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n8n Error Handling · sickn33 | DecimalAI

DevOpsby sickn33

n8n Error Handling

Design visible, structured, recoverable n8n failures using error outputs, retries, Error Trigger workflows, and HTTP error responses.

Never made a case worseMaintained

SkillScore

SkillScore99/100

Test results 100 · Live runs 99

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

91%100%of test cases passed +9 pts

Without skill20 of 22

With skill22 of 22

2 fixed20 passed either way0 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: 508; 216 in the last 30 days
Forks: 3

See one task, with and without the skill

One test taskWhat are the two standard top-level JSON fields required in caller-facing error responses for n8n API endpoints?

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-error-handling
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-error-handling")
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-error-handling

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

Copy SKILL.md

Save it in your project, as

.claude/skills/sickn33-n8n-error-handling/SKILL.md

This gets SKILL.md only. The skill's 4 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-error-handling

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-error-handling")
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-error-handling/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-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/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-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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-error-handling")
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-error-handling/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sickn33-n8n-error-handling/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 4 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-error-handling

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

Copy SKILL.md

Save it in your project, as

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

This gets SKILL.md only. The skill's 4 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-error-handling/SKILL.md

Read the summary as JSON

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

This gets SKILL.md only. The skill's 4 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 Error Handling

Operational boundaries

Discover actual n8n tool schemas and node versions: this tree bundles documents, not an MCP server. Drafting does not authorize changing or activating production workflows, sending alerts or replaying writes. Retry only operations proven idempotent or protected by durable deduplication; ambiguous timeouts may have committed. An Error Trigger is not a process-crash/OOM availability guarantee: use independent platform monitoring. It does not fire for manual editor executions. Node error output positions depend on node output layout; verify rather than assuming index 1 for multi-output nodes. Use structured error codes rather than regex matches on untrusted text to decide status or retries.

When to Use

Use this skill for unattended workflows, webhook/API response contracts, retry design, error outputs, Error Trigger workflows, alerting, or any path where failure must be visible and recoverable.

Make retries bounded and idempotent, especially for sends, payments, and writes. Redact credentials, personal data, request bodies, and stack details from caller-facing responses and alerts; expose only the minimum diagnostic context required.

By default, when an n8n node throws, the whole workflow halts. For an interactive run you're watching, that's fine — you see the red node and fix it. For anything unattended (a webhook API, a cron job, a queue worker, an agent tool), it's the wrong default: the caller gets a timeout or an empty 500, the operator gets no alert, and the symptom is "the integration just stopped working" with no log and no clue.

This skill is about making failures loud, structured, and recoverable — and, best case, self-healing so transient blips never reach a human at all.

The two ideas that prevent most silent failures:

Per-node error outputs — a node's failure routes down a second output you control, instead of killing the run.
A workflow-level error workflow — a catch-all that fires for anything that escapes per-node handling (timeouts, crashes between nodes, unwired failures).

When you actually need this

Workflow shape	Error handling posture
Webhook / API (anything with `Respond to Webhook`)	Required. Every fallible node's error output wired; status code matches cause.
Scheduled / cron / queue worker / agent tool (unattended)	Required. A workflow-level error workflow, plus bounded retries on retry-safe nodes.
Internal one-off you run and watch yourself	Optional. Default `onError: "stopWorkflow"` is fine — you'll see the red node and re-run.

The dividing line: if anyone other than you sees the output — a downstream system, an end user, an on-call engineer — the failure has to be handled, not swallowed. If you're the only watcher and the cost of failure is "I notice and re-run", looser is fine.

The #1 silent trap: per-node error output is a TWO-step setup

This is the single most common way an n8n workflow "handles" errors while actually swallowing them. Routing a node's failure to a handler takes two changes, and doing only one looks complete but misbehaves:

Set onError: "continueErrorOutput" on the node. This is what creates the second output. Without it, main[1] doesn't exist no matter what you wire.
Wire that error output (connections.<node>.main[1], i.e. sourceIndex: 1) to a real handler. Without a target, the error data is emitted into the void.

