Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

QA Test Planner · davila7 | DecimalAI

Testingby davila7

QA Test Planner

Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.

Never made a case worse

SkillScore

SkillScore88/100

Test results 88

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

67%88%of test cases passed +21 pts

Without skill16 of 24

With skill21 of 24

5 fixed16 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/11/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe want to run the interactive command-line helper script provided in the qa-test-planner workspace repository to guide a tester step-by-step through filing a bug report. What script file path should be executed?

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 davila7-qa-test-planner
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("davila7-qa-test-planner")
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 davila7-qa-test-planner

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/davila7-qa-test-planner/SKILL.md https://app.decimal.ai/s/davila7-qa-test-planner/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/davila7-qa-test-planner/SKILL.md

This gets SKILL.md only. The skill's 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/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/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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("davila7-qa-test-planner")
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/davila7-qa-test-planner/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/davila7-qa-test-planner/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 6 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 davila7-qa-test-planner

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/davila7-qa-test-planner/SKILL.md https://app.decimal.ai/s/davila7-qa-test-planner/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/davila7-qa-test-planner/SKILL.md

This gets SKILL.md only. The skill's 6 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/davila7-qa-test-planner/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/davila7-qa-test-planner.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/11/2026
Source: davila7/claude-code-templates ↗
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

QA Test Planner

A comprehensive skill for QA engineers to create test plans, generate manual test cases, build regression test suites, validate designs against Figma, and document bugs effectively.

Activation: This skill is triggered only when explicitly called by name (e.g., /qa-test-planner, qa-test-planner, or use the skill qa-test-planner).

Quick Start

Create a test plan:

"Create a test plan for the user authentication feature"

Generate test cases:

"Generate manual test cases for the checkout flow"

Build regression suite:

"Build a regression test suite for the payment module"

Validate against Figma:

"Compare the login page against the Figma design at [URL]"

Create bug report:

"Create a bug report for the form validation issue"

Quick Reference

Task	What You Get	Time
Test Plan	Strategy, scope, schedule, risks	10-15 min
Test Cases	Step-by-step instructions, expected results	5-10 min each
Regression Suite	Smoke tests, critical paths, execution order	15-20 min
Figma Validation	Design-implementation comparison, discrepancy list	10-15 min
Bug Report	Reproducible steps, environment, evidence	5 min

How It Works

Your Request
    │
    ▼
┌─────────────────────────────────────────────────────┐
│ 1. ANALYZE                                          │
│    • Parse feature/requirement                      │
│    • Identify test types needed                     │
│    • Determine scope and priorities                 │
├─────────────────────────────────────────────────────┤
│ 2. GENERATE                                         │
│    • Create structured deliverables                 │
│    • Apply templates and best practices             │
│    • Include edge cases and variations              │
├─────────────────────────────────────────────────────┤
│ 3. VALIDATE                                         │
│    • Check completeness                             │
│    • Verify traceability                            │
│    • Ensure actionable steps                        │
└─────────────────────────────────────────────────────┘
    │
    ▼
QA Deliverable Ready

Commands

Interactive Scripts

Script	Purpose	Usage
`./scripts/generate_test_cases.sh`	Create test cases interactively	Step-by-step prompts
`./scripts/create_bug_report.sh`	Generate bug reports	Guided input collection

Natural Language

Request	Output
"Create test plan for {feature}"	Complete test plan document
"Generate {N} test cases for {feature}"	Numbered test cases with steps
"Build smoke test suite"	Critical path tests
"Compare with Figma at {URL}"	Visual validation checklist
"Document bug: {description}"	Structured bug report

Core Deliverables

1. Test Plans

Test scope and objectives
Testing approach and strategy
Environment requirements
Entry/exit criteria
Risk assessment
Timeline and milestones

2. Manual Test Cases

Step-by-step instructions
Expected vs actual results
Preconditions and setup
Test data requirements
Priority and severity

3. Regression Suites

Smoke tests (15-30 min)
Full regression (2-4 hours)
Targeted regression (30-60 min)
Execution order and dependencies

4. Figma Validation

Component-by-component comparison
Spacing and typography checks
Color and visual consistency
Interactive state validation

5. Bug Reports

Clear reproduction steps
Environment details
Evidence (screenshots, logs)
Severity and priority

