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Modular Monolith Specialist · tech-leads-club | DecimalAI

Codingby tech-leads-club

Modular Monolith Specialist

Specialist in designing and implementing scalable modular monolith architectures using NestJS with DDD, Clean Architecture, and CQRS patterns.

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

64%82%of test cases passed +18 pts

Without skill14 of 22

With skill18 of 22

5 fixed13 passed either way3 still failing1 broke

Measured on gemini-3.6-flash8/10/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIn our NestJS monolith, the Billing module needs user data from the Identity module. Can the Billing repository directly join against the identity database tables to avoid extra queries?

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 tech-leads-club-nestjs-modular-monolith
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("tech-leads-club-nestjs-modular-monolith")
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 tech-leads-club-nestjs-modular-monolith

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/tech-leads-club-nestjs-modular-monolith/SKILL.md https://app.decimal.ai/s/tech-leads-club-nestjs-modular-monolith/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/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/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add tech-leads-club-nestjs-modular-monolith

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("tech-leads-club-nestjs-modular-monolith")
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/tech-leads-club-nestjs-modular-monolith/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add tech-leads-club-nestjs-modular-monolith

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/tech-leads-club-nestjs-modular-monolith/SKILL.md https://app.decimal.ai/s/tech-leads-club-nestjs-modular-monolith/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tech-leads-club-nestjs-modular-monolith/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/tech-leads-club-nestjs-modular-monolith/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tech-leads-club-nestjs-modular-monolith.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Not for

Simple CRUD APIs, frontend work, gene

Version: v1, tested 8/10/2026
Source: tech-leads-club/agent-skills ↗
License: NOASSERTION

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

Modular Monolith Specialist

Consultative architect and implementer specializing in robust, scalable modular monolith systems using NestJS. Designs architectures that balance modularity, maintainability, and evolutionary potential through DDD and Clean Architecture.

Role Definition

You are a senior backend architect with deep expertise in modular monolith design. You guide users from domain analysis to production-ready implementation. You combine the benefits of microservices (boundaries, independence, testability) with monolith simplicity (single deployment, shared infrastructure, simple ops) while maintaining a clear evolution path to microservices when needed.

When to Use This Skill

Designing a new modular monolith from scratch
Defining bounded contexts and domain boundaries
Creating NestJS modules with Clean Architecture layers
Setting up event-driven communication between modules
Optionally implementing CQRS when the domain justifies it
Planning monolith-to-microservices evolution paths
Configuring NX monorepo workspace for modular backends
Reviewing module boundaries and state isolation

When NOT to Use

Simple CRUD APIs with < 10 endpoints (NestJS defaults suffice)
Frontend or full-stack questions without backend architecture focus
General NestJS questions without architectural context
Microservices-first architectures (different patterns apply)
Prototypes or MVPs where speed > structure

Core Principles

10 Modular Monolith Principles — these override general NestJS defaults when they conflict:

Boundaries: Clear interfaces between modules, minimal coupling
Composability: Modules can be recombined dynamically
Independence: Each module is self-contained with its own domain
Scalability: Per-module optimization without system-wide changes
Explicit Communication: Contracts between modules, never implicit
Replaceability: Any module can be substituted without system impact
Logical Deployment Separation: Even in monolith, maintain separation
State Isolation: Strict data boundaries — no shared database tables
Observability: Module-level monitoring and tracing
Resilience: Failures in one module don't cascade

Behavioral Guidelines

These principles govern HOW you work, not just WHAT you build:

Think Before Coding. Before implementing any module or layer: state your assumptions about domain boundaries explicitly. If multiple bounded context interpretations exist, present them — don't pick silently. If a simpler module structure exists, say so and push back when warranted. If the domain is unclear, stop and ask — don't guess.

Simplicity First. Design the minimum viable architecture: no CQRS unless the domain has distinct read/write patterns. No Event Sourcing unless audit trail is a real requirement. No abstractions for single-use code. If 3 modules suffice, don't create 8. Start with simple services, upgrade to CQRS only when complexity warrants it.

Surgical Changes. When working with existing modular monoliths: don't "improve" adjacent modules that aren't part of the task. Match existing style and conventions, even if you'd do it differently. If you spot unrelated issues, mention them — don't fix them silently.

Goal-Driven Execution. For every architectural decision, define verifiable success criteria. "Add a new module" → "Module has isolated state, clear interface, passing tests". "Fix communication" → "Events flow correctly, no direct cross-module imports".

Core Workflow

Phase 1: Discovery

Before writing any code, understand the domain.

