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AWS CloudFormation CloudWatch Monitoring · giuseppe-trisciuoglio | DecimalAI

DevOpsby giuseppe-trisciuoglio

AWS CloudFormation CloudWatch Monitoring

Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability.

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

82%86%of test cases passed +5 pts

Without skill18 of 22

With skill19 of 22

2 fixed17 passed either way2 still failing1 broke

Measured on gemini-3.6-flash8/21/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAn automated generator produces a CloudWatch dashboard JSON snippet that is 700KB in size for an AWS::CloudWatch::Dashboard resource. Will CloudFormation accept this? State the limit.

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 giuseppe-trisciuoglio-aws-cloudformation-cloudwatch
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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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 giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md https://app.decimal.ai/s/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

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

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

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

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

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

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

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

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

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

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

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

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

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

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

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

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

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

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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("giuseppe-trisciuoglio-aws-cloudformation-cloudwatch")
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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

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

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add giuseppe-trisciuoglio-aws-cloudformation-cloudwatch

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/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md https://app.decimal.ai/s/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

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

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/giuseppe-trisciuoglio-aws-cloudformation-cloudwatch.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/21/2026
Source: giuseppe-trisciuoglio/developer-kit ↗
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

AWS CloudFormation CloudWatch Monitoring

Overview

Creates CloudWatch monitoring infrastructure using CloudFormation templates: metrics, alarms, dashboards, log groups, anomaly detection, synthesized canaries, and Application Signals.

When to Use

Creating CloudWatch metrics and alarms for production infrastructure
Building CloudWatch dashboards for multi-region visualization
Implementing log groups with retention, encryption, and metric filters
Configuring anomaly detection and composite alarms
Setting up cross-stack references with Parameters and Outputs
Validating and deploying monitoring stacks with CloudFormation

Instructions

Follow these steps to create CloudWatch monitoring infrastructure with CloudFormation:

1. Define Alarm Parameters

Specify metric namespaces, dimensions, and threshold values:

Parameters:
  ErrorRateThreshold:
    Type: Number
    Default: 5
    Description: Error rate threshold for alarms (percentage)

  LatencyThreshold:
    Type: Number
    Default: 1000
    Description: Latency threshold in milliseconds

  CpuUtilizationThreshold:
    Type: Number
    Default: 80
    Description: CPU utilization threshold (percentage)

  LogRetentionDays:
    Type: Number
    Default: 30
    AllowedValues:
      - 1
      - 3
      - 7
      - 14
      - 30
      - 60
      - 90
      - 120
      - 365
    Description: Number of days to retain log events

2. Create CloudWatch Alarms

Set up alarms for CPU, memory, disk, and custom metrics:

Resources:
  HighCpuAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: !Sub "${AWS::StackName}-high-cpu"
      AlarmDescription: Trigger when CPU utilization exceeds threshold
      MetricName: CPUUtilization
      Namespace: AWS/EC2
      Dimensions:
        - Name: InstanceId
          Value: !Ref InstanceId
      Statistic: Average
      Period: 60
      EvaluationPeriods: 3
      Threshold: !Ref CpuUtilizationThreshold
      ComparisonOperator: GreaterThanThreshold
      AlarmActions:
        - !Ref AlarmTopic

  ErrorRateAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: !Sub "${AWS::StackName}-error-rate"
      MetricName: ErrorRate
      Namespace: !Ref CustomNamespace
      Dimensions:
        - Name: Service
          Value: !Ref ServiceName
      Statistic: Average
      Period: 60
      EvaluationPeriods: 5
      Threshold: !Ref ErrorRateThreshold
      ComparisonOperator: GreaterThanThreshold

3. Configure Alarm Actions

Define SNS topics for notification delivery:

Resources:
  AlarmNotificationTopic:
    Type: AWS::SNS::Topic
    Properties:
      DisplayName: !Sub "${AWS::StackName}-alarms"
      TopicName: !Sub "${AWS::StackName}-alarms"

  AlarmTopicPolicy:
    Type: AWS::SNS::TopicPolicy
    Properties:
      PolicyDocument:
        Statement:
          - Effect: Allow
            Principal:
              Service: cloudwatch.amazonaws.com
            Action: sns:Publish
            Resource: !Ref AlarmNotificationTopic
      Topics:
        - !Ref AlarmNotificationTopic

4. Create Dashboards

Build visualization widgets for metrics across resources:

Resources:
  MonitoringDashboard:
    Type: AWS::CloudWatch::Dashboard
    Properties:
      DashboardName: !Sub "${AWS::StackName}-dashboard"
      DashboardBody: !Sub |
        {
          "widgets": [
            {
              "type": "metric",
              "x": 0,
              "y": 0,
              "width": 12,
              "height": 6,
              "properties": {
                "title": "CPU Utilization",
                "metrics": [["AWS/EC2", "CPUUtilization", "InstanceId", "${InstanceId}"]],
                "period": 300,
                "stat": "Average",
                "region": "${AWS::Region}"
              }
            }
          ]
        }