Anti-Patterns

Avoid	Why	Instead
Vague test steps	Can't reproduce	Specific actions + expected results
Missing preconditions	Tests fail unexpectedly	Document all setup requirements
No test data	Tester blocked	Provide sample data or generation
Generic bug titles	Hard to track	Specific: "[Feature] issue when [action]"
Skip edge cases	Miss critical bugs	Include boundary values, nulls

Verification Checklist

Test Plan:

Scope clearly defined (in/out)
Entry/exit criteria specified
Risks identified with mitigations
Timeline realistic

Test Cases:

Each step has expected result
Preconditions documented
Test data available
Priority assigned

Bug Reports:

Reproducible steps
Environment documented
Screenshots/evidence attached
Severity/priority set

References

Test Case Templates - Standard formats for all test types
Bug Report Templates - Documentation templates
Regression Testing Guide - Suite building and execution
Figma Validation Guide - Design-implementation validation

Standard Test Case Format

## TC-001: [Test Case Title]

**Priority:** High | Medium | Low
**Type:** Functional | UI | Integration | Regression
**Status:** Not Run | Pass | Fail | Blocked

### Objective
[What are we testing and why]

### Preconditions
- [Setup requirement 1]
- [Setup requirement 2]
- [Test data needed]

### Test Steps
1. [Action to perform]
   **Expected:** [What should happen]

2. [Action to perform]
   **Expected:** [What should happen]

3. [Action to perform]
   **Expected:** [What should happen]

### Test Data
- Input: [Test data values]
- User: [Test account details]
- Configuration: [Environment settings]

### Post-conditions
- [System state after test]
- [Cleanup required]

### Notes
- [Edge cases to consider]
- [Related test cases]
- [Known issues]

Test Types

Type	Focus	Example
Functional	Business logic	Login with valid credentials
UI/Visual	Appearance, layout	Button matches Figma design
Integration	Component interaction	API returns data to frontend
Regression	Existing functionality	Previous features still work
Performance	Speed, load handling	Page loads under 3 seconds
Security	Vulnerabilities	SQL injection prevented

Test Plan Structure

# Test Plan: [Feature/Release Name]

## Executive Summary
- Feature/product being tested
- Testing objectives
- Key risks
- Timeline overview

## Test Scope

**In Scope:**
- Features to be tested
- Test types (functional, UI, performance)
- Platforms and environments
- User flows and scenarios

**Out of Scope:**
- Features not being tested
- Known limitations
- Third-party integrations (if applicable)

## Test Strategy

**Test Types:**
- Manual testing
- Exploratory testing
- Regression testing
- Integration testing
- User acceptance testing

**Test Approach:**
- Black box testing
- Positive and negative testing
- Boundary value analysis
- Equivalence partitioning

## Test Environment
- Operating systems
- Browsers and versions
- Devices (mobile, tablet, desktop)
- Test data requirements
- Backend/API environments

## Entry Criteria
- [ ] Requirements documented
- [ ] Designs finalized
- [ ] Test environment ready
- [ ] Test data prepared
- [ ] Build deployed

## Exit Criteria
- [ ] All high-priority test cases executed
- [ ] 90%+ test case pass rate
- [ ] All critical bugs fixed
- [ ] No open high-severity bugs
- [ ] Regression suite passed

## Risk Assessment

| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| [Risk 1] | H/M/L | H/M/L | [Mitigation] |

## Test Deliverables
- Test plan document
- Test cases
- Test execution reports
- Bug reports
- Test summary report

Bug Report Template

# BUG-[ID]: [Clear, specific title]

**Severity:** Critical | High | Medium | Low
**Priority:** P0 | P1 | P2 | P3
**Type:** Functional | UI | Performance | Security
**Status:** Open | In Progress | Fixed | Closed

## Environment
- **OS:** [Windows 11, macOS 14, etc.]
- **Browser:** [Chrome 120, Firefox 121, etc.]
- **Device:** [Desktop, iPhone 15, etc.]
- **Build:** [Version/commit]
- **URL:** [Page where bug occurs]

## Description
[Clear, concise description of the issue]

## Steps to Reproduce
1. [Specific step]
2. [Specific step]
3. [Specific step]

## Expected Behavior
[What should happen]

## Actual Behavior
[What actually happens]

## Visual Evidence
- Screenshot: [attached]
- Video: [link if applicable]
- Console errors: [paste errors]

## Impact
- **User Impact:** [How many users affected]
- **Frequency:** [Always, Sometimes, Rarely]
- **Workaround:** [If one exists]

