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EU AI Act Compliance Specialist · alirezarezvani | DecimalAI

Legalby alirezarezvani

EU AI Act Compliance Specialist

EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer /

Never made a case worseMaintained

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

73%82%of test cases passed +9 pts

Without skill16 of 22

With skill18 of 22

2 fixed16 passed either way4 still failing0 broke

Measured on gemini-3.6-flash8/4/2026v1 · latest is v2

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 taskOur engineering team is preparing to launch a biometric access control system for our office buildings, which we already know falls under the high-risk category. We need to understand the correct conformity assessment path and the required documentation pack. Provide your answer starting with a brief bottom-line summary, followed by the exact article references, the conformity route decision, the evidence for this route, three concrete next steps with assigned owners and deadlines, and the final decision point for our compliance officer.

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-eu-ai-act-specialist
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-eu-ai-act-specialist")
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-eu-ai-act-specialist

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-eu-ai-act-specialist/SKILL.md https://app.decimal.ai/s/alirezarezvani-eu-ai-act-specialist/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/alirezarezvani-eu-ai-act-specialist/SKILL.md

This gets SKILL.md only. The skill's 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/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-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/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-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist")
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-eu-ai-act-specialist/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/alirezarezvani-eu-ai-act-specialist/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 7 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-eu-ai-act-specialist

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-eu-ai-act-specialist/SKILL.md https://app.decimal.ai/s/alirezarezvani-eu-ai-act-specialist/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-eu-ai-act-specialist/SKILL.md

This gets SKILL.md only. The skill's 7 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-eu-ai-act-specialist/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/alirezarezvani-eu-ai-act-specialist.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 8/4/2026)
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

EU AI Act Compliance Specialist

Article-cited operational skill for Regulation (EU) 2024/1689. Three decisions, no executive AI strategy:

What tier is this AI system? — prohibited (Article 5) / high-risk (Article 6 + Annex III) / limited-risk transparency (Article 50) / minimal-risk
For high-risk systems, what's the conformity assessment route + documentation pack? — Article 43 Module A vs Module H + Annex IV technical documentation
Per organizational role, what are the obligations? — provider / deployer / importer / distributor / authorized representative matrix per Article 16, 22, 25, 26

This skill is NOT chief-ai-officer-advisor. CAIO decides whether to ship the AI feature at all and accepts business risk. This skill operates the conformity work that turns "we'll ship it" into Article-compliant artefacts.

This skill is NOT a legal substitute. The Act is binding regulation. For novel cases (Is this a GPAI model? Does Article 6(2) carve-out apply? Is fine-tuning a foundation model "substantial modification"?), engage qualified outside counsel. The skill cites Articles + Annexes and uses Commission/EDPB published interpretation but does not provide binding legal opinion.

This skill is NOT GDPR. Many AI systems also trigger GDPR (training data, output processing). See ra-qm-team/skills/gdpr-dsgvo-expert/ for DPIA + lawful basis work. The Acts interact (Recital 10, Article 10 for high-risk training data).

Keywords

EU AI Act, EU AI Regulation, Regulation 2024/1689, AI Act, AI regulation Europe, high-risk AI, prohibited AI, Article 5 AI Act, Article 6 AI Act, Article 9 AI Act, Article 50 AI Act, Annex III, Annex IV, conformity assessment, CE marking AI, notified body AI, Module A, Module H, technical documentation AI, post-market monitoring AI, fundamental rights impact assessment, FRIA, GPAI, general-purpose AI model, systemic risk GPAI, AI Office, ENISA AI, EDPB AI, AI Act timeline, AI Act penalties, EU AI Act provider, EU AI Act deployer, EU AI Act importer, EU AI Act distributor, EU AI Act fines, AI literacy

Quick Start

# Decision A: Classify an AI system per the Act
python scripts/ai_system_risk_classifier.py                       # embedded 5-system sample
python scripts/ai_system_risk_classifier.py path/to/systems.json

# Decision B: Conformity assessment plan for a high-risk system
python scripts/conformity_assessment_planner.py                   # embedded high-risk sample
python scripts/conformity_assessment_planner.py path/to/system.json

# Decision C: Obligation tracker per organizational role
python scripts/ai_act_obligation_tracker.py                       # embedded sample (provider + deployer)
python scripts/ai_act_obligation_tracker.py path/to/roles.json

Key Questions (ask these first)

Does this AI system fall under Article 5 (prohibited practices)? Social scoring, emotion recognition in workplace/education, manipulative subliminal techniques, real-time remote biometric identification in public — any of these are flat-out prohibited.
Does it fall under Annex III (high-risk categories)? 8 categories: biometrics, critical infrastructure, education, employment, essential services, law enforcement, migration, justice. Triggering Annex III triggers Article 6(2) — unless the Article 6(3) carve-outs apply.
What organizational role does the company play? Provider (placed on market), deployer (uses under own authority), importer (places third-country system on EU market), distributor (makes available in supply chain). Many companies are BOTH provider AND deployer simultaneously.
Is this a general-purpose AI model? GPAI has its own track (Articles 51–55) with stricter rules above 10²⁵ FLOPs training compute (Article 51 systemic risk).
For high-risk: have we run Article 9 risk management AND Article 27 FRIA? Article 9 is the lifecycle risk management; Article 27 is the Fundamental Rights Impact Assessment for public-sector deployers + essential services.
What's the conformity assessment Module per Article 43? Module A (internal control, possible for most Annex III systems) vs Module H (full QMS + notified body, required for biometrics + sometimes others).

