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ISO Standards Readiness Evidence Preparation · k-dense-ai | DecimalAI

Legalby k-dense-ai

ISO Standards Readiness Evidence Preparation

Prepares and structurally reviews readiness evidence for ISO management-system and laboratory-competence standards - ISO 13485 medical device QMS, ISO 14971 device risk management, ISO/IEC 17025 testing and calibration laboratories, and ISO 15189 medical laboratories.

Maintained

SkillScore

SkillScore71/100

Test results 71

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

21%71%of test cases passed +50 pts

Without skill5 of 24

With skill17 of 24

13 fixed4 passed either way6 still failing1 broke

Measured on gemini-3.6-flash9/8/2026v2 · latest is v321 comparable

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe have assembled a local evidence manifest for ISO/IEC 17025 export files located in `/exports/lab-data/`. What exact CLI command validates this manifest against local JSON/Markdown files and outputs a report to `/exports/manifest-report.json`?

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 k-dense-ai-iso-standards-readiness
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("k-dense-ai-iso-standards-readiness")
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 k-dense-ai-iso-standards-readiness

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/k-dense-ai-iso-standards-readiness/SKILL.md https://app.decimal.ai/s/k-dense-ai-iso-standards-readiness/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/k-dense-ai-iso-standards-readiness/SKILL.md

This gets SKILL.md only. The skill's 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/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/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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("k-dense-ai-iso-standards-readiness")
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/k-dense-ai-iso-standards-readiness/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/k-dense-ai-iso-standards-readiness/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 19 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 k-dense-ai-iso-standards-readiness

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/k-dense-ai-iso-standards-readiness/SKILL.md https://app.decimal.ai/s/k-dense-ai-iso-standards-readiness/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/k-dense-ai-iso-standards-readiness/SKILL.md

This gets SKILL.md only. The skill's 19 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/k-dense-ai-iso-standards-readiness/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/k-dense-ai-iso-standards-readiness.json

This gets SKILL.md only. The skill's 19 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v2 tested 9/8/2026)
Source: k-dense-ai/scientific-agent-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

ISO Standards Readiness Evidence Preparation

Purpose

Use this skill to organize declared scope, controlled documents, implementation records, traceability, and readiness evidence for substantive human review against a named standard. It summarizes process workflows and provides deterministic local checks. It contains no clause text and performs no audit.

SKILL.md holds the boundary, lane discipline, shared evidence workflow, and CLI contract. Per-standard preparation details live in references/.

Non-negotiable boundary

This skill cannot:

certify or accredit anything, issue or validate a certificate, accreditation schedule, or licence, or promise an audit, assessment, or inspection result;
determine legal/regulatory applicability, device classification, reportability, conformity route, product authorization, market access, licensure, personnel qualification, or compliance;
replace authorized management, the management representative, laboratory director, quality manager, authorized signatory, RA/QA, legal counsel, regulatory/competent authorities, a notified body, an MDSAP Auditing Organization, an accreditation body, an assessor, or a certification body;
validate a method, compute or approve measurement uncertainty, establish metrological traceability, set risk-acceptability criteria, or judge whether a risk, decision rule, or reference interval is fit for purpose; or
infer implementation, competence, conformity, compliance, or readiness from a template, checklist, filename, keyword, document count, percentage, or script result.

Always label outputs draft evidence-preparation material for authorized human review. Preserve unresolved decisions as blockers rather than resolving them.

ISO and IEC copyright

ISO and IEC standards are copyrighted. Obtain each standard from ISO, IEC, an ISO national member, or another authorized source. Do not retrieve, paste, reproduce, or generate clause text. Summarize the organization's own process and cite the controlled authorized copy. See ISO copyright. Accreditation-body, CAP, and scheme checklists that quote requirements are separately licensed — keep them out of shared repositories and prompts too.

Standards covered

Read the reference file for the standard in play before preparing evidence. Each one carries its own current edition, lane, domain vocabulary, and failure modes.