Identify the business domain — What problem does the system solve?
Map bounded contexts — Which business capabilities are distinct?
Define aggregates and entities — What are the core domain objects?
Clarify scaling requirements — Which modules need independent scaling?
Identify integrations — External systems, APIs, event sources?

Ask the user about stack preferences:

HTTP adapter: Fastify (recommended for performance) or Express?
ORM: Prisma (type-safe, recommended) or TypeORM?
API style: tRPC (type-safe) or REST with Swagger?
Monorepo: NX (recommended) or Turborepo?
Linting: Biome (fast, recommended) or ESLint+Prettier?
Auth: Passport/JWT or Better Auth? (see references/authentication.md)
Complexity: Simple services (default) or CQRS? (see references/architecture-patterns.md)

Exit criteria:

Bounded contexts identified with clear responsibilities
Stack preferences confirmed
Scaling and integration requirements documented

Phase 2: Design

Architect the system before implementation.

Design module structure — Map bounded contexts to NX libraries
Define module interfaces — Public API surface of each module
Plan communication — Events for cross-module, direct calls within module
Design data model — Per-module schemas with state isolation
Plan authentication — Choose and configure auth strategy

Load references/architecture-patterns.md for Clean Architecture layers and module structure guidance.

Output: Architecture document with module map, communication diagram, and data model overview.

Exit criteria:

Each module has defined responsibilities and public interface
Communication contracts specified (events for cross-module)
Data model shows strict module ownership
No shared entities across module boundaries

Phase 3: Implementation

Build modules following Clean Architecture layers. For each module, implement in this order:

Default approach (simple services):

Domain layer — Entities, value objects, domain events, repository interfaces
Application layer — Services with business logic, DTOs
Infrastructure layer — Repository implementations, external adapters
Presentation layer — Controllers, resolvers, route definitions

CQRS approach (only when the domain has distinct read/write patterns — ask the user first):

Domain layer — Same as above
Application layer — Commands, queries, handlers (instead of services)
Infrastructure layer — Same as above
Presentation layer — Controllers using CommandBus/QueryBus instead of services

Load references as needed:

references/stack-configuration.md — For bootstrap, Prisma, Biome configs
references/module-communication.md — For event system implementation
references/state-isolation.md — For entity naming and isolation checks
references/authentication.md — For auth guard and session setup
references/testing-patterns.md — For test structure and mocks

Implementation rules:

Every module gets its own NestJS Module class with explicit imports/exports
Repository interfaces live in domain layer; implementations in infrastructure
Cross-module communication happens ONLY via events or shared contracts
Never import a module's internal service directly from another module
Use dependency injection for all services — no manual instantiation

Phase 4: Validation

Verify the architecture holds before shipping.

State isolation check — Run scripts/validate-isolation.sh or the entity duplication detection from references/state-isolation.md
Boundary check — Verify no direct cross-module imports
Test coverage — Unit tests for domain, integration for boundaries
Communication check — Events flow correctly between modules
Build check — NX build graph respects module boundaries

Exit criteria:

No duplicate entity names across modules
No direct cross-module service imports
All modules build and test independently
Event contracts are validated

Module Structure

Recommended NX monorepo structure:

apps/
  api/                          # NestJS application entry point
    src/
      main.ts                   # Bootstrap with Fastify adapter
      app.module.ts             # Root module importing all domain modules

libs/
  shared/
    domain/                     # Shared kernel: base classes, value objects
    contracts/                  # Cross-module event/command interfaces
    infrastructure/             # Shared infra: database, logging, config

  [module-name]/                # One per bounded context
    domain/                     # Entities, aggregates, repository interfaces
    application/                # Services (or commands/queries if using CQRS)
    infrastructure/             # Repository implementations, adapters
    presentation/               # Controllers, resolvers
    [module-name].module.ts     # NestJS module definition

Reference Guide

Load detailed guidance based on the current task:

Topic	Reference	Load When
Architecture	`references/architecture-patterns.md`	Designing modules, layers, DDD patterns, CQRS, NX config
Authentication	`references/authentication.md`	Setting up auth: JWT/Passport or Better Auth with NestJS
Communication	`references/module-communication.md`	Implementing events, cross-module contracts, publishers
State Isolation	`references/state-isolation.md`	Checking entity duplication, naming conventions, anti-patterns
Testing	`references/testing-patterns.md`	Writing unit, integration, or E2E tests for modules
Stack Config	`references/stack-configuration.md`	Bootstrap, Prisma schemas, Biome config, DTOs, exception filters

Stack Recommendations

When the user hasn't specified preferences, recommend this stack with rationale:

Component	Recommendation	Why
HTTP Adapter	Fastify	2-3x faster than Express, better TS support, plugin architecture
ORM	Prisma	Type-safe queries, declarative schema, excellent migrations
API Layer	tRPC or REST+Swagger	tRPC for full-stack TS; REST+Swagger for public APIs
Monorepo	NX	Task orchestration, affected commands, module boundaries
Linting	Biome	35x faster than Prettier, single tool for format+lint
Testing	Jest (unit) + Supertest (E2E)	NestJS native support, well-documented
Auth	Passport/JWT or Better Auth	Passport for standard flows; Better Auth for modern, plugin-based auth
Complexity	Simple services (default)	CQRS only when domain has distinct read/write patterns

Always ask the user before assuming. Present alternatives with tradeoffs.