## Additional Context
- Related to: [Feature/ticket]
- Regression: [Yes/No]
- Figma design: [Link if UI bug]

Severity Definitions

Level	Criteria	Examples
Critical (P0)	System crash, data loss, security	Payment fails, login broken
High (P1)	Major feature broken, no workaround	Search not working
Medium (P2)	Feature partial, workaround exists	Filter missing one option
Low (P3)	Cosmetic, rare edge cases	Typo, minor alignment

Design Validation Workflow

Prerequisites:

Figma MCP server configured
Access to Figma design files
Figma URLs for components/pages

Process:

Get Design Specs from Figma

"Get the button specifications from Figma file [URL]"

Response includes:
- Dimensions (width, height)
- Colors (background, text, border)
- Typography (font, size, weight)
- Spacing (padding, margin)
- Border radius
- States (default, hover, active, disabled)

Compare Implementation

TC: Primary Button Visual Validation
1. Inspect primary button in browser dev tools
2. Compare against Figma specs:
   - Dimensions: 120x40px
   - Border-radius: 8px
   - Background color: #0066FF
   - Font: 16px Medium #FFFFFF
3. Document discrepancies

Create Bug if Mismatch

BUG: Primary button color doesn't match design
Severity: Medium
Expected (Figma): #0066FF
Actual (Implementation): #0052CC
Screenshot: [attached]
Figma link: [specific component]

What to Validate

Element	What to Check	Tool
Colors	Hex values exact	Browser color picker
Spacing	Padding/margin px	DevTools computed styles
Typography	Font, size, weight	DevTools font panel
Layout	Width, height, position	DevTools box model
States	Hover, active, focus	Manual interaction
Responsive	Breakpoint behavior	DevTools device mode

Example Queries

"Get button specifications from Figma design [URL]"
"Compare navigation menu implementation against Figma design"
"Extract spacing values for dashboard layout from Figma"
"List all color tokens used in Figma design system"

Suite Structure

Suite Type	Duration	Frequency	Coverage
Smoke	15-30 min	Daily	Critical paths only
Targeted	30-60 min	Per change	Affected areas
Full	2-4 hours	Weekly/Release	Comprehensive
Sanity	10-15 min	After hotfix	Quick validation

Building a Regression Suite

Step 1: Identify Critical Paths

What can users NOT live without?
What generates revenue?
What handles sensitive data?
What's used most frequently?

Step 2: Prioritize Test Cases

Priority	Description	Must Run
P0	Business-critical, security	Always
P1	Major features, common flows	Weekly+
P2	Minor features, edge cases	Releases

Step 3: Execution Order

Smoke first - if fails, stop and fix build
P0 tests next - must pass before proceeding
P1 then P2 - track all failures
Exploratory - find unexpected issues

Pass/Fail Criteria

PASS:

All P0 tests pass
90%+ P1 tests pass
No critical bugs open

FAIL (Block Release):

Any P0 test fails
Critical bug discovered
Security vulnerability
Data loss scenario

CONDITIONAL:

P1 failures with workarounds
Known issues documented
Fix plan in place

Test Run Report Template

# Test Run: [Release Version]

**Date:** 2024-01-15
**Build:** v2.5.0-rc1
**Tester:** [Name]
**Environment:** Staging

## Summary
- Total Test Cases: 150
- Executed: 145
- Passed: 130
- Failed: 10
- Blocked: 5
- Not Run: 5
- Pass Rate: 90%

## Test Cases by Priority

| Priority | Total | Pass | Fail | Blocked |
|----------|-------|------|------|---------|
| P0 (Critical) | 25 | 23 | 2 | 0 |
| P1 (High) | 50 | 45 | 3 | 2 |
| P2 (Medium) | 50 | 45 | 3 | 2 |
| P3 (Low) | 25 | 17 | 2 | 1 |

## Critical Failures
- TC-045: Payment processing fails
  - Bug: BUG-234
  - Status: Open

## Blocked Tests
- TC-112: Dashboard widget (API endpoint down)

## Risks
- 2 critical bugs blocking release
- Payment integration needs attention

## Next Steps
- Retest after BUG-234 fix
- Complete remaining 5 test cases
- Run full regression before sign-off

Coverage Tracking

## Coverage Matrix

| Feature | Requirements | Test Cases | Status | Gaps |
|---------|--------------|------------|--------|------|
| Login | 8 | 12 | Complete | None |
| Checkout | 15 | 10 | Partial | Payment errors |
| Dashboard | 12 | 15 | Complete | None |

