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Senior Quality Manager Responsible Person (QMR) · alirezarezvani | DecimalAI

Legalby alirezarezvani

Senior Quality Manager Responsible Person (QMR)

Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulatory compliance oversight, and quality performance monitoring per ISO 13485 Clause 5.5.2.

Never made a case worse

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%73%of test cases passed +41 pts

Without skill7 of 22

With skill16 of 22

9 fixed7 passed either way6 still failing0 broke

Measured on gemini-3.6-flash8/10/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskOur QMR needs to hire an external consultant to assist with a 45-day technical review costing $40,000. Is this expenditure within the QMR's direct resource allocation authority, or does it trigger an escalation threshold requiring higher approval?

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 alirezarezvani-quality-manager-qmr
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("alirezarezvani-quality-manager-qmr")
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 alirezarezvani-quality-manager-qmr

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/alirezarezvani-quality-manager-qmr/SKILL.md https://app.decimal.ai/s/alirezarezvani-quality-manager-qmr/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/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/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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("alirezarezvani-quality-manager-qmr")
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/alirezarezvani-quality-manager-qmr/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/alirezarezvani-quality-manager-qmr/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 alirezarezvani-quality-manager-qmr

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/alirezarezvani-quality-manager-qmr/SKILL.md https://app.decimal.ai/s/alirezarezvani-quality-manager-qmr/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-quality-manager-qmr/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/alirezarezvani-quality-manager-qmr/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/alirezarezvani-quality-manager-qmr.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/10/2026
Source: alirezarezvani/claude-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

Senior Quality Manager Responsible Person (QMR)

Quality system accountability, management review leadership, and regulatory compliance oversight per ISO 13485 Clause 5.5.2 requirements.

QMR Responsibilities
Management Review Workflow
Quality KPI Management Workflow
Quality Objectives Workflow
Quality Culture Assessment Workflow
Regulatory Compliance Oversight
Decision Frameworks
Tools and References

QMR Responsibilities

ISO 13485 Clause 5.5.2 Requirements

Responsibility	Scope	Evidence
QMS effectiveness	Monitor system performance and suitability	Management review records
Reporting to management	Communicate QMS performance to top management	Quality reports, dashboards
Quality awareness	Promote regulatory and quality requirements	Training records, communications
Liaison with external parties	Interface with regulators, Notified Bodies	Meeting records, correspondence

QMR Accountability Matrix

Domain	Accountable For	Reports To	Frequency
Quality Policy	Policy adequacy and communication	CEO/Board	Annual review
Quality Objectives	Objective achievement and relevance	Executive Team	Quarterly
QMS Performance	System effectiveness metrics	Management	Monthly
Regulatory Compliance	Compliance status across jurisdictions	CEO	Quarterly
Audit Program	Audit schedule completion, findings closure	Management	Per audit
CAPA Oversight	CAPA effectiveness and timeliness	Executive Team	Monthly

Authority Boundaries

Decision Type	QMR Authority	Escalation Required
Process changes within QMS	Approve with owner	Major process redesign
Document approval	Final QA approval	Policy-level changes
Nonconformity disposition	Accept/reject with MRB	Product release decisions
Supplier quality actions	Quality holds, audits	Supplier termination
Audit scheduling	Adjust internal audit schedule	External audit timing
Training requirements	Define quality training needs	Organization-wide training budget

Management Review Workflow

Conduct management reviews per ISO 13485 Clause 5.6 requirements.

Workflow: Prepare and Execute Management Review

Schedule management review (minimum annually, typically quarterly or semi-annually)
Notify all required attendees minimum 2 weeks prior
Collect required inputs from process owners:
- Audit results (internal and external)
- Customer feedback (complaints, satisfaction, returns)
- Process performance and product conformity
- CAPA status and effectiveness
- Previous review action items
- Changes affecting QMS (regulatory, organizational)
- Recommendations for improvement
Compile input summary report with trend analysis
Prepare presentation materials with supporting data
Distribute agenda and input package 1 week prior
Conduct review meeting per agenda
Validation: All required inputs reviewed; decisions documented with owners and due dates

Required Attendees

Role	Requirement	Input Responsibility
CEO/General Manager	Required	Strategic decisions
QMR	Chair	Overall QMS status
Department Heads	Required	Process performance
RA Manager	Required	Regulatory changes
Production Manager	Required	Product conformity
Customer Quality	Required	Complaint data