Get one without the other and you hit a failure mode:

What you did	What happens at runtime
`onError` set, error output not wired	Error data is silently discarded. Downstream doesn't fire. The dashboard shows the run as succeeded. Worst case — no error logged anywhere.
Error output wired, `onError` not set	The slot never fires; the handler is unreachable. On failure the workflow just halts (default `stopWorkflow`).
Both done	Failure routes down `main[1]` to your handler. ✅

Doing both with `n8n_update_partial_workflow`

// 1) Turn on the error output (creates main[1])
{ type: "updateNode", nodeName: "HTTP Request",
  changes: { onError: "continueErrorOutput" } }

// 2) Wire the error output to a handler. sourceIndex: 1 = the error output.
{ type: "addConnection",
  source: "HTTP Request",
  target: "Handle Error",
  sourceIndex: 1 }

sourceIndex: 0 is the success path, sourceIndex: 1 is the error path. (For IF nodes the aliases branch: "true"/"false" map to index 0/1; for a generic fallible node, use the explicit sourceIndex: 1.)

Then verify. This trap doesn't surface in validate_workflow — a half-wired error output validates clean. Pull the workflow with n8n_get_workflow and confirm both halves:

The node's onError is "continueErrorOutput".
connections["HTTP Request"].main[1] contains your handler.

Valid onError values:

Value	Effect
`"stopWorkflow"` (default)	Error halts the whole workflow.
`"continueRegularOutput"`	Error item flows out the normal output. Rare, usually wrong — downstream gets error-shaped data and keeps going.
`"continueErrorOutput"`	Error item flows out the separate error output (`main[1]`). The one you wire.

Full failure-mode catalog, fan-in/fan-out shapes, and verification: references/NODE_ERROR_OUTPUTS.md.

Self-healing first: `retryOnFail` before you wire error paths

Before you build error branches, absorb the transient failures so they never reach those branches. On a network node only after verifying its retry safety — HTTP Request, comms (Gmail/Slack/Discord), databases, AI nodes, third-party integrations — set node-level retry:

{ type: "updateNode", nodeName: "HTTP Request",
  changes: {
    retryOnFail: true,
    maxTries: 3,
    waitBetweenTries: 5000   // ms
  } }

Why this comes first: a 429 or a brief upstream hiccup will retry and usually succeed on its own. The error output then fires only on real, persistent failures — so your 5xx responses and on-call alerts reflect actual problems instead of noise.

Engine limits to know: retry fires on any error (there's no per-status-code filter), maxTries caps at 5, and waitBetweenTries caps at 5000ms — so 5000 is both the max and a sensible default. See n8n-node-configuration (NODE_FAMILY_GOTCHAS.md) for node-specific notes.

API workflows: the canonical shape

A webhook-triggered workflow that responds to its caller has one rule that overrides everything else: no hanging branches. Every path — success and every error — must end at a Respond to Webhook, or the caller sits there until it times out.

Webhook (responseMode: "responseNode")
  ├── validate input → process → Respond (200, body)
  └── (any fallible node's error output → sourceIndex 1)
            → Respond (4xx/5xx, structured error body)
            → optional: log redacted diagnostics privately / notify

Three things make this work:

Fan-in to one error responder. Many fallible nodes can route their main[1] to a single Respond node. Keeps the graph readable.
Validation failures (4xx) are checked upstream, not via error outputs. A missing field isn't a node crashing — it's an expected outcome with a known response. Branch on it with IF/Switch (or the schema validator below) and return 400/401/403/404 directly. Error outputs are for unexpected failures (5xx).
responseCode defaults to 200 — even on error branches. This is its own silent trap (see references/RESPONSE_SHAPES.md and n8n-node-configuration at ../n8n-node-configuration/references/NODE_FAMILY_GOTCHAS.md): an error branch that returns 200 with an error body looks like success to the caller's HTTP client, so their error handling never fires. Set responseCode explicitly on every Respond node.