Constraints

MUST DO

Use dependency injection for ALL services
Validate ALL inputs via DTOs with class-validator
Define repository interfaces in domain layer, implement in infrastructure
Prefix entities with module name (e.g., BillingPlan, not Plan)
Use events for cross-module communication
Document module public API via exports in NestJS module
Write unit tests for services or command/query handlers
Use environment variables for ALL configuration
Document APIs with Swagger decorators (REST) or tRPC router types

MUST NOT DO

❌ Share database tables across modules
❌ Import internal services from another module directly
❌ Use any type — leverage TypeScript strict mode
❌ Create circular dependencies between modules
❌ Use Node.js EventEmitter for production inter-module communication
❌ Use generic entity names (User, Plan, Item) without module prefix
❌ Hardcode configuration values
❌ Skip error handling — use domain-specific exceptions
❌ Export internal services that should stay private to a module
❌ Access shared mutable state across modules
❌ Force CQRS on modules that don't need it — start simple

Output Templates

When implementing a complete module, provide files in this order:

Domain entities — With module-prefixed names and business logic
Repository interface — In domain layer, defines data access contract
Service (default) or Commands/Queries + Handlers (if CQRS) — Implementing business rules
DTOs — Request/response with Swagger decorators and validation
Repository implementation — Prisma/TypeORM in infrastructure layer
Controller — With guards, Swagger docs, and proper HTTP codes
Module definition — NestJS module with explicit imports/exports
Tests — Unit tests for services/handlers, integration tests for boundaries
Domain events — If cross-module communication is needed

When designing architecture (not implementing), provide:

Executive Summary — Architecture overview, key decisions, rationale
Bounded Contexts Map — Responsibilities, aggregates, communication
Module Interface Contracts — Public API surface of each module
Data Model — Per-module schemas with ownership boundaries
Communication Diagram — Event flows between modules
Evolution Path — How to extract modules to microservices later

Quick Anti-Pattern Detection

Before finalizing any module, run scripts/validate-isolation.sh or verify manually:

# Check duplicate entity names across modules
grep -r "@Entity.*name:" libs/ | grep -o "name: '[^']*'" | sort | uniq -d

# Detect direct cross-module imports (should only import from index)
grep -r "from.*@company.*/" libs/ | grep -v shared | grep -v index

# Find shared mutable state
grep -r "export.*=.*new" libs/ | grep -v test

# Check for synchronous inter-module calls
grep -r "await.*\..*Service" libs/ | grep -v "this\."

If any check finds violations, fix them before proceeding.

MCP Tools

Use these MCP tools when available for enhanced results:

context7: Query latest docs for NestJS, Prisma, Better Auth, NX, and other stack components. Always prefer fresh docs over built-in knowledge.
sequential-thinking: Use for complex architectural analysis, multi-step design decisions, and tradeoff evaluation.

Knowledge Reference

NestJS, Fastify, Express, TypeScript, NX, Prisma, TypeORM, tRPC, DDD, Clean Architecture, CQRS, Event Sourcing, Bounded Contexts, Domain Events, Passport, JWT, Better Auth, class-validator, class-transformer, Swagger/OpenAPI, Jest, Supertest, Biome, Kafka, SQS, Redis, RabbitMQ

When it activates

Automatically, when a task matches. Use when building modular monolith backends, designing bounded contexts, creating domain modules, implementing event-driven module communication, or when user mentions "modular monolith", "bounded contexts", "module boundaries", "DDD", "CQRS", "clean architecture NestJS", or "monolith to microservices".

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

tech-leads-club / agent-skills

packages/skills-catalog/skills/(development)/nestjs-modular-monolith/SKILL.md

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License CC-BY-4.0Imported 8/3/2026Version v1Bundled 7 files · scripts

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What it changes

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Modular Monolith Specialist

Role Definition

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Core Principles

Behavioral Guidelines

Core Workflow

Phase 1: Discovery

Phase 2: Design

Phase 3: Implementation

Phase 4: Validation

Module Structure

Reference Guide

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