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AWS DrawIO Diagram Generator · a5c-ai | DecimalAI

DevOpsby a5c-ai

AWS DrawIO Diagram Generator

Generates DrawIO XML diagrams for Amazon Web Services architectures from text descriptions or images. Analyzes existing .drawio files to extract AWS components.

Maintained

SkillScore

SkillScore64/100

Test results 64

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

41%64%of test cases passed +23 pts

Without skill9 of 22

With skill14 of 22

5 fixed9 passed either way8 still failing0 broke

Measured on gemini-3.6-flash8/29/2026v118 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskGenerate DrawIO XML for an AWS deployment spanning an AWS Cloud boundary containing an AWS Region container and an Availability Zone container.

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 a5c-ai-generating-aws-diagrams
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("a5c-ai-generating-aws-diagrams")
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 a5c-ai-generating-aws-diagrams

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/a5c-ai-generating-aws-diagrams/SKILL.md https://app.decimal.ai/s/a5c-ai-generating-aws-diagrams/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/a5c-ai-generating-aws-diagrams/SKILL.md

This gets SKILL.md only. The skill's 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/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/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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("a5c-ai-generating-aws-diagrams")
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/a5c-ai-generating-aws-diagrams/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/a5c-ai-generating-aws-diagrams/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 17 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 a5c-ai-generating-aws-diagrams

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/a5c-ai-generating-aws-diagrams/SKILL.md https://app.decimal.ai/s/a5c-ai-generating-aws-diagrams/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/a5c-ai-generating-aws-diagrams/SKILL.md

This gets SKILL.md only. The skill's 17 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/a5c-ai-generating-aws-diagrams/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/a5c-ai-generating-aws-diagrams.json

This gets SKILL.md only. The skill's 17 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/29/2026
Source: a5c-ai/babysitter ↗
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 DrawIO Diagram Generator

Generates professional DrawIO XML diagrams for Amazon Web Services architectures.

Capabilities

Extract - Analyze existing DrawIO XML files to identify AWS shapes, connections, and structure
Identify - Recognize AWS service icons from architecture diagram images
Generate - Create valid DrawIO XML from images or text descriptions
Convert - Transform AWS architecture diagrams into editable DrawIO format

Quick Reference

AWS Shape Patterns

There are two icon types. Use service icons when labeling the AWS service itself, and instance icons when representing a specific resource.

Service icon (colored square background) — for labeling the service:

shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.{shape_name}

Instance icon (bare icon, no background) — for specific resources:

shape=mxgraph.aws4.{shape_name};fillColor={category_color}

CRITICAL: Always keep fillColor set to the category color. Without it the icon renders white/invisible. The only difference from a service icon is the absence of the resourceIcon background square — never remove fillColor when using an instance icon.

Scenario	Icon Type	Label Example
The AWS service itself	Service (with background)	"AWS Lambda", "Amazon RDS"
A specific resource/instance	Instance (no background)	"Order Processor", "Users Table"
Multiple instances of same service	Instance (no background)	"ECS Task 1", "ECS Task 2"

CRITICAL: Underscore-Separated Names

Multi-word AWS shape names use UNDERSCORES in style properties!

step_functions (correct)
api_gateway (correct)
kinesis_data_streams (correct)
step functions (WRONG - will not render)
api gateway (WRONG - will not render)

The aws4.xml stencil defines names with spaces, but DrawIO's style parser requires underscores.

Always look up exact names in assets/aws-icons.json before using.

Common AWS Services

Service	Service Icon (with background)	Instance Icon (no background)
Lambda	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.lambda`	`shape=mxgraph.aws4.lambda`
Amazon S3	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.s3`	`shape=mxgraph.aws4.s3`
Amazon EC2	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.ec2`	`shape=mxgraph.aws4.ec2`
DynamoDB	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.dynamodb`	`shape=mxgraph.aws4.dynamodb`
API Gateway	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.api_gateway`	`shape=mxgraph.aws4.api_gateway`
Amazon SQS	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.sqs`	`shape=mxgraph.aws4.sqs`
Amazon SNS	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.sns`	`shape=mxgraph.aws4.sns`
Step Functions	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.step_functions`	`shape=mxgraph.aws4.step_functions`
Amazon ECS	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.ecs`	`shape=mxgraph.aws4.ecs`
Amazon RDS	`shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.rds`	`shape=mxgraph.aws4.rds`

