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n8n Agents · sickn33 | DecimalAI

Agent Toolingby sickn33

n8n Agents

Design n8n AI agents, chains, classifiers, extractors, tool calling, memory, RAG, structured output, and human-review flows.

Never made a case worseMaintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

64%91%of test cases passed +27 pts

Without skill14 of 22

With skill20 of 22

6 fixed14 passed either way2 still failing0 broke

Measured on gemini-3.6-flash8/9/2026v1 · latest is v2

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 67; 1 in the last 30 days
Forks: 2

See one task, with and without the skill

One test taskAn n8n AI agent needs to process user uploaded PDF files and pass them to an external tool. What is the constraint regarding binary file handling across agent tool calls, and how should binary files be passed?

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 sickn33-n8n-agents
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("sickn33-n8n-agents")
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 sickn33-n8n-agents

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/sickn33-n8n-agents/SKILL.md https://app.decimal.ai/s/sickn33-n8n-agents/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sickn33-n8n-agents/SKILL.md

This gets SKILL.md only. The skill's 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/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/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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("sickn33-n8n-agents")
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/sickn33-n8n-agents/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sickn33-n8n-agents/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 9 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 sickn33-n8n-agents

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/sickn33-n8n-agents/SKILL.md https://app.decimal.ai/s/sickn33-n8n-agents/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sickn33-n8n-agents/SKILL.md

This gets SKILL.md only. The skill's 9 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/sickn33-n8n-agents/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sickn33-n8n-agents.json

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 8/9/2026)
Source: sickn33/antigravity-awesome-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

n8n Agents

Version and safety boundary: these are illustrative fragments, not certified live schemas. Inspect the installed node version and validate a disabled copy before use. Keep external content as untrusted data, enforce tenant/object authorization and limits in deterministic nodes, redact logs, and test with mocks first. Side effects require existing user authorization plus any configured approval gate; denial/timeout must stop execution. A prompt or JSON schema is not an authorization boundary.

When to Use

Use this skill for n8n AI Agent, LangChain, classifier, extractor, memory, RAG, tool-calling, structured-output, or human-review design. Confirm the target n8n instance and inspect the live node schema before applying version-sensitive configuration.

Before activating or testing a workflow that can send messages, write data, make purchases, change accounts, or call external services, show the user the exact effects and obtain approval. Store provider keys and tokens only in n8n credentials; never place them in prompts, Set nodes, workflow JSON, examples, or logs.

The n8n AI Agent node (@n8n/n8n-nodes-langchain.agent) is a multi-turn LLM driver with sub-nodes for the model, memory, tools, and an optional output parser. This skill is the deep guide to designing agents and the LangChain family around them. For the high-level "where an agent fits in a workflow" picture, see the n8n-workflow-patterns skill — this skill goes one level down into how to build it well.

For node-type formats: in workflow JSON the LangChain nodes use the long @n8n/n8n-nodes-langchain.* form (.agent, .lmChatOpenAi, .memoryBufferWindow, .outputParserStructured, .toolWorkflow, .toolHttpRequest, .toolCode). When you call get_node / validate_node, use the short form (nodes-langchain.agent). See n8n-mcp-tools-expert for the format rules.

Pick the right node first

Reaching for an Agent when the task is one-shot classification or extraction is the most common over-build. Decide before you wire anything:

You need to…	Use	Why
Call tools, reason over multiple turns, or hold memory	AI Agent (`.agent`)	The full loop: model + tools + memory + optional parser. Also a fine default when you'd rather standardize.
One-shot text in → text out, no tools	Basic LLM Chain (`.chainLlm`)	No agent loop, easier to debug. Still accepts an `outputParserStructured` sub-node.
Route a natural-language input to one of N branches	Text Classifier (`.textClassifier`)	ONE node, N output handles, downstream wires directly into each. Not Agent + Switch.
Pull structured fields out of free text	Information Extractor (`.informationExtractor`)	Purpose-built field extraction with a schema.
3-way positive/neutral/negative split	Sentiment Analysis (`.sentimentAnalysis`)	Built-in branch outputs.
Condense a long document	Summarization Chain (`.chainSummarization`)	Map-reduce summarization built in.
Generate an image / audio / video	The provider's native single-call node (OpenAI, Gemini, ElevenLabs…)	NEVER wrap media generation in an Agent — see "Binary and the agent boundary".