Core Responsibilities

1. AI System Risk Classification

The framework: The Act takes a risk-based approach (Recital 26). Each AI system falls into exactly one of four tiers:

Tier	Source	Examples	Obligations
Prohibited	Article 5	Social scoring; emotion recognition in workplace/education; subliminal manipulation; real-time public biometrics by law enforcement (with narrow exceptions)	Cannot be placed on market or used (penalties up to EUR 35M / 7% turnover)
High-risk	Article 6 + Annex III; Article 6(1) + Annex I	CV-screening, credit scoring, biometric categorisation, safety components of regulated products	Articles 8–17 (provider) + Article 26 (deployer); conformity assessment; CE marking
Limited-risk (transparency)	Article 50	Chatbots, deepfakes, emotion recognition outside Article 5 contexts	Transparency disclosures to natural persons
Minimal-risk	Default	Spam filters, video-game AI, inventory forecasters	None under the Act (voluntary codes of conduct, Article 95)

Critical carve-outs (Article 6(3)): an Annex III system is NOT high-risk if it (a) performs a narrow procedural task, (b) improves the result of previously completed human activity, (c) detects decision-making patterns without replacing human assessment, (d) performs a preparatory task. Caveat: profiling of natural persons is always Annex III high-risk regardless of carve-outs.

Run ai_system_risk_classifier.py with system characteristics. The tool checks Article 5 prohibitions first, then Annex III categories, then Article 6(3) carve-outs, then Article 50 transparency, then minimal-risk default.

See references/eu_ai_act_titles.md for the full Article-by-Article walkthrough.

2. Conformity Assessment + Annex IV Technical Documentation

The framework (Article 43 + Annex VI/VII): for high-risk AI systems, the provider must demonstrate conformity before placing on market. Two routes:

Module A — Internal control (Annex VI): provider self-assesses against the requirements. Applies to most Annex III systems where the provider has implemented harmonised standards.
Module H — Full quality management system + technical documentation (Annex VII): notified body involvement. Required for biometrics systems (Article 43(1)).

Required artifacts per Annex IV — Technical Documentation:

General description of the AI system (intended purpose, identification, version)
Detailed description of system elements (architecture, training data, validation procedures)
Information about monitoring, functioning and control
Description of risk management system (Article 9)
Description of changes after placing on market
List of harmonised standards applied (or alternative)
EU declaration of conformity (Article 47)
Description of the post-market monitoring system (Article 72)

Run conformity_assessment_planner.py to select the Module and produce the Annex IV checklist for a given high-risk system.

See references/high_risk_systems_annex_iii.md for which systems require which conformity route.

3. Per-Role Obligation Tracker

The framework (Articles 16, 22, 23, 24, 25, 26): the Act distinguishes provider obligations (most) from downstream-actor obligations (deployer, importer, distributor, authorized representative). A single company can play multiple roles simultaneously.

Role	Primary Articles	Key obligations
Provider (Article 3(3))	8–17, 47, 49, 72	Conformity assessment; CE marking; risk management; data governance; technical documentation; post-market monitoring; serious incident reporting (Article 73)
Deployer (Article 3(4))	26	Use according to instructions; human oversight; input data quality; record-keeping (Article 19); inform workers (Article 26(7)); FRIA if public-sector/essential-services (Article 27)
Importer (Article 3(6))	23	Verify conformity; affixed CE marking; technical documentation availability
Distributor (Article 3(7))	24	Verify CE marking + documentation before making available
Authorized representative (Article 22)	22	Non-EU providers must appoint one; representative liable for provider obligations

Important: under Article 25, a deployer who substantially modifies a high-risk AI system, or places it on the market under their own name, becomes a provider and inherits provider obligations.

Run ai_act_obligation_tracker.py with the roles JSON to produce a deadline-sorted obligation matrix.

See references/gpai_obligations.md for the separate GPAI Articles 51–55 track.

Workflows

Workflow 1: AI System Intake Review (per system, ~2 hours)

Goal: classify, identify obligations, scope the conformity work.

# 1. Document system characteristics: purpose, users, data, autonomy, deployment context
# 2. Run classifier
python scripts/ai_system_risk_classifier.py systems.json
# 3. If high-risk: run planner
python scripts/conformity_assessment_planner.py system.json
# 4. Identify org roles played (provider / deployer / both)
python scripts/ai_act_obligation_tracker.py roles.json
# 5. Cross-check with GDPR DPIA (gdpr-dsgvo-expert) if personal data
# 6. Cross-check with ISO 42001 AIMS evidence (compliance-team-iso42001)
# 7. Output: classification memo + conformity plan + obligation list

Workflow 2: Annex IV Technical Documentation Build (per high-risk system, 2–4 weeks)

Goal: assemble the Annex IV pack before conformity assessment.