Standard	Profile key	Lane	Reference
ISO 13485 medical device QMS	`iso-13485`	Certification	`references/iso-13485.md`
ISO 14971 device risk management	`iso-14971`	Supports a declared lane	`references/iso-14971.md`
ISO/IEC 17025 testing and calibration laboratories	`iso-17025`	Accreditation	`references/iso-17025.md`
ISO 15189 medical laboratories	`iso-15189`	Accreditation	`references/iso-15189.md`

A standard absent from this table is out of scope for the bundled checks. Do not repurpose a profile for a standard it does not name — a domain vocabulary borrowed from a different standard produces a report that looks complete and means nothing.

Current baseline (read the ledger before any time-sensitive statement)

ISO 13485:2016 Edition 3, confirmed after its 2025 systematic review. EN ISO 13485:2016/A11:2021 is a European amendment, not an ISO international "Amendment 1:2021."
ISO 14971:2019 Edition 3, confirmed in 2025, with ISO/TR 24971:2020 as its informative guidance companion. Its proposed successor ISO/AWI TS 24971-1 is under development, not a published replacement or a new normative basis.
ISO/IEC 17025:2017 Edition 3 remains current; no successor edition identified.
ISO 15189:2022 Edition 4 replaced the 2012 edition, absorbed the POCT requirements formerly in ISO 22870, and its accreditation transition closed in December 2025 — implemented, not upcoming.
FDA QMSR effective and enforced since 2026-02-02; Part 820 is titled Quality Management System Regulation; QSIT is retired in favour of Compliance Program 7382.850.
MDSAP current Audit Approach is MDSAP AU P0002.011, version date 2026-08-03.
Accreditation recognition: Global Accreditation Cooperation Incorporated commenced full operations 2026-01-01, replacing ILAC and IAF, with its own MRA; former IAF MLA / ILAC MRA outputs stay recognized during the transition.
EU: use current consolidated MDR/IVDR texts, current OJEU harmonised-standard decisions, current MDCG guidance, and the product-specific conformity route.

Read references/source-ledger.md before making any time-sensitive statement. It records catalogue confirmation, published versus draft documents, and the remaining EU source-retrieval limitations. The bundled report basis_date is this skill release's research baseline, not the organization's review date or a freshness guarantee.

Keep the assurance lanes separate

Lane confusion, not missing documents, causes most substantive errors here. Certification, accreditation, regulator inspection, mandatory licensure, regulatory audit programmes, and product conformity assessment are decided by different bodies against different bases, and none substitutes for another. Two rules that are violated constantly:

Organizations are certified; laboratories are accredited. "ISO 17025 certified" and "ISO 15189 certified" are category errors.
A certificate never displaces a regulator. ISO 13485 certification does not exempt anyone from FDA inspection, and ISO 15189 accreditation does not satisfy CLIA.

Read references/assurance-lanes.md for the full lane table, scope-statement limits, and the titling rule.

Core workflow

Step 1: Declare the standard, purpose, and authorized owners

Name the standard(s), the lane(s) the work supports, and the owners: management representative or laboratory director, quality owner, legal/applicability owner, process or technical owners, approvers, and escalation route. A lane is a declared input, never an inference.

PYTHONDONTWRITEBYTECODE=1 python3 scripts/validate_scope_intake.py \
  assets/templates/scope-intake-template.json --standard iso-13485

Use the matching template and profile:

Profile	Template
`iso-13485`, `iso-14971`	`assets/templates/scope-intake-template.json`
`iso-17025`	`assets/templates/laboratory-scope-intake-template.json`
`iso-15189`	`assets/templates/medical-laboratory-scope-intake-template.json`

--standard defaults to iso-13485. Every distributed template intentionally fails closed; copy it outside the skill and complete it with controlled organizational evidence. Undetermined applicability raises HUMAN_DECISION_REQUIRED — leave it as a blocker.

Step 2: Freeze source/version evidence

For every standard, regulation, guidance, scheme document, audit model, and product source, record publisher, official title, edition/version/date, authorized location, access and currency-review dates, scope/applicability owner, impact assessment, status, evidence, and approval.

Do not use search snippets as controlled requirements. Do not silently update an incorporated edition when a publisher releases a new one — FDA incorporated a specific ISO 13485 edition, and a later ISO or EN publication does not change it.