Text Classifier detail (the Agent + Switch anti-pattern): every category needs both a name AND a description. The model routes against the description, not the name — a category with no description gets picked by coin-flip. Set options.enableAutoFixing: true for robustness on edge inputs. One node, N branches, done. Reaching for an Agent that "decides" then a Switch that "routes" is two nodes plus prompt boilerplate for what Text Classifier does natively.

Chat-model nodes (.lmChatOpenAi, .lmChatAnthropic, .lmChatOpenRouter, …) are sub-nodes — they don't run standalone. They wire into a chain, agent, classifier, or extractor via the ai_languageModel connection.

The sub-node pattern

The Agent has a main input (the prompt / user message) and up to four sub-node slots, each wired by its own ai_* connection type:

Slot	Connection type	Required?	Node example
model	`ai_languageModel`	Yes	`.lmChatOpenAi`, `.lmChatAnthropic`, `.lmChatOpenRouter`
memory	`ai_memory`	Optional	`.memoryBufferWindow`, `.memoryPostgresChat`
tools	`ai_tool`	Optional (but the point of an agent)	`slackTool`, `.toolWorkflow`, `.toolHttpRequest`, `.toolCode`
outputParser	`ai_outputParser`	Optional	`.outputParserStructured`

A sub-node connects FROM itself TO the agent. In workflow JSON the connection lives on the sub-node, keyed by the ai_* type:

"Main LLM": {
  "ai_languageModel": [[{ "node": "AI Agent", "type": "ai_languageModel", "index": 0 }]]
},
"Simple Memory": {
  "ai_memory": [[{ "node": "AI Agent", "type": "ai_memory", "index": 0 }]]
},
"Search customer DB": {
  "ai_tool": [[{ "node": "AI Agent", "type": "ai_tool", "index": 0 }]]
}

Multiple tools all connect into the same ai_tool index 0 — they stack, they don't fan into separate indices. With n8n_update_partial_workflow you wire each with an addConnection op using sourceOutput: "ai_tool". The agent puts its final answer in $json.output (not .text, not .response) — downstream nodes read {{ $json.output }}.

See references/EXAMPLES.md for a complete stateless agent-core node-object snippet.

Two non-negotiables

Tool names and descriptions ARE part of the prompt. The model picks a tool by reading its name and description — nothing else. A tool named tool1 with an empty description is invisible to the model: it skips it, mis-selects it, or hallucinates parameters. There's usually no error — just an agent that "won't use my tool". Treat both like API design. → references/TOOLS.md
Structured output must validate and fail safely. Verify whether repair requires a separate Auto-fixing Output Parser (outputParserAutofixing) wrapping the structured parser; do not assume an autoFix field on the structured parser exists. Without autoFix, one malformed JSON response halts the whole workflow. → references/STRUCTURED_OUTPUT.md

Strong defaults

Per-tool usage goes in the tool description, not the system prompt. Anything about how to call this specific tool belongs with the tool, so it travels across agents and keeps the system prompt focused. → references/SYSTEM_PROMPT.md
Sub-workflow tools (.toolWorkflow) for anything multi-step. Any workflow becomes a tool with typed $fromAI() inputs, and composes with branching, error handling, and reuse. Default here when in doubt. → references/SUBWORKFLOW_AS_TOOL.md and n8n-subworkflows.
Wrap tools with user-visible side effects in human review. Sends, payments, refunds, account changes get gated behind an approval node so a human signs off before the tool fires. → references/HUMAN_REVIEW.md
Measure and bound maxIterations. The default tool-call cap is low (single digits on most versions) — fine for a one-tool agent, far too low for a multi-tool agent that chains several calls per turn. It surfaces as "max iterations reached" or empty output. Set options.maxIterations to a realistic ceiling (15 for a focused sub-agent, a measured bounded budget for a broad orchestrator, with token, elapsed-time and tool-call limits).
Put the current date in the system prompt via {{ $now }} (or {{ $now.toISODate() }}). A hardcoded date is stale immediately.

The four tool types

Pick the lightest option that covers the job:

Tool type	Node	Use when
Native tool node	`slackTool`, `gmailTool`, `toolCalculator`, …	The capability maps to one existing node + one operation. Lowest overhead.
Sub-workflow as tool	`.toolWorkflow`	More than one node, reusable logic, or you want independent testability. The canonical n8n way — default when in doubt.
HTTP Request Tool	`.toolHttpRequest`	A single external HTTP API the agent should orchestrate directly. Reuse the service's predefined credential to cover operations a native node doesn't expose.
MCP Client Tool	`.mcpClientTool`	A maintained MCP server already covers it, or you want one published workflow to serve many agents.