Management Review Input Template

MANAGEMENT REVIEW INPUT SUMMARY

Review Period: [Start Date] to [End Date]
Review Date: [Scheduled Date]
Prepared By: [QMR Name]

1. AUDIT RESULTS
   Internal audits completed: [X] of [X] planned
   External audits completed: [X]
   Total findings: [X] major / [X] minor
   Open findings: [X]
   Finding trends: [Analysis]

2. CUSTOMER FEEDBACK
   Complaints received: [X]
   Complaint rate: [X per 1000 units]
   Customer satisfaction score: [X.X/5.0]
   Returns: [X] units ([X]%)
   Top issues: [Categories]

3. PROCESS PERFORMANCE
   [Process 1]: [Metric] vs [Target] - [Status]
   [Process 2]: [Metric] vs [Target] - [Status]
   Out-of-spec processes: [List]

4. PRODUCT CONFORMITY
   First pass yield: [X]%
   Nonconformance rate: [X]%
   Scrap cost: $[X]
   Top defect categories: [List]

5. CAPA STATUS
   Open CAPAs: [X]
   Overdue: [X]
   Effectiveness rate: [X]%
   Average age: [X] days

6. PREVIOUS ACTIONS
   Total from last review: [X]
   Completed: [X] | In progress: [X] | Overdue: [X]

7. CHANGES AFFECTING QMS
   Regulatory: [List changes]
   Organizational: [List changes]
   Process: [List changes]

8. RECOMMENDATIONS
   [Collected improvement opportunities]

Management Review Output Requirements

Output	Documentation	Owner
QMS improvement decisions	Action items with due dates	Assigned per item
Resource needs	Resource plan updates	Department heads
Quality objectives changes	Updated objectives document	QMR
Process improvement needs	Improvement project charters	Process owners

See: references/management-review-guide.md

Quality KPI Management Workflow

Establish, monitor, and report quality performance indicators.

Workflow: Establish Quality KPI Framework

Identify quality objectives requiring measurement
Select KPIs per objective using SMART criteria:
- Specific: Clear definition and calculation
- Measurable: Quantifiable with available data
- Actionable: Team can influence results
- Relevant: Aligned to quality objectives
- Time-bound: Defined measurement frequency
Define target values based on baseline data and benchmarks
Assign data source and collection responsibility
Establish reporting frequency per KPI category
Configure dashboard displays and trend analysis
Define escalation thresholds and alert triggers
Validation: Each KPI has owner, target, data source, and escalation criteria

Core Quality KPIs

Category	KPI	Target	Calculation
Process	First Pass Yield	>95%	(Units passed first time / Total units) × 100
Process	Nonconformance Rate	<1%	(NC count / Total units) × 100
CAPA	CAPA Closure Rate	>90%	(On-time closures / Due closures) × 100
CAPA	CAPA Effectiveness	>85%	(Effective CAPAs / Verified CAPAs) × 100
Audit	Finding Closure Rate	>90%	(On-time closures / Due closures) × 100
Audit	Repeat Finding Rate	<10%	(Repeat findings / Total findings) × 100
Customer	Complaint Rate	<0.1%	(Complaints / Units sold) × 100
Customer	Satisfaction Score	>4.0/5.0	Average of survey scores

KPI Review Frequency

KPI Type	Review Frequency	Trend Period	Audience
Safety/Compliance	Daily monitoring	Weekly	Operations
Production Quality	Weekly	Monthly	Department heads
Customer Quality	Monthly	Quarterly	Executive team
Strategic Quality	Quarterly	Annual	Board/C-suite

Performance Response Matrix

Performance Level	Status	Action Required
>110% of target	Exceeding	Consider raising target
100-110% of target	Meeting	Maintain current approach
90-100% of target	Approaching	Monitor closely
80-90% of target	Below	Improvement plan required
<80% of target	Critical	Immediate intervention

See: references/quality-kpi-framework.md

Quality Objectives Workflow

Establish and maintain measurable quality objectives per ISO 13485 Clause 5.4.1.