AWS Container Types

Container	Use Case
aws_cloud	Top-level AWS Cloud boundary
aws_region	Region grouping
aws_availability_zone	AZ grouping
aws_vpc	VPC boundary
aws_public_subnet	Public subnet
aws_private_subnet	Private subnet
aws_security_group	Security group boundary
aws_auto_scaling_group	Auto scaling group
aws_account	AWS Account boundary
aws_corporate_datacenter	Corporate datacenter
aws_generic_group	Generic grouping
aws_step_functions_workflow	Step Functions workflow
aws_elastic_beanstalk_container	Elastic Beanstalk app

Task 1: Analyze a DrawIO File

Use this workflow to extract and document all components from an existing DrawIO file.

Steps

Read the file - Load the .drawio XML file
Parse structure - Extract all <mxCell> elements
Identify shapes - Find cells with vertex="1"
Identify connections - Find cells with edge="1"
Extract styles - Parse style strings for each element
Map hierarchy - Build container/child relationships using parent attribute
Generate report - Output findings in structured format

Input

Path to .drawio file

Output

Generate a Markdown report with:

# DrawIO Analysis Report

## Summary
- Total shapes: X
- Total connections: Y
- Containers: Z

## Shape Inventory

| ID | Label | Type | Position | Parent |
|----|-------|------|----------|--------|
| abc | Lambda | mxgraph.aws4.lambda | (100,200) | vpc1 |

## Connection Matrix

| From | To | Label | Type |
|------|-----|-------|------|
| Lambda | DynamoDB | API | solid |

## Container Hierarchy

- VPC (vpc1)
  - Lambda (lambda1)
  - Lambda (lambda2)
  - DynamoDB (db1)

## Style Analysis

### Unique Shapes Found
- mxgraph.aws4.lambda (4 instances)
- mxgraph.aws4.dynamodb (2 instances)

Task 2: Convert Image to DrawIO

Use this workflow to recreate an AWS architecture diagram from an image.

Steps

Analyze image - Identify all visual elements:
- AWS service icons (shape, color, label)
- Containers/boundaries (color, border style)
- Connections (solid, dashed, arrows)
- Labels and text
Map to library - For each identified element:
- Look up in assets/aws-icons.json by visual signature or label
- Match containers to assets/aws-containers.json
- CRITICAL: Verify underscore-separated names for multi-word services (e.g., step_functions, not step functions)
- Note any unrecognized elements
Estimate layout - Determine positions:
- Identify container boundaries first
- Place icons within containers
- Estimate x,y coordinates and dimensions
- Standard icon size: 64x64 pixels
Generate XML - Build the DrawIO structure:
- Start with base template from assets/templates/drawio-base.xml
- Add containers first (they become parents)
- Add service icons with correct parent references
- Add connections between shapes
Create confidence report - Document accuracy:
- List all identified components
- Note any uncertain matches
- Flag potential issues

Input

AWS architecture diagram image (PNG/JPG)

Output

Valid .drawio XML file
Confidence report (Markdown)

Confidence Report Format

# Conversion Confidence Report

## Overall Confidence: 85%

## Identified Components

### High Confidence (>90%)
- Lambda x4 - Clear icon match
- DynamoDB x2 - Clear icon match
- VPC container - Purple border, correct label

### Medium Confidence (70-90%)
- Step Functions - Icon similar, label confirms (using `step_functions` with underscores)

### Low Confidence (<70%)
- Unknown icon at position (300, 400) - Mapped to generic service

## Connection Accuracy
- 12/14 connections clearly visible
- 2 connections inferred from layout

## Notes
- VPC subnet grouping identified
- Bidirectional arrows on 3 connections

Task 3: Create DrawIO from Description

Use this workflow to generate a new AWS diagram from text specifications.

