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Azure Reliability Assessment & Configuration · microsoft | DecimalAI

DevOpsby microsoft

Azure Reliability Assessment & Configuration

Assess and improve the reliability posture of PaaS Applications (Azure Functions and Azure App Service). Scans deployed resources for zone redundancy, ZRS storage, health probes, and multi-region failover. Presents a feature-pivoted checklist, then drives staged remediation (CLI or IaC patches) end-to-end with user confirmation. WHEN: \"assess reliability\", \"check reliability\", \"zone redundant\", \"multi-region failover\", \"high availability\", \"disaster recovery\", \"single points of fail

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

5%68%of test cases passed +64 pts

Without skill1 of 22

With skill15 of 22

14 fixed1 passed either way7 still failing0 broke

Measured on gemini-3.6-flash7/23/2026v117 comparable

Track record

Agent runs: 0; All time

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 azure-reliability
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("azure-reliability")
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 azure-reliability

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/azure-reliability/SKILL.md https://app.decimal.ai/s/azure-reliability/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/azure-reliability/SKILL.md

This gets SKILL.md only. The skill's 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/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/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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("azure-reliability")
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/azure-reliability/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/azure-reliability/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 10 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 azure-reliability

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/azure-reliability/SKILL.md https://app.decimal.ai/s/azure-reliability/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/azure-reliability/SKILL.md

This gets SKILL.md only. The skill's 10 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/azure-reliability/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/azure-reliability.json

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/23/2026
Source: microsoft/azure-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

Azure Reliability Assessment & Configuration

Quick Reference

Property	Details
Best for	Reliability posture assessment, zone redundancy enablement, multi-region failover setup
Primary capabilities	Reliability assessment table, Zone Redundancy Configuration, Multi-Region IaC Generation
Supported services	Azure Functions, App Service (Container Apps planned for a future version)
MCP tools	Azure Resource Graph queries, Azure CLI commands

When to Use This Skill

Activate this skill when user wants to:

"Assess my Function app's reliability"
"Assess my Web app's reliability"
"Check the reliability of my resource group" (App Service and Functions resources only)
"Is my app zone redundant?" (App Service and Functions resources only)
"Is my app service plan zone redundant?"
"Make my app zone redundant" (App Service and Functions resources only)
"Make my app service plan zone redundant"
"Set up multi-region failover for my app" (App Service and Functions resources only)
"Check my reliability posture"
"Find single points of failure" (App Service and Functions resources only)
"Enable high availability for my app" (App Service and Functions resources only)
"Check disaster recovery readiness"
"Improve my app's resilience" (App Service and Functions resources only)

Scope note: This skill currently covers Azure Functions and Azure App Service only. If the user asks about Azure Container Apps reliability, acknowledge that support is planned but not yet available, and only proceed with the parts that apply to App Service and Functions resources in scope.

Prerequisites

Authentication: user is logged in to Azure via az login
Permissions: Reader access on target subscription/resource group (for assessment)
Permissions: Contributor access (for configuration changes)
Azure Resource Graph extension: az extension add --name resource-graph

MCP Tools

Tool	Purpose
`mcp_azure_mcp_extension_cli_generate`	Generate `az` CLI commands for resource queries and configuration
`mcp_azure_mcp_subscription_list`	List available subscriptions
`mcp_azure_mcp_group_list`	List resource groups

Primary query method: Azure Resource Graph via az graph query (requires az extension add --name resource-graph).

Assessment Workflow

Phase 1: Discover Resources

Identify scope — Ask user for resource group, subscription, or app name
Query Azure Resource Graph to discover all resources in scope
Classify resources by service type (Functions, Storage, etc.). If non-Functions compute (App Service sites that aren't Function Apps, Container Apps) is found, note it but do not deep-dive — those services are planned for a future version of this skill.

Important: Always scope queries to the user's specified resource group or subscription. Add these filters to every Resource Graph query:

Resource group: | where resourceGroup =~ '<rg-name>'
Subscription: Use --subscriptions <sub-id> flag on az graph query
App name: | where name =~ '<app-name>'

Phase 2: Assess Reliability

Two-step assessment: platform-level discovery first, then per-service deep dive.