Workflow: Annual Quality Objectives Setting

Review prior year objective achievement
Analyze quality performance trends and gaps
Align with organizational strategic plan
Draft objectives with measurable targets
Validate resource availability for achievement
Obtain executive approval
Communicate objectives organization-wide
Validation: Each objective is measurable, has owner, target, and timeline

Quality Objective Structure

QUALITY OBJECTIVE [Number]

Objective Statement: [Clear, measurable statement]
Aligned to Policy Element: [Quality policy section]
Target: [Specific measurable target]
Baseline: [Current performance]
Owner: [Name and title]
Due Date: [Target achievement date]

Success Criteria:
- [Criterion 1]
- [Criterion 2]

Measurement Method: [How progress is tracked]
Reporting Frequency: [Monthly/Quarterly]

Supporting Initiatives:
- [Initiative 1]
- [Initiative 2]

Resource Requirements:
- [Resource 1]
- [Resource 2]

Objective Categories

Category	Example Objectives	Typical Targets
Customer Quality	Reduce complaint rate	<0.1% of units sold
Process Quality	Improve first pass yield	>96%
Compliance	Maintain certification	Zero major NCs
Efficiency	Reduce quality costs	<4% of revenue
Culture	Increase training completion	>98% on-time

Quarterly Objective Review

Review Element	Assessment	Action
Progress vs. target	On track / Behind / Ahead	Adjust resources if behind
Relevance	Still valid / Needs update	Modify if conditions changed
Resources	Adequate / Insufficient	Request additional if needed
Barriers	Identified obstacles	Escalate for resolution

Quality Culture Assessment Workflow

Assess and improve organizational quality culture.

Workflow: Annual Quality Culture Assessment

Design or select quality culture survey instrument
Define survey population (all employees or sample)
Communicate survey purpose and confidentiality
Administer survey with 2-week response window
Analyze results by department, role, and tenure
Identify strengths and improvement areas
Develop action plan for culture gaps
Validation: Response rate >60%; action plan addresses bottom 3 scores

Quality Culture Dimensions

Dimension	Indicators	Assessment Method
Leadership commitment	Management visible support for quality	Survey, observation
Quality ownership	Employees feel responsible for quality	Survey
Communication	Quality information flows effectively	Survey, audit
Continuous improvement	Suggestions submitted and implemented	Metrics
Training and competence	Employees feel adequately trained	Survey, records
Problem solving	Issues addressed at root cause	CAPA analysis

Culture Survey Categories

Category	Sample Questions
Leadership	"Management demonstrates commitment to quality"
Resources	"I have the tools and training to do quality work"
Communication	"Quality expectations are clearly communicated"
Empowerment	"I am encouraged to report quality issues"
Recognition	"Quality achievements are recognized"

Culture Improvement Actions

Gap Identified	Potential Actions
Low leadership visibility	Quality gemba walks, all-hands quality updates
Inadequate training	Competency-based training program
Poor communication	Quality newsletters, department huddles
Low reporting	Anonymous reporting system, no-blame culture
Lack of recognition	Quality award program, team celebrations

Regulatory Compliance Oversight

Monitor and maintain regulatory compliance across jurisdictions.

Multi-Jurisdictional Compliance Matrix

Jurisdiction	Regulation	Requirement	Status Tracking
EU	MDR 2017/745	CE marking, Notified Body	Technical file, annual review
USA	21 CFR 820 (QMSR)	FDA registration, QMSR compliance — 21 CFR 820 incorporates ISO 13485:2016 by reference (effective 2026-02-02; formerly the QSR)	Annual registration, inspections
International	ISO 13485	QMS certification	Surveillance audits
Germany	MPG/MPDG	National implementation	Competent authority filings

Compliance Monitoring Workflow

Maintain regulatory requirement register
Subscribe to regulatory update services
Assess impact of regulatory changes monthly
Update affected processes within 90 days of effective date
Verify training completion for regulatory changes
Document compliance status in management review
Maintain inspection readiness checklist
Validation: All applicable requirements mapped; no expired registrations

Decision discipline: This compliance matrix is decision support, not a compliance determination. Final regulatory-status calls are yours to make as QMR and must be signed off by the named owner; route FDA-specific questions to Regulatory Affairs and verify current 21 CFR 820 (QMSR) / ISO 13485:2016 text at fda.gov before relying on any citation here.