5. Set Up Log Groups

Configure retention policies and encryption settings:

Resources:
  ApplicationLogGroup:
    Type: AWS::Logs::LogGroup
    Properties:
      LogGroupName: !Sub "/aws/applications/${Environment}/${ApplicationName}"
      RetentionInDays: !Ref LogRetentionDays
      KmsKeyId: !Ref LogEncryptionKey

6. Implement Metric Filters

Create metrics from log data:

Resources:
  ErrorMetricFilter:
    Type: AWS::Logs::MetricFilter
    Properties:
      LogGroupName: !Ref ApplicationLogGroup
      FilterPattern: '[level="ERROR", msg]'
      MetricTransformations:
        - MetricValue: "1"
          MetricNamespace: !Sub "${AWS::StackName}/Application"
          MetricName: ErrorCount

7. Add Composite Alarms

Build multi-condition alarm logic:

Resources:
  SystemHealthComposite:
    Type: AWS::CloudWatch::CompositeAlarm
    Properties:
      AlarmName: !Sub "${AWS::StackName}-system-health"
      AlarmRule: !Or
        - !Ref HighCpuAlarm
        - !Ref ErrorRateAlarm
      AlarmActions:
        - !Ref AlarmTopic

8. Configure Log Insights Queries

Create saved queries for log analysis:

Resources:
  ErrorAnalysisQuery:
    Type: AWS::Logs::QueryDefinition
    Properties:
      Name: !Sub "${AWS::StackName}-errors"
      LogGroupNames:
        - !Ref ApplicationLogGroup
      QueryString: |
        fields @timestamp, @message
        | filter @message like /ERROR/
        | sort @timestamp desc
        | limit 100

9. Validate Template

Before deploying, validate the CloudFormation template:

aws cloudformation validate-template --template-body file://template.yaml

For parameterized templates, test with sample values:

aws cloudformation validate-template \
  --template-body file://monitoring.yaml \
  --capabilities CAPABILITY_IAM

10. Deploy and Verify

Deploy the stack and verify resources:

# Deploy stack
aws cloudformation create-stack \
  --stack-name my-monitoring-stack \
  --template-body file://monitoring.yaml \
  --parameters file://parameters.json \
  --capabilities CAPABILITY_IAM

# Wait for completion
aws cloudformation wait stack-create-complete \
  --stack-name my-monitoring-stack

# Verify alarms are in OK state
aws cloudwatch describe-alarms --stack-name my-monitoring-stack

# Check dashboard accessibility
aws cloudwatch get-dashboard --dashboard-name my-monitoring-stack-dashboard

Test alarm actions before production:

# Set alarm to testing state
aws cloudwatch set-alarm-state \
  --alarm-name my-alarm \
  --state-value ALARM \
  --state-reason "Testing alarm action"

Best Practices

Use composite alarms to reduce noise and avoid false positives
Set meaningful thresholds based on baseline metrics
Configure appropriate evaluation periods (3-5 datapoints)
Enable anomaly detection for metrics with variable patterns
Use metric math for derived metrics (error rates, latency percentiles)
Set appropriate log retention based on compliance needs
Encrypt log groups with sensitive data using KMS
Test alarm actions with set-alarm-state before production

Examples

Example 1: Complete Monitoring Stack

AWSTemplateFormatVersion: '2010-09-09'
Description: Complete CloudWatch monitoring setup

Parameters:
  Environment:
    Type: String
    Default: prod
    AllowedValues: [dev, staging, prod]

Resources:
  # SNS Topic for notifications
  AlarmTopic:
    Type: AWS::SNS::Topic
    Properties:
      DisplayName: !Sub "${Environment}-alarms"

  # Alarm for high CPU
  CpuAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: !Sub "${AWS::StackName}-cpu-high"
      MetricName: CPUUtilization
      Namespace: AWS/EC2
      Statistic: Average
      Period: 300
      EvaluationPeriods: 2
      Threshold: 80
      ComparisonOperator: GreaterThanThreshold
      AlarmActions:
        - !Ref AlarmTopic

  # Dashboard
  Dashboard:
    Type: AWS::CloudWatch::Dashboard
    Properties:
      DashboardName: !Ref AWS::StackName
      DashboardBody: !Sub |
        {
          "widgets": [{
            "type": "metric",
            "properties": {
              "metrics": [["AWS/EC2", "CPUUtilization"]],
              "period": 300,
              "stat": "Average"
            }
          }]
        }

Example 2: Log-Based Metrics with Filters

Resources:
  AppLogGroup:
    Type: AWS::Logs::LogGroup
    Properties:
      LogGroupName: !Sub "/app/${Environment}"
      RetentionInDays: 30

  ErrorMetricFilter:
    Type: AWS::Logs::MetricFilter
    Properties:
      LogGroupName: !Ref AppLogGroup
      FilterPattern: '"ERROR"'
      MetricTransformations:
        - MetricValue: "1"
          MetricNamespace: !Sub "${AWS::StackName}/App"
          MetricName: ErrorCount

  ErrorAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: !Sub "${AWS::StackName}-errors"
      MetricName: ErrorCount
      MetricNamespace: !Sub "${AWS::StackName}/App"
      Statistic: Sum
      Period: 300
      EvaluationPeriods: 1
      Threshold: 1
      ComparisonOperator: GreaterThanOrEqualToThreshold

Constraints and Warnings

Resource Limits

Maximum 5000 alarms per account
Maximum 50 metric filters per log group
Dashboard body limited to 512KB
Maximum 200 metrics per alarm expression

Security Considerations

Enable encryption on log groups containing sensitive data
Use IAM policies to restrict alarm action permissions
Avoid hardcoding thresholds in templates; use Parameters
Protect SNS topics with encryption and access policies

Operational Warnings

High-resolution alarms (10s/30s) incur higher costs
Missing data treatment affects alarm state; configure explicitly
Composite alarms can mask individual alarm states
Metric filters are immutable after creation; recreate to change

Cost Optimization

Use standard resolution (5-minute period) unless necessary
Set appropriate retention periods (shorter = cheaper)
Delete unused dashboards and custom metrics
Monitor CloudWatch charges with budgets and alerts

Monitoring Strategy

Use composite alarms to reduce alarm noise
Configure appropriate evaluation periods to avoid false positives
Set up anomaly detection for metrics with variable patterns
Use metric math for derived metrics (error rates, averages)
Implement high-resolution alarms for critical metrics
Create separate dashboards for different audiences (ops, dev, management)

Log Management

Use appropriate retention periods based on compliance requirements
Encrypt log groups containing sensitive data
Implement metric filters for critical log patterns
Set up cross-account log aggregation for centralized analysis
Use CloudWatch Logs Insights for troubleshooting
Configure log subscriptions to Lambda/Kinesis for real-time processing

Alarm Configuration

Set meaningful thresholds based on baseline metrics
Use datapoints-to-alarm for reliability
Configure OK actions to reset notifications
Treat missing data appropriately (breaching, not breaching, ignore)
Test alarm actions regularly
Document alarm runbooks and escalation procedures

Template Structure

Use AWS-specific parameter types for resources
Implement parameter constraints for validation
Use Conditions for environment-specific configuration
Leverage Mappings for region-specific settings
Apply Metadata for parameter grouping
Use nested stacks for large monitoring setups

Dashboard Design

Organize dashboards by service or application tier
Use consistent widget layouts and sizing
Include text widgets for context and documentation
Set appropriate time ranges for data visualization
Use variables for dynamic dashboard filtering
Limit metrics per dashboard to avoid performance issues

References

For detailed implementation guidance, see:

alarms.md - CloudWatch metrics and alarms including base metric alarms, latency alarms, API Gateway errors, EC2 instance alarms, Lambda function alarms, composite alarms, anomaly detection, metric math, alarm actions (SNS, Auto Scaling, EC2), missing data treatment, custom metrics, metric filters, and high-resolution alarms
dashboards.md - CloudWatch dashboards including base template, service-specific dashboards, widget types (metric, log, text, single value, alarm status), multi-region dashboards, stacked metrics, anomaly detection widgets, math expressions, layout patterns (grid, row, column), dynamic variables, cross-account sharing, and dashboard automation
logs.md - CloudWatch logs including log group configurations, metric filters, subscription filters (Lambda, Kinesis Firehose), cross-account log aggregation, log insights queries, resource policies, export and archive tasks, CloudWatch agent configuration, log encryption with KMS, lifecycle management, centralized logging, and search patterns
constraints.md - Resource limits (5000 alarms max, 500 dashboards max), operational constraints (metric resolution, evaluation periods, dashboard widgets, cross-account), security constraints (log data access, encryption, metric filters, alarm actions), cost considerations (detailed monitoring, custom metrics, log retention, dashboard queries), and data constraints (metric age, log ingestion, filter limits)

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When it activates

Automatically, when a task matches. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.

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giuseppe-trisciuoglio / developer-kit

plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-cloudwatch/SKILL.md

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License MITImported 8/3/2026Version v1Bundled 6 files

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AWS CloudFormation CloudWatch Monitoring

Overview

When to Use

Instructions

1. Define Alarm Parameters

2. Create CloudWatch Alarms

3. Configure Alarm Actions

4. Create Dashboards

5. Set Up Log Groups

6. Implement Metric Filters

7. Add Composite Alarms

8. Configure Log Insights Queries

9. Validate Template

10. Deploy and Verify

Best Practices

Examples

Example 1: Complete Monitoring Stack

Example 2: Log-Based Metrics with Filters

Constraints and Warnings

Resource Limits

Security Considerations

Operational Warnings

Cost Optimization

Monitoring Strategy

Log Management

Alarm Configuration

Template Structure

Dashboard Design

References

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