Input validation: the Set-node schema validator

For any endpoint doing structured input validation, run the check as an IIFE inside a single Set node rather than a chain of IF/Switch nodes per field. One node validates the whole payload, returns { valid, validationError, details, requiredSchema }, and an IF branches on valid → your logic (200) or a 400 Respond that echoes the schema back so the caller can self-correct. Choose a validator that enforces the stated schema; no performance comparison is established here. The full pattern, the constraint cookbook, and the expression-escaping gotchas live in references/API_WORKFLOWS.md.

Response shapes: map cause → status code

A 5xx with text/plain "Internal Server Error" is technically an error response and practically useless. And not every failure is a 5xx. Match the status code to why the request failed, because the caller branches on it: their monitoring alerts on 5xx (your fault) but not 4xx (their fault), and 5xx suggests "retry" while 4xx suggests "don't".

The common mistake: wiring everything — including bad input — to one Respond that returns 500 internal_error. Now the caller can't tell their bug from your outage, and your error rates can't separate real incidents from client noise.

Cause	Status	`error` code	Where it's handled
Required field missing / wrong type	400	`validation_error`	Upstream check (schema validator / IF), not error output
Auth missing or invalid	401	`unauthorized`	Upstream check
Authenticated but not allowed	403	`forbidden`	Upstream check
Resource ID valid in request, absent in your data	404	`not_found`	Branch on the lookup result, not its error
Conflicts with current state (duplicate, race)	409	`conflict`	Detect with logic
Caller exceeded rate limit	429	`rate_limit_exceeded`	Set `Retry-After` header
Node threw, cause unknown	500	`internal_error`	Error output path
Third-party API returned an error	502	`upstream_error`	Error output of the HTTP node
Can't process right now (downstream down)	503	`service_unavailable`	Detect specific error, hint retry
Third-party API timed out	504	`upstream_timeout`	Error output filtered by message

So there are two distinct flows: 4xx is decided before the work (IF/Switch + dedicated Respond), 5xx comes out of error outputs ("we tried, it broke").

One Respond, expression-driven code. When error paths differ only by number and message (same body shape, same headers), don't fan out to N Respond nodes through a Switch. The Respond node accepts expressions in both Response Code and body — compute the code inline:

// Response Code field on a single Respond to Webhook:
{{ (() => {
    const msg = $json.error?.message || $json.message || '';
    if (msg.includes('INVALID_ID')) return 400;
    if (/429|too many/i.test(msg)) return 429;
    if (/timeout/i.test(msg))      return 504;
    if (/upstream|llm|api/i.test(msg)) return 502;
    return 500;
})() }}

Reserve Switch + multiple Responds for paths that diverge structurally (different headers, different body shapes, redirects). Same shape with a different number is one expression-driven Respond.

The default envelope is { "error": "<code>", "message": "<human text>" } — the HTTP status already says success-vs-failure, so no ok: false flag. Never leak internals (stack traces, SQL, upstream bodies, tokens) into the response — log those privately, return a sanitized message. Correlation IDs, retry_after, validation details, and the full do-not-leak list are in references/RESPONSE_SHAPES.md.

Workflow-level error workflow (the catch-all)

Per-node outputs handle the failures you anticipated on the nodes you remembered to wire. An error workflow catches everything else: a node you forgot to wire, a crash between nodes, a whole-workflow timeout, a trigger failure. For unattended workflows this is the safety net that turns "it silently stopped" into "an alert arrived".