Phase 1: Planning

Phase 2: Test Design

Phase 3: Execution

Phase 4: Reporting

Test Case Writing

DO:

Be specific and unambiguous
Include expected results for each step
Test one thing per test case
Use consistent naming conventions
Keep test cases maintainable

DON'T:

Assume knowledge
Make test cases too long
Skip preconditions
Forget edge cases
Leave expected results vague

Bug Reporting

DO:

Provide clear reproduction steps
Include screenshots/videos
Specify exact environment details
Describe impact on users
Link to Figma for UI bugs

DON'T:

Report without reproduction steps
Use vague descriptions
Skip environment details
Forget to assign priority
Duplicate existing bugs

Regression Testing

DO:

Automate repetitive tests when possible
Maintain regression suite regularly
Prioritize critical paths
Run smoke tests frequently
Update suite after each release

DON'T:

Skip regression before releases
Let suite become outdated
Test everything every time
Ignore failed regression tests

Examples

## TC-LOGIN-001: Valid User Login

**Priority:** P0 (Critical)
**Type:** Functional
**Estimated Time:** 2 minutes

### Objective
Verify users can successfully login with valid credentials

### Preconditions
- User account exists ([email protected] / Test123!)
- User is not already logged in
- Browser cookies cleared

### Test Steps
1. Navigate to https://app.example.com/login
   **Expected:** Login page displays with email and password fields

2. Enter email: [email protected]
   **Expected:** Email field accepts input

3. Enter password: Test123!
   **Expected:** Password field shows masked characters

4. Click "Login" button
   **Expected:**
   - Loading indicator appears
   - User redirected to /dashboard
   - Welcome message shown: "Welcome back, Test User"
   - Avatar/profile image displayed in header

### Post-conditions
- User session created
- Auth token stored
- Analytics event logged

### Edge Cases to Consider
- TC-LOGIN-002: Invalid password
- TC-LOGIN-003: Non-existent email
- TC-LOGIN-004: SQL injection attempt
- TC-LOGIN-005: Very long password

## TC-UI-045: Mobile Navigation Menu

**Priority:** P1 (High)
**Type:** UI/Responsive
**Devices:** Mobile (iPhone, Android)

### Objective
Verify navigation menu works correctly on mobile devices

### Preconditions
- Access from mobile device or responsive mode
- Viewport width: 375px (iPhone SE) to 428px (iPhone Pro Max)

### Test Steps
1. Open homepage on mobile device
   **Expected:** Hamburger menu icon visible (top-right)

2. Tap hamburger icon
   **Expected:**
   - Menu slides in from right
   - Overlay appears over content
   - Close (X) button visible

3. Tap menu item
   **Expected:** Navigate to section, menu closes

4. Compare against Figma mobile design [link]
   **Expected:**
   - Menu width: 280px
   - Slide animation: 300ms ease-out
   - Overlay opacity: 0.5, color #000000
   - Font size: 16px, line-height 24px

### Breakpoints to Test
- 375px (iPhone SE)
- 390px (iPhone 14)
- 428px (iPhone 14 Pro Max)
- 360px (Galaxy S21)

"Testing shows the presence, not the absence of bugs." - Edsger Dijkstra

"Quality is not an act, it is a habit." - Aristotle

About this skill

Automatic

QA & Test Engineering pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

davila7 / claude-code-templates

cli-tool/components/skills/ai-research/qa-test-planner/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 6 files · scripts

Illustrative Want to see what davila7/qa-test-planner would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

QA Test Planner

Quick Start

Quick Reference

How It Works

Commands

Interactive Scripts

Natural Language

Core Deliverables

1. Test Plans

2. Manual Test Cases

3. Regression Suites

4. Figma Validation

5. Bug Reports

Anti-Patterns

Verification Checklist

References

Standard Test Case Format

Test Types

Test Plan Structure

Bug Report Template

Severity Definitions

Design Validation Workflow

What to Validate

Example Queries

Suite Structure

Building a Regression Suite

Pass/Fail Criteria

Test Run Report Template

Coverage Tracking

Phase 1: Planning

Phase 2: Test Design

Phase 3: Execution

Phase 4: Reporting

Test Case Writing

Bug Reporting

Regression Testing

Examples

SkillSafety

Source

Related skills

More in the registry