# 1. Run conformity assessment planner to get the checklist
python scripts/conformity_assessment_planner.py system.json
# 2. Assemble: system description, architecture, training data, validation, risk management
# 3. Reference ISO 42001 evidence where it satisfies Annex IV items
# 4. Reference ISO 27001 evidence for security controls
# 5. Run Article 9 risk management lifecycle
# 6. Sign EU declaration of conformity (Article 47) AFTER assessment passes
# 7. Affix CE marking (Article 48)
# 8. Register in EU database (Article 71) — high-risk Annex III systems

Workflow 3: Pre-Deployment Obligation Audit (per system, before launch)

Goal: confirm all active obligations are in place before EU placement.

# 1. Confirm classification still correct (re-run classifier if system changed)
# 2. Confirm conformity assessment completed (if high-risk)
# 3. Confirm transparency requirements (Article 50) — for chatbots, deepfakes, emotion detection
# 4. Confirm post-market monitoring system (Article 72) is live
# 5. Confirm serious-incident reporting procedure (Article 73) is documented
# 6. For deployers: FRIA done (Article 27, if applicable); workers informed (Article 26(7))
# 7. For GPAI: Articles 51-55 obligations met if applicable

Workflow 4: Annual Compliance Refresh (per organization, yearly)

Goal: re-verify classifications + obligations as the Act phases in.

List all AI systems on or planned for EU market
Run classifier for each — Article 5 prohibited list may expand via delegated acts
Run obligation tracker — deadlines shift as the Act phases in (2025 → Art. 50 in Aug 2026 → Annex III high-risk Dec 2027 → Annex I Aug 2028, per Regulation (EU) 2026/1744). Re-check the dates on every refresh: the Omnibus showed they can move
For each high-risk system: verify post-market monitoring data flow + serious incident reporting capacity
Update Annex IV technical documentation per Article 11 ongoing requirement
Pair with ISO 42001 management review (Clause 9.3) if both operate

Output Standards

**Bottom Line:** [one sentence — classification + most-significant obligation]
**Article Citation:** [Article + paragraph number; do not paraphrase without cite]
**The Decision:** [one of: classify | conformity-route | obligation-scope]
**The Evidence:** [Article + Annex references; classification confidence]
**How to Act:** [3 concrete next steps with owner + deadline aligned to phasing]
**Your Decision:** [the call for compliance officer or legal counsel — risk-class disputes, novel cases, GPAI threshold determinations]

Adjacent Skills

ra-qm-team/skills/gdpr-dsgvo-expert/ — GDPR DPIA + lawful basis (most AI systems also trigger GDPR)
ra-qm-team/compliance-team-iso42001/ — ISO 42001 AIMS (voluntary management system that satisfies parts of Article 17 QMS for providers)
ra-qm-team/skills/information-security-manager-iso27001/ — ISO 27001 for cybersecurity requirements (Article 15)
ra-qm-team/skills/risk-management-specialist/ — ISO 14971 risk management (referenced for safety-component AI under Article 6(1))
ra-qm-team/skills/mdr-745-specialist/ — MDR 2017/745 (medical-device AI overlap)
compliance-os/ — Meta-orchestrator for multi-framework programs
c-level-advisor/chief-ai-officer-advisor/ — Executive AI strategy

References

eu_ai_act_titles.md — Titles I–XII Article-by-Article walkthrough with deployer/provider/importer/distributor obligation breakdown
high_risk_systems_annex_iii.md — Annex III 8 categories detailed + Article 6(2)–(3) interaction + carve-out test
gpai_obligations.md — Articles 51–55 GPAI track + systemic-risk threshold + transparency rules + Code of Practice status
cross_framework_mapping_ai_act.md — AI Act ↔ ISO 42001 ↔ NIST AI RMF ↔ GDPR control-level mapping

Version: 1.0.0 Status: Production Ready

About this skill

Automatic

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alirezarezvani / claude-skills

.gemini/skills/eu-ai-act-specialist/SKILL.md

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License MITImported 11/7/2026Version v2Bundled 7 files · scripts

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EU AI Act Compliance Specialist

Keywords

Quick Start

Key Questions (ask these first)

Core Responsibilities

1. AI System Risk Classification

2. Conformity Assessment + Annex IV Technical Documentation

3. Per-Role Obligation Tracker

Workflows

Workflow 1: AI System Intake Review (per system, ~2 hours)

Workflow 2: Annex IV Technical Documentation Build (per high-risk system, 2–4 weeks)

Workflow 3: Pre-Deployment Obligation Audit (per system, before launch)

Workflow 4: Annual Compliance Refresh (per organization, yearly)

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