Build it as a separate workflow starting with an Error Trigger node. n8n invokes it with the failure context:

{
  "execution": { "id": "...", "url": "...", "lastNodeExecuted": "Fetch order",
    "error": { "name": "NodeApiError", "message": "...", "timestamp": 1715000000000 } },
  "workflow": { "id": "...", "name": "Sync Stripe customers" }
}

Minimal version — capture → notify:

Error Trigger → Set (build alert from execution + error) → Slack/email (post to #incidents)

A good alert includes the workflow name, a link to the editor and a link to the failed execution, the failed node name, and the real error message (not "Workflow failed"). Field expressions and the optional "fetch the failing input via the n8n node" upgrade are in references/ERROR_WORKFLOWS.md.

Two traps worth flagging up front:

The recursion trap. If the error workflow notifies Slack and Slack is what's down, the error workflow fails too — and the original error vanishes. Notify on a different channel than your monitored workflows use (most workflows alert Slack → error workflow uses email), and add a fallback (write to a Data Table) so a failed notification still leaves a trace.
A "handled" error won't bubble up. If a node's error output is wired to a no-op that drops the data, n8n considers the error handled and the error workflow does not fire. Only catch per-node when you're actually doing something with the error.

What the community MCP can't do: assigning the error workflow (instance default or per-workflow override) is an n8n setting available through the UI and, on supported versions/connectors, an API — Workflow Settings → Error Workflow. Inspect the actual connector schema before claiming it is unsupported. Build the error workflow with the MCP, then tell the user the exact UI step to wire it up, and to repeat it (or set the instance default) for every unattended workflow.

What's NOT available via the community MCP

Want to do	Reality
Set a workflow's Error Workflow setting	Connector/version dependent; UI fallback (Workflow Settings → Error Workflow). No MCP tool. Build the workflow, then hand the user the UI step.
Toggle other workflow settings (Save Execution Data, timezone, timeout, caller policy)	Connector/version dependent; UI fallback. `n8n_update_partial_workflow` has `updateSettings`, but the error-workflow assignment is not reliably exposed — confirm in the UI.
Enable instance-wide error logging (Sentry, server logs)	Instance config, outside n8n workflows entirely.

What the MCP can do: build the error workflow, set onError/retryOnFail on nodes (updateNode/patchNodeField), wire error outputs (addConnection with sourceIndex: 1), validate (validate_workflow, n8n_validate_workflow), auto-fix common issues (n8n_autofix_workflow), test (n8n_test_workflow), and inspect failures (n8n_executions).

Anti-patterns

Anti-pattern	What goes wrong	Fix
`onError` set but error output unwired	Error silently discarded; run shows as succeeded	Wire `sourceIndex: 1` to a real handler, or revert `onError` to `stopWorkflow` so it's loud
Error output wired but `onError` not set	Slot never fires; handler unreachable; workflow halts on failure	Set `onError: "continueErrorOutput"`
Webhook → process → respond, no error branch	Caller gets a timeout or n8n's generic 500	Wire every fallible node's error output to a Respond
Error branch returns 200 with an `{error}` body	Caller's client reads success; their error handling never fires	Set `responseCode` to 4xx/5xx explicitly on error Responds
One 500 `internal_error` for everything	Caller can't tell their bad input from your outage	Map cause → status (4xx caller, 5xx you)
Catching errors in a Code node and returning them as data	Downstream processes error-shaped data and continues	Let it throw; use `onError: "continueErrorOutput"` + wired path
Network node with no `retryOnFail`	Every transient 429/blip surfaces as a 5xx; alerts fire on noise	`retryOnFail: true, maxTries: 3, waitBetweenTries: 5000`
Switch → N Responds differing only by status code	5 nodes for what's one Respond	Compute the code inline in one expression-driven Respond
Unattended workflow with no error workflow	A genuine failure goes nowhere	Build an Error Trigger workflow + assign it in the UI
Error workflow notifies the same channel the workflows monitor	Channel down → error workflow also fails → error vanishes	Use a different channel + a Data Table fallback
Leaking `$json.error` (stack/SQL/tokens) into the response	Exposes internals to callers/attackers	Log privately, return a sanitized message