Step 1 — Platform discovery (find what's there). Use these to enumerate resources in scope and detect cross-cutting reliability gaps:

Platform check	Reference
Zone redundancy — discovery	references/zone-redundancy-checks.md
Storage redundancy (cross-service)	references/storage-redundancy-checks.md
Multi-region & global load balancers	references/multi-region-checks.md
Front Door / Traffic Manager / App Insights probes	references/health-probe-checks.md

Step 2 — Per-service deep dive. For each compute resource discovered in Step 1, load the matching service reference. The service reference is the single source of truth for that service's plan/SKU rules, assessment queries, CLI commands, IaC patches (Bicep + Terraform + AVM), and reporting hints.

This skill version ships only the Azure Functions and App Service per-service references. Other compute services are listed below explicitly so the dispatch logic is unambiguous: if a resource matches an unsupported row, do not attempt to load a reference, fabricate CLI commands, or generate IaC patches for it.

Service detected	Reference
Azure Functions (`microsoft.web/serverfarms` with `kind contains 'functionapp'`)	references/services/functions/reliability.md
Azure App Service (non-Functions sites: `microsoft.web/sites` without `kind contains 'functionapp'`, `microsoft.web/serverfarms` without `kind contains 'functionapp'`)	references/services/app-service/reliability.md
Azure Container Apps (`microsoft.app/containerapps`, `microsoft.app/managedenvironments`)	⚪ Not yet shipped — planned for a future version

Handling unsupported services: If a resource matches an unsupported row above, surface it in the discovery summary, mark it as ⚪ not assessed (planned) in the Phase 3 table, and skip the per-service remediation steps for it. Do not attempt to fabricate CLI commands or IaC patches for those services.

Phase 3: Generate Reliability Checklist

Present findings as a feature-pivoted table: one row per reliability feature (Zone redundancy on compute, Zone-redundant storage, Health probes, Multi-region failover), with a single status indicator and the specific resources that are relevant to that feature. This avoids the noise of one-row-per-resource with mostly n/a cells. Do not assign numeric scores or grades.

🔍 Reliability Assessment — {scope}
─────────────────────────────────────────────────────────────────────────────────────────────
Reliability Feature              Status      Resources
─────────────────────────────────────────────────────────────────────────────────────────────
Zone redundancy — compute        🔴 OFF      • plan-web-ii5trxva2ark4 (P1v3)
                                              • plan-ii5trxva2ark4 (FC1)

Zone-redundant storage           🔴 GRS      • stii5trxva2ark4 (defaulted; no SKU set in IaC)

Health probes                    🔴 OFF      • func-api-ii5trxva2ark4 — needs code change (FC1)
                                              • app-web-ii5trxva2ark4 — no health check path

Multi-region failover            🔴 OFF      • Single region (eastus) only — Front Door not configured
─────────────────────────────────────────────────────────────────────────────────────────────

Want me to fix the 🔴 items? I'll do the quick wins first (App
plan zone redundancy + health checks on supported plans), then ask before
storage migration and multi-region setup. (yes/no)

Rules for the table:

Four feature rows, in this order: Zone redundancy — compute · Zone-redundant storage · Health probes · Multi-region failover. Omit a row entirely only if no resource in scope could ever apply to it.
Status column is one symbol + one short word, no other characters:
- 🟢 ON — feature is fully enabled across all relevant resources in scope
- 🟡 PARTIAL — some resources have it, some don't (or partial config like liveness-only)
- 🔴 OFF — feature is missing on all relevant resources
- For storage, replace OFF with the current SKU when relevant (🔴 LRS, 🔴 GRS, 🟢 ZRS, 🟢 GZRS). When no SKU is set in IaC, label as 🔴 GRS (ARM/AVM default) and note that in the resource line.
Resources column lists only what's relevant to that feature, one bullet per resource:
- For "needs fixing" resources, include a short inline reason ((FC1), (defaulted; no SKU set), liveness only, needs code change (FC1)).
- For resources that are already ON for that feature, mention them on the same row with — already ON so the user sees credit for what's right.
Do not include n/a, —, or empty cells. If a feature doesn't apply to any resource in scope, drop the row.
Do not include numeric scores, grades, or point totals.
End the assessment with a single yes/no question that kicks off the staged remediation flow. Do not enumerate the per-resource fix list here — the user will see it after they say yes (Configuration Workflow Step 1).

UX Note: If the assessment finds the app already has all core reliability features (zone redundancy, ZRS/GZRS storage, health probes), skip the fix-it question and jump straight to Configuration Workflow Step 3 (Multi-region follow-up). Do NOT start any multi-region work without explicit consent.

