standards only. Test code has additional quality dimensions (reliability, diagnostics, coverage) that production-code linters do not check. Apply the six smell buckets, not just "clean code" principles.
Flagging every smell without context. A 50-line test for a complex state machine is not obscure setup — it is necessary complexity. Evaluate smells against the behavior being tested.
Suggesting mocks for everything. Over-mocking is itself a smell. Do not recommend mocking pure functions, value objects, or fast in-process collaborators. Mock boundaries: network, database, filesystem, clock.
Focusing on coverage percentage over coverage quality. 95% line coverage with only happy-path tests is worse than 75% coverage that includes error paths and boundaries. Review what is asserted, not just what is executed — and back it with mutation score.
Reviewing without running. Static analysis misses runtime issues. Run the suite (see Verification), check for flakiness with repeated runs, and check execution time. A passing suite that takes 20 minutes has a performance smell.
One-time review without follow-up. Test quality degrades over time. Establish a recurring review cadence or automated quality gates (lint rule, mutation threshold) that catch regression.

Verification

Run before you start commenting — these are the runtime checks the skill insists on, made concrete. Scope them to the PR's changed test files in PR review; run the whole suite in a batch audit.

Run the suite once. Confirm it is green before reviewing — never review red or skipped tests as if they pass.
- Vitest: npx vitest run <files> · Jest: npx jest <files> · Playwright: npx playwright test <files> · pytest: pytest <files>
Repeat to surface flakiness. A test that passes once and fails on the next run is the reliability smell, not a fluke.
- Vitest: npx vitest run --retry=0 <files> looped, or for i in 1 2 3; do npx vitest run <files> || break; done
- Jest: npx jest <files> && npx jest <files> && npx jest <files> (or jest-circus repeat)
- Playwright: npx playwright test --repeat-each=3 <files>
- pytest: pytest --count=3 <files> (pytest-repeat) or pytest -p no:randomly vs random order to catch order dependency
Capture per-test timing. A 20-minute suite has a performance smell.
- Vitest: --reporter=verbose (prints per-test duration) · Jest: --verbose · Playwright: --reporter=list · pytest: --durations=10
Mutation score — the objective backstop. For the qualitative smells you can't eyeball at scale (Closed AI loop, weak assertions), run a mutation runner and read the score. AI-generated tests under ~60% mutation score are not constraining the implementation. Full tooling, thresholds, and config in references/mutation-testing.md.
- JS/TS: npx stryker run · Java: PIT mvn ... mutationCoverage · Rust: cargo mutants · Python: mutmut run

Output of steps 1-3 plus the mutation score is the evidence behind every "reliability"/"diagnostic" finding in the report.

Done When

The findings artifact exists with one row per reviewed file, each row carrying a severity rating (high/medium/low). (file exists, row count == files reviewed)
Every smell dimension that applies to the reviewed code is covered: readability, reliability, diagnostic, design, AI-generated, coverage. (six headings present in the report; "N/A" allowed, blank not)
Each high-severity finding has an actionable remediation step (what + why + fix), not just a description.
Verification ran: the suite executed at least 3x green (no flaky failures) and per-test timing captured; for AI-generated suites, a mutation score is recorded.
At least one recurring smell was converted to an automated gate — an ESLint rule, a CI check, or a mutation-score threshold committed — or the report explicitly states none was warranted.

Reference Files (in `references/`)

smell-examples.md — Full SMELL/FIX code for every catalogued smell across reliability, diagnostic, design, and coverage dimensions.
testability-refactors.md — Hard-to-test vs. testable before/after for all four testability subsections: dependency injection, side effect isolation, pure function extraction, interface segregation.
mutation-testing.md — Tooling (Stryker/PIT/cargo-mutants/mutmut), thresholds, Stryker config, and the AI-loop / weak-assertion gate.

Related Skills

unit-testing — Framework-specific patterns (Jest, Vitest, pytest) that inform what "good" looks like for each framework.
shift-left-testing — Pre-commit hooks and IDE integration that catch test smells before review.
coverage-analysis — Interpreting coverage reports to find meaningful gaps, not just percentage targets.
ai-test-generation — Generated tests need review too. Apply these smell checks to AI-generated test code.
test-reliability — Reliability smells (sleep-based waits, order dependency) overlap with flaky test patterns.

When it activates

Automatically, when a task matches. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features i

About this skill

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QA & Test Engineering pack

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/26/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

petrkindlmann / qa-skills

skills/ai-qa-review/SKILL.md

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License MITImported 10/26/2026Version v1Bundled 3 files

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

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What it changes

Track record

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Quick Route

Discovery Questions

Core Principles

Test Smell Buckets

Readability Smells

Obscure Setup

Mystery Guest

Duplicate Assertions

Reliability Smells

Sleep-Based Waiting

Order Dependency

External Service Coupling

Diagnostic Smells

Weak Assertion Messages

Multiple Failure Causes Per Test

Design Smells

Conditional Test Logic

Giant Fixtures

Over-Mocking

AI-Generated Test Smells

Coverage Smells

Happy Path Only

Missing Boundary Cases

Missing Error/Negative Cases

Testability Analysis

Dependency Injection

Side Effect Isolation

Pure Function Extraction

Interface Segregation

Review Workflow

PR Review Checklist

Batch Audit Process

Prompt Templates

Anti-Patterns

Verification

Done When

Reference Files (in references/)

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Reference Files (in `references/`)