There is also a Custom Code Tool (.toolCode) for pure inline computation — but its runtime contract (string in / string out, no $fromAI, no $helpers) is owned by the n8n-code-tool skill. Read that before writing one. Rule of thumb: if you find yourself reaching for $fromAI() inside the code, you want .toolWorkflow instead.

`$fromAI()`: how the agent fills tool parameters

Tool parameters the agent should decide are wrapped in $fromAI(). It is a real n8n expression helper, used inside a tool node's parameter expressions:

={{ $fromAI('paramName', 'what to put here — be specific: format, range, example', 'string') }}

paramName — the name the model uses internally (snake_case or camelCase, be consistent).
description — tells the model what value to produce. It is part of the prompt — write it like JSDoc.
type (optional) — 'string' (default), 'number', 'boolean', 'json'. A wrong-typed value fails the call.
defaultValue (optional) — used when the model omits it.

$fromAI() carries JSON only — it cannot carry binary (no base64, no file bytes). And not every parameter has to be $fromAI: plumb identity, authority limits, and correlation IDs (userId, refund caps, sessionId) deterministically from workflow context so the agent can't get them wrong or even see them. → references/TOOLS.md for the full anatomy and the "give the agent a button, not a steering wheel" pattern.

System prompt vs tool description

Belongs in the system prompt	Belongs in the tool's description
Persona, role, voice	What this specific tool does
Global output/format rules ("respond in markdown")	When to use it vs other tools
Refusal / safety behavior	What each parameter means and its shape
Display protocols (`![]()` for images)	Examples of good vs bad invocations
Universal context (current date via `$now`, user role)	Tool-specific gotchas (rate limits, edge cases)
Inter-tool flow ("after generating, always display")	Tool-specific input transformations

Why split it: a well-described tool works in any agent that drops it in, tool schemas/descriptions may be sent on every model turn; measure actual context cost, and you update one tool description instead of a paragraph buried in a 5000-token prompt. → references/SYSTEM_PROMPT.md

Structured output: when and how

Add an outputParserStructured sub-node (wired ai_outputParser) when downstream needs strict JSON, not free-form text. Two rules:

Use schemaType: 'manual' with a real JSON Schema, not jsonSchemaExample. An example can't express required-vs-optional, enums, numeric ranges, or array constraints — you outgrow it the first time the shape gets non-trivial. Reach for fromJson + an example only for throwaway shapes.
Optional bounded repair, using the installed Auto-fixing Output Parser schema. Wire a second model into the parser's ai_languageModel slot. Reconciling broken JSON against a schema is a coding task — a weak fixer just produces another malformed retry and burns tokens.

→ references/STRUCTURED_OUTPUT.md for the schema patterns, the load-bearing "DO NOT wrap in markdown" retry line, and the parse-failure cookbook.

Memory: brief mental model

Memory is a sub-node (ai_memory). Without it, every call is stateless — correct for one-shot tasks (classify, summarize). With it, the agent holds a conversation, keyed by whatever expression you bind to sessionKey.

memoryBufferWindow — temporary process-local conversation context. Do not assume restart persistence, shared-worker availability or archival deletion from window size. Choose a measured window based on token/privacy needs.
memoryPostgresChat / memoryRedisChat — only when memory must be read outside the agent (your own UI, analytics, cross-system). Use a durable backend when history must survive process restarts or be shared across workers.

Plumb a stable key from the trigger to memory consistently. Chat triggers fill sessionId automatically; for other surfaces derive one (Slack thread_ts, a webhook conversation ID). Never hardcode sessionId: 'default' and never put sessionId behind $fromAI (the model will fabricate a UUID). → references/MEMORY.md

Binary and the agent boundary

This is the seam that trips people up:

The model CAN see uploaded images (vision) via options.passthroughBinaryImages: true on the agent.
Tools CANNOT receive binary. $fromAI() is JSON-only — no base64, no bytes, even through non-AI bindings.
The agent's output is text-shaped (or structured-text with a parser). When a model returns image/audio/video bytes, the Agent doesn't surface them at all — there's nothing to recover downstream.

Workaround: pre-stage uploads to storage before the agent runs, inject the storage keys into the system prompt, and let tools accept the key as a string parameter and re-fetch internally. For one-shot media generation, skip the agent and call the provider's native single-call node directly.

The binary mechanics (which storage, how to stage, how to re-fetch) are owned by n8n-binary-and-data — see its agent-tool binary reference. This skill only marks the boundary; don't re-derive the mechanics here.