Configuration Workflow

When user wants to fix findings from the assessment:

⛔ ALWAYS confirm with user before executing changes. Show what will change, any cost implications, and any destructive actions (e.g., environment recreation).

Step 1: Present Fix Plan + Choose Path

After assessment, if user says "fix it" / "improve my reliability" / "enable zone redundancy":

List each fixable finding with the specific action
Flag any cost implications or breaking changes
Ask user which path they want:

I'll start with the quick wins (no downtime, fast):

1. ✏️  Enable zone redundancy on plan-ii5trxva2ark4 (Flex Consumption — no cost change)
2. ✏️  Set health check path to /api/health on func-api-ii5trxva2ark4

Then, separately, I'll ask if you want to upgrade storage:

3. 🕒  Upgrade stii5trxva2ark4 from LRS → ZRS (small cost increase, migration takes hours)
   — Required for full zone redundancy, but I'll confirm with you before starting.

How would you like to apply these changes?

  A) Fix now — Run az CLI commands against your live resources (immediate, one-time)
  B) Patch my IaC — Update your Bicep/Terraform files so changes persist across deploys

(If you use azd or Terraform, option B is recommended so `azd up` won't overwrite changes.)

Path A: Fix Now (CLI)

Run fixes against live resources using az CLI commands. Quick wins first, then ask before the slow storage migration.

The exact CLI commands per service live in the per-service references — pick the one(s) matching the resources discovered in Phase 2:

Fix	Reference
Enable zone redundancy / configure health probes (Functions)	references/services/functions/reliability.md
Enable zone redundancy / configure health probes (App Service)	references/services/app-service/reliability.md
Upgrade storage replication (cross-service)	references/configure-storage.md
Set up multi-region (cross-service)	references/configure-multi-region.md
Platform overview / verification	references/configure-zone-redundancy.md, references/configure-health-probes.md

Execution order — always quick wins first:

Zone redundancy on compute (fast, in-place property update on the App's plan).
Health probes (Premium / Dedicated only — in-place; for FC1 / Consumption, follow the consent gate in configure-health-probes.md).
Verify the compute changes succeeded before doing anything else.

⛔ STOP — Ask about storage upgrade. Compute is now zone-redundant, but storage may still be LRS or GRS. Ask the user explicitly:

✅ Compute is now zone-redundant.

To be **fully zone-redundant**, your storage account also needs to be upgraded:
  • stii5trxva2ark4: currently `Standard_LRS` → needs `Standard_ZRS`

⚠️  This is a live storage redundancy conversion:
   • Takes hours to days depending on data volume
   • Small ongoing cost increase (~$0.01/GB/month more)
   • Only supported for Standard general-purpose v2 accounts

Do you want me to start the storage migration now? (yes / no / later)

yes → run az storage account update --sku Standard_ZRS (or migration start if needed); poll az storage account show --query sku.name until it reports Standard_ZRS.
no / later → leave storage as-is; note in the re-assessment that ZR storage remains a gap.

Multi-region — do NOT auto-run. Handled in Step 3 below as an explicit follow-up after re-assessment.

⚠️ Warning: If the user uses azd up or terraform apply later, CLI-only changes may be overwritten by the IaC definitions. Recommend also patching IaC after CLI fixes.

Path B: Patch IaC

Update the user's Bicep or Terraform files so reliability settings are persistent.

Step 1: Detect IaC type

Look for infra/ folder in project root
If not found, check project root for *.bicep or *.tf files
If still not found, ask user: "Where are your IaC files located?"
Check for *.bicep files → use Bicep patching
Check for *.tf files → use Terraform patching
If both exist, ask user which to patch
If no IaC exists, fall back to Path A (CLI) and inform user

Step 2: Classify each fix by risk level

Fix	Risk Level	What Happens
Zone redundancy (App plan)	🟢 Safe patch	In-place property update on next deploy
Storage LRS → ZRS	🟡 Pre-migration required	Live storage migration must complete before the IaC SKU change can deploy. Never bundle with safe patches — use the two-deploy flow in Steps 3–5.
Health check path (Basic/Standard/Premium / Dedicated)	🟢 Safe patch	In-place update, but causes app restart
Health check path (FC1 / Consumption)	⚪ Code-only — ask first	`healthCheckPath` is unsupported. Adding a health endpoint requires adding an HTTP-triggered `/api/health` function to app code. Always ask the user for explicit consent before touching source code. Do not patch IaC.