Step 3: Inventory controlled documents and records

Do not count named procedures or scan keywords. Build an explicit register linking documents, records, source versions, owners, approvals, effective dates, retention bases, training, and change records.

PYTHONDONTWRITEBYTECODE=1 python3 scripts/audit_document_records.py \
  assets/templates/document-register-template.json

This check is standard-agnostic. Read references/evidence-architecture.md for the evidence architecture.

Step 4: Review process implementation

Assess controlled procedures and sampled records across the domains your profile declares — the per-standard reference file lists them. Each item needs owner, status, evidence IDs, source/version, approval, and open-gap links.

A procedure describing an activity is not evidence the activity happened. Sample records in every domain you report on, and state what you sampled and what you did not.

Step 5: Run the focused checks that apply to the lane

Device lanes (iso-13485, iso-14971) — risk/design/production/post-market chain:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/check_traceability.py \
  assets/templates/traceability-matrix-template.json

All standards — corrective action and effectiveness:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/check_capa.py \
  assets/templates/capa-record-template.json

All standards — suppliers and externally provided products and services, including calibration providers, reference-material suppliers, and referral or subcontracted laboratories:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/check_supplier_controls.py \
  assets/templates/supplier-controls-template.json

For a calibration supplier, link the purchased measurand, range, method, and location to the provider's dated accreditation scope and applicable calibration and measurement capability, using the current Global ACI-TECH-1-009 (M) policy basis (successor to ILAC P14; see the source ledger). Capture the actual certificate's reported uncertainty separately; a scope CMC is not automatically the uncertainty of the delivered calibration. Leave any coverage or suitability judgment to the authorized technical owner.

Pending or ineffective CAPA effectiveness evidence blocks closure. High-risk supplier controls stay blocked until risk-based controls and approvals are evidenced.

Note that check_traceability.py concerns design and risk traceability, not metrological traceability — the words collide and it is the wrong tool for laboratory work.

Step 6: Address lane-specific regulator evidence separately

For the US device lane only:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/check_qmsr_transition.py \
  assets/templates/qmsr-transition-template.json

Review current Part 820/FDA source basis, supplemental provisions, obsolete QSR/QSIT references, pre-effective-date records, inspection-accessible management/quality/ supplier-audit records, current inspection-process training, complaint and servicing records, labeling/packaging controls, supplier/software/change evidence, and prohibited certificate-equivalence claims. Do not build an old-820-to-ISO clause map as the current control framework.

Laboratory lanes have no equivalent bundled check. CLIA, licensure, and national inspection evidence stays with the authorized compliance owner; see references/iso-15189.md.

Step 7: Assemble a bounded readiness manifest

Copy the evidence template outside the skill. Use relative paths to local .json, .md, or .markdown evidence only, and one declared lane purpose per manifest.

PYTHONDONTWRITEBYTECODE=1 python3 scripts/validate_evidence_manifest.py \
  /path/to/evidence-manifest.json \
  --standard iso-17025 \
  --base-dir /path/to/controlled-export \
  --verify-files \
  --output /path/to/manifest-report.json

Then generate a domain-level gap view against the same profile:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/gap_analyzer.py \
  /path/to/evidence-manifest.json \
  --standard iso-17025 \
  --base-dir /path/to/controlled-export \
  --verify-files \
  --output /path/to/gap-report.json

The analyzer uses explicit manifest labels. It does not infer evidence from filenames, keywords, or proprietary standard text, and does not calculate a compliance score. A domain absent from expected_domains is reported not-assessed, which is not a not-applicable determination. Entry-level structural findings, including a requested hash mismatch, prevent an evidence-present-for-human-review domain label. Always read the report-level findings too; domain labels do not establish file authenticity or substantive adequacy.

Read references/gap-analysis-checklist.md for the fail-closed review questions.

Step 8: Human review and controlled handoff

Present:

declared standard, scope, assurance lane(s), and unresolved applicability decisions;
the exact source/version baseline;
evidence sampled and the limitations of that sample;
structural findings grouped by process and risk;
actions, change, and CAPA owners with dates;
approval state; and
the authorized party responsible for the next decision.