Regulatory Authority Interface

Activity	QMR Role	Preparation Required
Notified Body audit	Primary contact	Audit package, personnel schedules
FDA inspection	Host, escort coordinator	Inspection readiness review
Competent Authority inquiry	Response coordinator	Technical file access
Regulatory meeting	Attendee or delegate	Briefing materials

Inspection Readiness Checklist

Area	Ready	Action Needed
Document control system current	☐
Training records complete	☐
CAPA system current, no overdue items	☐
Complaint files complete	☐
Equipment calibration current	☐
Supplier qualification files complete	☐
Management review records available	☐
Internal audit program current	☐

Decision Frameworks

Escalation Decision Tree

Issue Identified
      │
      ▼
Is it a regulatory violation?
      │
  Yes─┴─No
  │      │
  ▼      ▼
Escalate to    Is it a safety issue?
Executive          │
immediately    Yes─┴─No
               │      │
               ▼      ▼
          Escalate to   Does it affect
          Safety Team   multiple departments?
                             │
                         Yes─┴─No
                         │      │
                         ▼      ▼
                    Escalate to  Handle at
                    Executive    department level

Quality Investment Prioritization

Criteria	Weight	Score Method
Regulatory requirement	30%	Required=10, Recommended=5, Optional=2
Customer impact	25%	Direct=10, Indirect=5, None=0
Cost savings potential	20%	>$100K=10, $50-100K=7, <$50K=3
Implementation complexity	15%	Simple=10, Moderate=5, Complex=2
Strategic alignment	10%	Core=10, Supporting=5, Peripheral=2

Resource Allocation Matrix

Resource Type	Allocation Authority	Escalation Threshold
Quality personnel	QMR	>1 FTE addition
Quality equipment	QMR	>$25K
External consultants	QMR	>$50K or >30 days
Quality systems	Executive approval	>$100K

Tools and References

Scripts

Tool	Purpose	Usage
management_review_tracker.py	Track review inputs, actions, metrics	`python management_review_tracker.py --help`

Management Review Tracker Features:

Track input collection status from process owners
Monitor action item completion and aging
Generate metrics summary for review
Produce recommendations for review focus areas

References

Document	Content
management-review-guide.md	ISO 13485 Clause 5.6 requirements, input/output templates, action tracking
quality-kpi-framework.md	KPI categories, targets, calculations, dashboard templates

Quick Reference: Management Review Inputs (ISO 13485 Clause 5.6.2)

Input	Source	Required
Feedback	Customer complaints, surveys	Yes
Audit results	Internal and external audits	Yes
Process performance	Process metrics	Yes
Product conformity	Inspection, NC data	Yes
CAPA status	CAPA system	Yes
Previous actions	Prior review records	Yes
Changes	Regulatory, organizational	Yes
Recommendations	All sources	Yes

Quick Reference: Management Review Outputs (ISO 13485 Clause 5.6.3)

Output	Documentation Required
Improvement to QMS and processes	Action items with owners
Improvement to product	Project initiation if needed
Resource needs	Resource plan updates

Related Skills

Skill	Integration Point
quality-manager-qms-iso13485	QMS process management
capa-officer	CAPA system oversight
qms-audit-expert	Internal audit program
quality-documentation-manager	Document control oversight

When it activates

Automatically, when a task matches. Use when leading management reviews, setting quality policy and objectives, monitoring quality KPIs and cost of quality, or exercising QMR governance and regulatory oversight responsibilities.

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alirezarezvani / claude-skills

.gemini/skills/quality-manager-qmr/SKILL.md

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License MITImported 11/7/2026Version v1Bundled 3 files · scripts

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Senior Quality Manager Responsible Person (QMR)

Table of Contents

QMR Responsibilities

ISO 13485 Clause 5.5.2 Requirements

QMR Accountability Matrix

Authority Boundaries

Management Review Workflow

Workflow: Prepare and Execute Management Review

Required Attendees

Management Review Input Template

Management Review Output Requirements

Quality KPI Management Workflow

Workflow: Establish Quality KPI Framework

Core Quality KPIs

KPI Review Frequency

Performance Response Matrix

Quality Objectives Workflow

Workflow: Annual Quality Objectives Setting

Quality Objective Structure

Objective Categories

Quarterly Objective Review

Quality Culture Assessment Workflow

Workflow: Annual Quality Culture Assessment

Quality Culture Dimensions

Culture Survey Categories

Culture Improvement Actions

Regulatory Compliance Oversight

Multi-Jurisdictional Compliance Matrix

Compliance Monitoring Workflow

Regulatory Authority Interface

Inspection Readiness Checklist

Decision Frameworks

Escalation Decision Tree

Quality Investment Prioritization

Resource Allocation Matrix

Tools and References

Scripts

References

Quick Reference: Management Review Inputs (ISO 13485 Clause 5.6.2)

Quick Reference: Management Review Outputs (ISO 13485 Clause 5.6.3)

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