Step 3: Apply patches in two deploys (quick wins first)

The IaC patching framework (detection, AVM-module guidance, deploy-order rule, storage SKU patch) lives in:

IaC Type	Framework reference
Bicep	references/iac-patching-bicep.md
Terraform	references/iac-patching-terraform.md

The actual per-service compute patches (Function App plan ZR, App Service Plan ZR, etc.) live in the per-service references — load the matching service file from Phase 2 for the exact Bicep / Terraform / AVM snippets. Only Azure Functions and App Service have per-service references in this skill version; Container Apps is out of scope.

Deploy 1 — Quick wins only. Patch the 🟢 Safe items (zone redundancy on the App Service/Function App plan, health probes on Basic/Standard/Premium / Dedicated). Do NOT include the storage SKU patch in this deploy.

After patching, the skill runs the deploy itself (do not stop and tell the user to run it). Detect the deployment tool and confirm once before executing:

📦 Patches applied to your IaC. Ready to deploy:
   Tool detected: azd (found azure.yaml)
   Command:       azd up

Proceed with deployment? (yes / no)

On yes, run the appropriate command, stream output back to the user, and continue to the next step on success:

AZD project (has azure.yaml): azd up
Bicep-only: az deployment group create --resource-group <rg> --template-file infra/main.bicep --parameters @infra/main.parameters.json
Terraform: terraform plan -out tfplan → (show plan summary) → terraform apply tfplan

On no, stop and report the patched files; do not proceed to Step 4 / Re-Assess.

If deployment fails, surface the error and stop — do not continue to the storage step.

⛔ STOP — Ask about storage upgrade before Deploy 2. After Deploy 1 succeeds, ask the user explicitly:

✅ Quick-win patches deployed. Compute is now zone-redundant.

To be **fully zone-redundant**, your storage account also needs to be upgraded:
  • stii5trxva2ark4: currently `Standard_LRS` → needs `Standard_ZRS`

⚠️  This is a two-part change:
   1. Live storage migration (`az storage account migration start`) — takes hours to days
   2. A second deploy to update your IaC's storage SKU to match

Do you want me to start the storage migration now? (yes / no / later)

yes → the skill runs the migration command itself, polls until complete, then patches the storage SKU in IaC and runs Deploy 2 (now a no-op confirmation). The user does not need to run anything manually.
no / later → leave the storage SKU patch unapplied. Note in the re-assessment that ZR storage remains a gap; suggest revisiting later.

Step 4: Storage migration (only if user said yes in Step 3)

The skill runs these commands itself — do not ask the user to run them. Show progress as you go:

🔄 Starting storage migration (this can take up to 72 hours)...

   az storage account migration start --name stii5trxva2ark4 \
     --resource-group rg-example --sku Standard_ZRS --no-wait

   Polling: az storage account show --name stii5trxva2ark4 --query sku.name
   ...
   ✅ Migration complete: sku.name = Standard_ZRS

For very long migrations, you may surface a checkpoint to the user ("this is still running, check back later") rather than blocking the entire conversation.

Step 5: Deploy 2 — storage SKU patch

After the migration completes, the skill patches the storage SKU in IaC and runs the same deploy command as Step 3 (e.g. azd up). This deploy is a no-op confirmation that the IaC matches the live state. Confirm once with the user before executing, then run it directly.

Step 2 (both paths): Re-Assess

After changes are applied (CLI) or deployed (IaC), automatically re-run the assessment and show the same feature-pivoted table as Phase 3, with each feature row's status updated to reflect the new state. Briefly call out what changed since the previous run.

🔄 Reliability Re-Assessment — rg-eventhubs-python-jan13 (eastus)
───────────────────────────────────────────────────────────────────────────────────────
Reliability Feature              Status      Resources
───────────────────────────────────────────────────────────────────────────────────────
Zone redundancy — compute        🟢 ON       • plan-ii5trxva2ark4 (FC1)              — now ON
                                             • plan-web-ii5trxva2ark4 (P1v3)         — now ON

Zone-redundant storage           🟢 ZRS      • stii5trxva2ark4                       — GRS → ZRS

Health probes                    🟡 PARTIAL  • func-api-ii5trxva2ark4                — still off (FC1, code change declined)
                                             • app-web-ii5trxva2ark4                 — now ON

Multi-region failover            🔴 OFF      • Single region (eastus) only
───────────────────────────────────────────────────────────────────────────────────────

What changed: Function App and App Service plan zone redundancy, storage replication and health probes on App Service.
(Multi-region offered next — see Step 3.)