Steps

Parse requirements - Extract from description:
- Required AWS services
- Container/grouping needs
- Connection requirements
- Layout preferences
Select components - From libraries:
- Look up services in assets/aws-icons.json
- Choose containers from assets/aws-containers.json
- Choose icon type: Use service icons (with background) for generic service labels, instance icons (no background) for specific resources (see Quick Reference)
- VERIFY: Underscore-separated names for multi-word services in style strings
- Select connection styles
Plan layout - Design the arrangement:
- Determine canvas size
- Position containers first
- Arrange services logically (left-to-right data flow, top-to-bottom hierarchy)
- Standard spacing: 120px between 64x64 icons
Generate XML - Build the diagram:
- Use assets/templates/drawio-base.xml as starting point
- Add elements in order: containers, services, connections
- Assign unique IDs to all elements
Validate - Check the output:
- All requested components present
- Connections reference valid IDs
- Layout is logical and readable

Input

Text description of desired AWS architecture

Output

Valid .drawio XML file

Example Input

Create an AWS serverless architecture with:
- VPC container
- API Gateway triggering Lambda
- Lambda connecting to DynamoDB and S3
- Step Functions orchestrating the workflow

Example Output Structure

<mxfile ...>
  <diagram name="AWS Architecture">
    <mxGraphModel ...>
      <root>
        <mxCell id="0" />
        <mxCell id="1" parent="0" />
        <!-- VPC Container -->
        <mxCell id="vpc" value="VPC" style="..." vertex="1" parent="1">
          <mxGeometry x="50" y="50" width="700" height="400" />
        </mxCell>
        <!-- API Gateway -->
        <mxCell id="api_gateway" value="API Gateway" style="...resIcon=mxgraph.aws4.api_gateway" vertex="1" parent="vpc">
          <mxGeometry x="50" y="100" width="64" height="64" />
        </mxCell>
        <!-- More shapes... -->
        <!-- Connections -->
        <mxCell id="conn_api_gateway_to_lambda" edge="1" source="api_gateway" target="lambda" style="..." />
      </root>
    </mxGraphModel>
  </diagram>
</mxfile>

Shape Library Reference

Looking Up an AWS Service

Open assets/aws-icons.json
Search by service_name or recognition_keywords
Use the drawio_shape.full_style for complete styling
Or construct style using: shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.{shape_name}

Service Coverage:

264 AWS services across 24 categories
96.6% validated against mxgraph.aws4 library (255/264 services)
9 unvalidated services (deprecated or newer than aws4.xml stencil) — see references/ICON-COMPATIBILITY.md for the list. For these, use a visually similar validated service icon or a generic shape.

Key AWS Specifications

Icon size: 64x64 pixels
Font color: #232F3E universally (dark gray)
Fill color varies by category (not per-service)
Arrow style: open arrow, 2pt stroke, #232F3E

Look up the exact category, fillColor, and full_style for any service in assets/aws-icons.json. All 24 categories with colors are listed there.

Visual Best Practices

See references/DIAGRAM-BEST-PRACTICES.md for full guidelines. Key rules:

Connection labels — always add: labelBackgroundColor=#FFFFFF;fontSize=10;fontColor=#232F3E;
strokeWidth — use 1 for most connections, 2 only for primary data paths (max 1–3 per diagram)
Consolidate connections when 3+ cross the same corridor into a single labeled edge

XML Structure Quick Reference

For complete XML examples and detailed structure, see references/xml-examples.md.

The key building blocks:

Shape: <mxCell id="api_gateway" value="Label" style="..." vertex="1" parent="1"> with <mxGeometry>
Connection: <mxCell id="conn_api_gateway_to_lambda" edge="1" source="api_gateway" target="lambda" style="...">
Container: Shape with container=1 in style; children set parent to container ID
Root cells: Every diagram needs <mxCell id="0"/> and <mxCell id="1" parent="0"/>

ID Naming Rules

Full service names — api_gateway, not apigw; step_functions, not stepfn
Role suffixes for duplicates — lambda_api_handler, lambda_processor, not lambda1, lambda2
Connection IDs — conn_{source}_to_{target} using full IDs

See references/DIAGRAM-BEST-PRACTICES.md for complete ID naming convention.

For XML parsing and extraction techniques, see references/xml-parser-guide.md.