Human review (gate destructive tools)

When a tool's effect needs human sign-off before execution (sends, payments, refunds, account changes), wrap it with a review tool node — slackHitlTool, discordHitlTool, telegramHitlTool, gmailHitlTool, etc. (n8n names these "Hitl" / human-in-the-loop). The review node sits between the wrapped tool and the agent on the ai_tool connection: wrapped tool → review node → Agent.

Whether sign-off is needed is a product/policy call — surface the question to the user, recommend based on blast radius, and let them decide.

The critical rule: show the actual parameters the wrapped tool will receive. Use the literal {{ $tool.parameters.<name> }} in the approval message, never a $fromAI() paraphrase — otherwise the human approves text the model made up, not the call about to fire. → references/HUMAN_REVIEW.md

Chat agents (Slack, Discord, Teams, Telegram)

The one non-negotiable, regardless of complexity: any chat-triggered workflow that posts a reply MUST filter out the bot's own user ID, or its own replies re-trigger it in an infinite loop that burns runs and tokens. Prefer trigger-level filtering when available (Slack Trigger's options.userIds is an exclusion list — put the bot ID there); otherwise filter $json.user !== '<BOT_USER_ID>' in the first node after the trigger.

Beyond the filter, a simple bot (trigger → agent → reply) lives fine in one workflow. Split into shell + core + sub-agents only once you need loading UX, sub-agents, multi-surface reuse, or robust error handling:

Shell — trigger, anti-loop filter, event-type Switch, loading/error UX, renders the reply. No LLM.
Core — stateless agent, chatInput + threadId inputs, memory keyed on threadId, tools and sub-agents.
Sub-agents — one narrow domain each, called via .toolWorkflow, stateless (full context in chatInput).

→ references/CHAT_AGENT_PATTERNS.md for per-surface semantics, threading-as-session, and the full topology.

RAG (retrieval augmented generation)

n8n ships the LangChain RAG primitives (document loaders, splitters, embeddings, vector stores, retrievers). Two opinions worth stating up front:

Rule out cheaper lookups first. Exact lookups → a database or Data Table query, not RAG. Freshness → a live search tool. A small/structured doc set → give the agent list/fetch tools. Reach for a vector store only when there are too many docs to list and queries are semantic.
Wire the vector store as a retrieval tool (mode: 'retrieve-as-tool', ai_tool) so the agent decides when retrieval is relevant and can phrase the query itself. Embed query and documents with the same model.

→ references/RAG.md (intentionally thin — defaults depend on data shape and scale).

Reference files

File	Read when
references/TOOLS.md	Adding tools, choosing among the four types, writing names/descriptions, `$fromAI` anatomy
references/SUBWORKFLOW_AS_TOOL.md	Wiring a sub-workflow as a tool via `.toolWorkflow`, mapping agent-filled vs plumbed params
references/SYSTEM_PROMPT.md	Writing/refactoring a system prompt, the system-prompt-vs-tool-description split
references/STRUCTURED_OUTPUT.md	Forcing JSON output, configuring autoFix, the fixer model, parse-failure fixes
references/MEMORY.md	Choosing a memory type, persistence, sessionId handling
references/HUMAN_REVIEW.md	Adding human approval, approval-message content, multi-channel approver
references/CHAT_AGENT_PATTERNS.md	Building a Slack/Discord/Teams/Telegram bot, shell + core + sub-agents topology
references/RAG.md	Retrieval-augmented agents (thin by design)
references/EXAMPLES.md	Concrete node-object snippets: stateless agent core, Slack router shell, domain sub-agent

Anti-patterns

Anti-pattern	What goes wrong	Fix
Generic tool names (`tool1`, `doStuff`, `runQuery`)	Model can't tell which tool to pick — skips them or hallucinates params	Verb-first specific names: `Search customer database`, `Generate image with Veo`
Empty or one-line tool descriptions	Model has no idea when to invoke; bad selection, no error	Write a real description: what it does, when to use, what each param means
Cramming per-tool instructions into the system prompt	Bloated prompt, no reuse, per-tool guidance buried	Move tool-specific instructions into tool descriptions
Agent + Switch to route on natural language	Two nodes + prompt boilerplate where Text Classifier is one node	Use Text Classifier — each category gets its own output handle (name and description)
Wrapping image/audio/video generation in an Agent	Binary doesn't flow through tools or out of the agent output	Use the provider's native single-call node directly
`outputParserStructured` without `autoFix`	One malformed response halts the workflow	`autoFix: true` + a coding-capable fixer model
Passing binary directly to a tool	Doesn't work — binary can't cross the tool boundary	Pre-stage to storage, pass keys; see n8n-binary-and-data
Hardcoded `sessionId` / no sessionId / `sessionId` behind `$fromAI`	Conversations cross, or the model fabricates a UUID	Plumb a stable key from the trigger to memory and tools
Two near-identical tools	Selection is non-deterministic, model gets confused	One tool with internal branching driven by a parameter
Chat bot with no bot-user filter	Its own replies re-trigger it → infinite loop	Exclude the bot user ID at the trigger or first node
`maxIterations` left at the low default on a multi-tool agent	"Max iterations reached" / empty output	Raise `options.maxIterations`
Filling the human-review message via `$fromAI()`	Approver signs off on a paraphrase, not the real call	Use literal `{{ $tool.parameters.<name> }}`