Never title the result "certificate," "accreditation," "compliance report," "audit pass," "deemed status," or "ready for inspection." A suitable title is Draft evidence review for authorized human assessment, naming the lane it was prepared for.

CLI behavior and safety

All bundled CLIs:

use the Python standard library only;
perform no network requests;
accept bounded local JSON; optional evidence verification accepts only bounded local JSON/Markdown;
reject symbolic-link inputs, duplicate JSON keys, non-finite numbers, excessive size/nesting/items, and unsafe evidence paths;
refuse an unlisted --standard value rather than falling back to a default;
use no dynamic evaluation, executable deserialization, pickle, or shell execution;
refuse to overwrite reports unless --force is explicit; and
produce deterministic sorted JSON.

Treat the manifest itself as a controlled organizational record. An optional SHA-256 comparison detects a local file mismatch only; it does not establish provenance, authenticity, adequacy, or trust in a user-supplied manifest. Values in JSON local_path and evidence.location fields refer to the user's controlled export, not to bundled skill resources; unresolved placeholders must never be opened.

Exit codes:

0: no structural finding for the supplied fields; not a compliance, conformity, competence, or accreditation result;
1: structural/evidence gaps found;
2: invalid or unsafe input/output, including an unlisted standard.

Run python3 scripts/<name>.py --help for each interface.

Templates

Scope intake, per profile:

assets/templates/scope-intake-template.json — device lifecycle
assets/templates/laboratory-scope-intake-template.json — testing/calibration
assets/templates/medical-laboratory-scope-intake-template.json — examinations

Shared registers and records:

assets/templates/document-register-template.json
assets/templates/capa-record-template.json
assets/templates/traceability-matrix-template.json
assets/templates/supplier-controls-template.json
assets/templates/evidence-manifest-template.json
assets/templates/qmsr-transition-template.json — US device lane only

Management-system documentation:

assets/templates/quality-manual-template.md
assets/templates/procedures/CAPA-procedure-template.md
assets/templates/procedures/document-control-procedure-template.md

Every template is deliberately draft/pending, uses placeholders, and includes owner/status/evidence/approval fields. Copy and control it; never edit a distributed template into a purported approved record.

References

Shared:

references/assurance-lanes.md — what each lane decides, and the titling rule
references/source-ledger.md — dated authoritative source baseline and provenance limitations
references/evidence-architecture.md — documentation and record architecture
references/gap-analysis-checklist.md — fail-closed evidence review questions
references/quality-manual-guide.md — controlled manual development

Per standard:

references/iso-13485.md — device QMS process/evidence framework, QMSR, MDSAP, EU
references/iso-14971.md — risk-management chain and the missing-link failure modes
references/iso-17025.md — laboratory competence, traceability, uncertainty, and decision rules
references/iso-15189.md — medical laboratories, POCT, reporting, and the CLIA lane

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

When it activates

Automatically, when a task matches. Use when organizing declared scope, controlled documents, risk-management files, scope of accreditation, traceability, CAPA, external-provider controls, or bounded local evidence manifests, and when separating ISO certification fro

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 v5 — 10/8/2026

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

k-dense-ai / scientific-agent-skills

skills/iso-standards-readiness/SKILL.md

View on GitHub ↗

License MITImported 10/8/2026Version v3Bundled 19 files · scripts

Illustrative Want to see what k-dense-ai/iso-standards-readiness 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.

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SkillScore

Safety

What it changes

Track record

Use this skill

ISO Standards Readiness Evidence Preparation

Purpose

Non-negotiable boundary

ISO and IEC copyright

Standards covered

Current baseline (read the ledger before any time-sensitive statement)

Keep the assurance lanes separate

Core workflow

Step 1: Declare the standard, purpose, and authorized owners

Step 2: Freeze source/version evidence

Step 3: Inventory controlled documents and records

Step 4: Review process implementation

Step 5: Run the focused checks that apply to the lane

Step 6: Address lane-specific regulator evidence separately

Step 7: Assemble a bounded readiness manifest

Step 8: Human review and controlled handoff

CLI behavior and safety

Templates

References

Citing Scientific Agent Skills

When it activates

SkillSafety

Source

Related skills

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