Reference files

File	Read when
references/NODE_ERROR_OUTPUTS.md	Wiring a per-node error output on individual fallible nodes
references/API_WORKFLOWS.md	Building/reviewing a webhook → Respond workflow, including the schema validator
references/RESPONSE_SHAPES.md	Defining response body conventions, status codes, and what not to leak
references/ERROR_WORKFLOWS.md	Setting up the workflow-level catch-all for unattended workflows

Integration with other skills

n8n-workflow-patterns — the webhook/API and scheduled patterns are where error handling lives. Use it for the overall shape; use this skill to harden it.
n8n-node-configuration — onError/retryOnFail are node config; NODE_FAMILY_GOTCHAS.md covers the Webhook/Respond response-code traps in depth.
n8n-validation-expert — the half-wired error output (one of the two steps missing) is a connection/config audit item, not a validation error. This skill is the fix.
n8n-expression-syntax — the expression-driven Response Code and the alert-message expressions rely on correct {{ }} syntax and $json.error access.
n8n-code-javascript / n8n-code-python — if you catch errors inside a Code node, decide deliberately: re-throw to use the error output, or handle and continue. Don't return error-shaped data and pretend it succeeded.
n8n-code-tool — an agent's Code Tool surfaces thrown errors back to the LLM, which then retries; that's a different error contract from workflow nodes.
n8n-binary-and-data — file/binary operations are fallible too; wire their error outputs like any network node.

Quick reference checklist

For an API / webhook workflow:

Webhook trigger uses responseMode: "responseNode"
Input validated upstream → 4xx Respond (schema validator or IF)
Every fallible node has onError: "continueErrorOutput" and main[1] wired
Network nodes have retryOnFail: true, maxTries: 3, waitBetweenTries: 5000
Error path ends at a Respond with an explicit 4xx/5xx responseCode
Status code matches cause (4xx caller, 5xx you)
Error body is { error, message } — no stack traces, SQL, or tokens
Verified with n8n_get_workflow: both onError and main[1] present on each fallible node

For an unattended (scheduled/cron/queue) workflow:

Network nodes have retryOnFail configured
An Error Trigger workflow exists (capture → notify, optional retry)
The error workflow notifies on a different channel + has a fallback (recursion trap)
The error-workflow setting is assigned in the n8n UI (verify the connected tool or use the UI)

Remember: the default is silence. Error handling is two moves — make the failure route (per-node onError + wired output, or a catch-all error workflow) and make it speak (a status code and body that tell the truth). Half a move is worse than none, because it looks done.

Limitations

Retry safety depends on each downstream operation's idempotency and cannot be inferred from workflow shape alone.
MCP validation cannot assign or prove the instance-level Error Workflow setting; verify it in the n8n UI.
Redaction rules must be adapted to the workflow's data classification and legal requirements.

About this skill

Automatic

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0 critical · 0 warning · 0 info — scanner v8 — 11/8/2026

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Source

Imported

sickn33 / antigravity-awesome-skills

skills/n8n-error-handling/SKILL.md

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License MITImported 11/8/2026Version v2Bundled 4 files

Illustrative Want to see what sickn33/n8n-error-handling 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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n8n Error Handling

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When to Use

When you actually need this

The #1 silent trap: per-node error output is a TWO-step setup

Doing both with n8n_update_partial_workflow

Self-healing first: retryOnFail before you wire error paths

API workflows: the canonical shape

Input validation: the Set-node schema validator

Response shapes: map cause → status code

Workflow-level error workflow (the catch-all)

What's NOT available via the community MCP

Anti-patterns

Reference files

Integration with other skills

Quick reference checklist

Limitations

SkillSafety

Source

Related skills

More in the registry

Doing both with `n8n_update_partial_workflow`

Self-healing first: `retryOnFail` before you wire error paths