Troubleshooting

Icon Not Displaying

CRITICAL: Verify multi-word shape names use UNDERSCORES: step_functions, NOT step functions
For service icons: verify both shape=mxgraph.aws4.resourceIcon AND resIcon=mxgraph.aws4.{name} are present
For instance icons: verify shape=mxgraph.aws4.{name} is used directly (no resourceIcon wrapper)
NEVER use productIcon/prIcon — always use resourceIcon/resIcon for service icons
Check assets/aws-icons.json for exact shape name
Ensure vertex="1" is present
Check that <mxGeometry> has valid width/height (64x64)

Wrong Icon Type (Background vs No Background)

See the Quick Reference section for the full definition of each icon type. In summary:

Service icon: shape=mxgraph.aws4.resourceIcon;resIcon=mxgraph.aws4.{name} — labels the AWS service itself
Instance icon: shape=mxgraph.aws4.{name};fillColor={category_color} — represents a specific resource; fillColor is required or the icon renders white

Connection Not Rendering

Verify source and target IDs exist
Ensure edge="1" is set
Check that source/target shapes are vertices

Shapes Not Inside Container

Set child's parent attribute to container's ID
Ensure container has container=1 in style
Position child coordinates relative to container (not absolute)

Label Not Showing

Check value attribute is set
Verify fontSize is reasonable (12-14 for AWS)
Ensure fontColor=#232F3E is set

File Won't Open in DrawIO

Validate XML structure (proper closing tags)
Ensure id="0" and id="1" root cells exist
Check for special characters in labels (use 
 for newlines)

Desktop Integration

After generating a .drawio file, you can validate and preview it:

Validate: python scripts/validate-drawio.py output.drawio --verbose
Analyze: python scripts/analyze-existing.py output.drawio --markdown
Validate Icons: python scripts/validate-aws-icons.py
Export PNG: ./scripts/export-diagram.sh output.drawio png
Open in DrawIO: ./scripts/open-diagram.sh output.drawio

Requires DrawIO Desktop. Install on macOS: brew install drawio

Files in This Skill

File	Purpose
`SKILL.md`	This file - main instructions
Assets
`assets/aws-icons.json`	AWS service icon database (264 services)
`assets/aws-containers.json`	AWS container/group and connection styles
`assets/templates/drawio-base.xml`	Base XML template
`assets/templates/node-template.xml`	Shape insertion template
`assets/templates/connection-template.xml`	Connection template
References
`references/ICON-COMPATIBILITY.md`	Icon validation reference (255/264 validated)
`references/DIAGRAM-BEST-PRACTICES.md`	Visual design and layout guidelines
`references/xml-parser-guide.md`	Detailed XML parsing reference
`references/xml-examples.md`	Copy-paste XML examples
`references/coordinate-system.md`	Positioning and layout guide
`references/style-guide.md`	Style string reference
Scripts
`scripts/validate-drawio.py`	Validate .drawio XML structure
`scripts/validate-aws-icons.py`	Validate AWS icon compatibility
`scripts/fix-aws-icons.py`	Auto-fix icon shape names
`scripts/fix-drawio-icons.py`	Bulk fix icon references in .drawio files
`scripts/extract-shape-names.py`	Extract available shapes from DrawIO stencil
`scripts/analyze-existing.py`	Extract shapes/connections from .drawio files
`scripts/export-diagram.sh`	Export to PNG/PDF via DrawIO Desktop CLI
`scripts/open-diagram.sh`	Open .drawio file in DrawIO Desktop

When it activates

Automatically, when a task matches. Use for AWS architecture diagrams, cloud infrastructure documentation, or when converting AWS diagram images to editable DrawIO format.

About this skill

Automatic

DevOps & SRE pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

a5c-ai / babysitter

library/specializations/devops-sre-platform/skills/generating-aws-diagrams/SKILL.md

View on GitHub ↗

License MITImported 8/10/2026Version v1Bundled 17 files · scripts

Illustrative Want to see what a5c-ai/generating-aws-diagrams would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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What it changes

Track record

Use this skill

AWS DrawIO Diagram Generator

Capabilities

Quick Reference

AWS Shape Patterns

CRITICAL: Underscore-Separated Names

Common AWS Services

AWS Container Types

Task 1: Analyze a DrawIO File

Steps

Input

Output

Task 2: Convert Image to DrawIO

Steps

Input

Output

Confidence Report Format

Task 3: Create DrawIO from Description

Steps

Input

Output

Example Input

Example Output Structure

Shape Library Reference

Looking Up an AWS Service

Key AWS Specifications

Visual Best Practices

XML Structure Quick Reference

ID Naming Rules

Troubleshooting

Icon Not Displaying

Wrong Icon Type (Background vs No Background)

Connection Not Rendering

Shapes Not Inside Container

Label Not Showing

File Won't Open in DrawIO

Desktop Integration

Files in This Skill

When it activates

SkillSafety

Source

Other skills for the same job

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