What's NOT available via the community MCP

Want to do	Reality
Run / chat-test the agent end-to-end with live tokens	`n8n_test_workflow` runs the workflow, but a true multi-turn chat session is a UI activity (canvas chat tester).
Set credentials' actual secret values	`n8n_manage_credentials` creates/updates credential records, but the agent provider keys themselves are entered/verified in the UI.
Assign a workflow's Error Workflow	UI only — see n8n-error-handling. Build the catch-all, then hand the user the UI step.
Pin the exact model availability per instance	Model lists shift between versions — `search_nodes`/`get_node` reflect what's installed. Verify on the target instance.

What the MCP can do: search and inspect every LangChain node (search_nodes, get_node), validate node config and the whole graph (validate_node, validate_workflow), build and patch the agent and its sub-nodes (n8n_update_partial_workflow with addConnection on ai_* outputs), test (n8n_test_workflow), and pull the saved JSON to verify wiring (n8n_get_workflow). The deep AI-agent guide also lives in tools_documentation({topic: "ai_agents_guide", depth: "full"}).

Integration with other skills

n8n-workflow-patterns — the high-level "agent in a workflow" shape. This skill is the deep dive; start there for architecture.
n8n-mcp-tools-expert — node-type formats (short form for get_node, long form in JSON) and tool-selection guidance. Consult before any MCP call.
n8n-node-configuration — displayOptions-driven fields on the agent and sub-nodes; Slack/Block Kit message shapes (NODE_FAMILY_GOTCHAS.md, Slack section).
n8n-expression-syntax — {{ }}, $json.output, $now, and $fromAI/$tool.parameters all rely on correct expression syntax.
n8n-code-tool — the Custom Code Tool's runtime contract (string in/out, no $fromAI). Read it before writing a .toolCode.
n8n-subworkflows — the sub-workflow primitive that .toolWorkflow builds on (Execute Workflow Trigger inputs/outputs, naming, search-before-build).
n8n-binary-and-data — owns the agent-tool binary boundary mechanics (staging uploads, returning generated files).
n8n-validation-expert — interpreting validate_workflow results, including AI-connection issues (a tool wired into main instead of ai_tool flags as disconnected).
n8n-error-handling — onError: 'continueErrorOutput' on tool sub-workflows and the agent-core call; error UX on chat shells.
n8n-code-javascript / n8n-code-python — for Code-node logic inside a tool sub-workflow (different sandbox from the Code Tool).

Quick reference checklist

Before shipping an agent:

Remember: an agent is only as good as its tool names, descriptions, and system-prompt discipline. The model can't see your wiring — it sees a system prompt and a list of named, described tools. Design those like an API and most "the agent won't behave" problems disappear.

Limitations

Node types, parameters, model availability, and defaults vary by n8n version; verify them against the target instance.
This guidance cannot set provider secret values or prove a live multi-turn agent works without an authorized execution.
Validation does not prove tool selection quality, correct wiring, idempotency, or safe side effects; inspect and test those separately.

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sickn33 / antigravity-awesome-skills

skills/n8n-agents/SKILL.md

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License MITImported 11/8/2026Version v2Bundled 9 files

Illustrative Want to see what sickn33/n8n-agents 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

n8n Agents

When to Use

Pick the right node first

The sub-node pattern

Two non-negotiables

Strong defaults

The four tool types

$fromAI(): how the agent fills tool parameters

System prompt vs tool description

Structured output: when and how

Memory: brief mental model

Binary and the agent boundary

Human review (gate destructive tools)

Chat agents (Slack, Discord, Teams, Telegram)

RAG (retrieval augmented generation)

Reference files

Anti-patterns

What's NOT available via the community MCP

Integration with other skills

Quick reference checklist

Limitations

SkillSafety

Source

Related skills

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`$fromAI()`: how the agent fills tool parameters