Step 3 (both paths): Multi-region follow-up — ASK and WAIT

Multi-region is a significant cost/complexity step. Do NOT start it automatically. After re-assessment, only if all core single-region reliability features are 🟢 ON (zone-redundant compute, ZRS/GZRS storage, health probes), explicitly ask the user and wait for their response before doing anything:

🟢 Your app is now fully zone-redundant in {region}.

The next step (optional) is multi-region failover with Azure Front Door:
   • Deploys compute + storage in a second region (paired region recommended)
   • Adds Azure Front Door for global load balancing with health-probe-driven failover
   • Protects against full region outages
   • Estimated additional cost: ~2x compute (active-passive); Front Door ~$35/month base

Do you want me to set up multi-region failover now? (yes / no / later)

yes → proceed with references/configure-multi-region.md. Confirm secondary region choice with the user, then:
1. Generate the multi-region IaC (Bicep / Terraform additions for the secondary region + Front Door).
2. Confirm once with the user: 📦 Multi-region IaC generated. Ready to deploy with \azd up`. Proceed? (yes / no)`
3. On yes, the skill runs the deploy itself (azd up / az deployment group create / terraform apply) and streams output. Do not stop and tell the user to run it.
4. After successful deploy, run a final re-assessment so the user sees Multi-region failover flip to 🟢 ON.
no / later → leave the deployment as-is. Note that single-region zone-redundant is a reliable end state; multi-region can be revisited anytime.

⛔ Do not skip the wait. Do not generate multi-region IaC, deploy a Front Door, or modify any files until the user has explicitly said yes. If core reliability is not yet all 🟢, do not ask about multi-region — finish the core gaps first.

Priority Classification

Priority	Criteria	Action
Critical	No zone redundancy AND production workload	Fix immediately
High	LRS storage on zone-redundant compute	Fix within days
Medium	No multi-region (single region but zone-redundant)	Plan for next sprint
Low	Missing health probes or monitoring gaps	Track and fix

Error Handling

Error	Message	Remediation
Authentication required	"Please login"	Run `az login` and retry
Access denied	"Forbidden"	Confirm Reader/Contributor role assignment
Plan doesn't support ZR	"Upgrade required"	Inform user of plan upgrade path + cost delta
Region doesn't support AZ	"Region limitation"	Suggest supported regions

Best Practices

Run reliability assessments after every significant infrastructure change
Test failover scenarios periodically (at least quarterly)

Skill Boundaries

Action	This skill does	Hand off to
Assess reliability posture	✅ Yes	—
Recommend improvements	✅ Yes	—
Enable zone redundancy (CLI commands)	✅ Yes	—
Patch Bicep/Terraform for reliability	✅ Yes	—
Generate multi-region IaC	✅ Yes (additions for the secondary region + Front Door)	`azure-prepare` for full new-app IaC scaffolding
Deploy IaC for reliability changes	✅ Yes (runs `azd up` / `terraform apply` / `az deployment` itself, after user confirmation)	`azure-deploy` for general/non-reliability deploys
Validate pre-deployment	Reliability checks only	`azure-validate` for full validation

About this skill

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Imported

microsoft / azure-skills

skills/azure-reliability/SKILL.md

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License MITImported 6/14/2026Version v1Bundled 10 files

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microsoft/azure-reliabilityAssess and improve the reliability posture of PaaS Applications (Azure Functions and Azure App Service). Scans deployed resources for zone redundancy, ZRS storage, health probes, and multi-region failover. Presents a feature-pivoted checklist, then drives staged remediation (CLI or IaC patches) end-to-end with user confirmation. WHEN: "assess reliability", "check reliability", "zone redundant", "multi-region failover", "high availability", "disaster recovery", "single points of failure", "reliabNear-identical description+41 pts pass-rate lift

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Use this skill

Azure Reliability Assessment & Configuration

Quick Reference

When to Use This Skill

Prerequisites

MCP Tools

Assessment Workflow

Phase 1: Discover Resources

Phase 2: Assess Reliability

Phase 3: Generate Reliability Checklist

Configuration Workflow

Step 1: Present Fix Plan + Choose Path

Path A: Fix Now (CLI)

Path B: Patch IaC

Step 2 (both paths): Re-Assess

Step 3 (both paths): Multi-region follow-up — ASK and WAIT

Priority Classification

Error Handling

Best Practices

Skill Boundaries

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