Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

n8n Binary and Data · sickn33 | DecimalAI

Databy sickn33

n8n Binary and Data

Handle n8n files and binary data across uploads, downloads, transforms, multimodal inputs, agent tools, and chat surfaces.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

67%100%of test cases passed +33 pts

Without skill16 of 24

With skill24 of 24

8 fixed16 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: 0; All time

See one task, with and without the skill

One test taskMy workflow uses an AI image generation node followed by an S3 upload node, but the S3 upload node complains that `$binary.data` is empty. Which parameter on the media generation node controls where the generated binary output gets placed?

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-binary-and-data
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-binary-and-data")
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-binary-and-data

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-binary-and-data/SKILL.md https://app.decimal.ai/s/sickn33-n8n-binary-and-data/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/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-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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-binary-and-data")
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-binary-and-data/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-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data")
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-binary-and-data/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data

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-binary-and-data/SKILL.md https://app.decimal.ai/s/sickn33-n8n-binary-and-data/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sickn33-n8n-binary-and-data/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-binary-and-data/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sickn33-n8n-binary-and-data.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 Binary and Data

When to Use

Use this skill when an n8n workflow reads, transforms, stores, uploads, downloads, or transmits files and binary fields, including multimodal agent inputs and chat attachments.

Treat uploaded files and generated URLs as potentially sensitive. Obtain approval before sending data to a new external host, use the narrowest retention and access scope available, avoid logging bytes or base64 payloads, and do not embed credentials in URLs or workflow fields.

Every n8n item carries two independent slots: $json for structured data and $binary for file bytes. They travel side by side through the workflow. File contents — the actual PDF, image, or zip — live in $binary, never in $json. Get that split wrong and you read an empty field, lose a file mid-flow, or hand an AI agent a tool input it can't use.

This skill covers where binary lives, how to read and write it, how to keep it from being silently stripped, the hard wall between binary and the AI-agent tool boundary, and why chat surfaces need a URL instead of raw bytes.

Three common binary-data rules

File contents are in $binary, not $json. After an HTTP download, a "Read Files", or an email-attachment trigger, the bytes sit in $binary.<key>. $json holds metadata at most. Reading $json.data for file contents gives you nothing.
Binary cannot cross the AI-agent tool boundary — in either direction. Tool arguments and tool return values are JSON only. An uploaded image can't be passed into a tool as a file, and a tool can't return raw bytes. Pre-stage to storage and pass a key or URL through JSON instead. See references/AGENT_TOOL_BINARY.md.
Chat surfaces render images by URL, not by $binary. Slack, Discord, Teams, Telegram, embedded webhook chat — none of them read the binary slot. The image has to live somewhere a URL can fetch it. See references/CDN_REQUIREMENT.md.

The two slots

Each item is shaped like this:

{
  "json": { "customerId": 42, "status": "sent" },
  "binary": {
    "invoice": {
      "data": "<base64-encoded bytes>",
      "mimeType": "application/pdf",
      "fileName": "invoice-42.pdf",
      "fileExtension": "pdf"
    }
  }
}

The key inside binary (invoice here) is the binary property name. Most file-handling nodes have a binaryPropertyName parameter that points at it — the producer names the slot, the consumer references it by that name. The default key across most nodes is data, so when nothing tells you otherwise, assume $binary.data.

$json and $binary are separate namespaces. An expression like {{ $binary.invoice.fileName }} reads file metadata; {{ $json.customerId }} reads data. They never mix.

This split also explains a webhook gotcha: a Webhook trigger receiving multipart/form-data puts the uploaded file in $binary and the accompanying form fields in $json.body — so an uploaded file is not somewhere under $json at all. (The $json.body nesting for webhooks is n8n-expression-syntax territory.)

See references/BINARY_BASICS.md for the full slot anatomy, mime types, and size limits.

Producing binary

You rarely build a $binary slot by hand — nodes populate it for you:

Source	How binary appears
HTTP Request with `responseFormat: "file"`	Response body lands in `$binary.data` (or the name you set)
Read/Write Files from Disk	File contents read into `$binary`
Storage downloads (S3, Google Drive, Dropbox, etc.)	Downloaded file in `$binary.<key>`
Email triggers with attachments	Each attachment arrives in `$binary`
Provider AI media nodes (image/audio gen)	Set `options.binaryPropertyOutput` so the bytes land where the next node looks

For an HTTP download, the one field that matters is responseFormat. Confirm it with get_node on nodes-base.httpRequest — leaving it as the default JSON/string format is the classic reason a downloaded file ends up as garbled text in $json instead of clean bytes in $binary.

Reading and writing binary in a Code node

Most workflows never need to crack open the bytes — they just pass binary through to a consumer (email attachment, file upload, Slack file). When you do need the raw bytes, do it in a Code node.

Read with getBinaryDataBuffer — do not try to base64-decode $binary.<key>.data by hand:

// Code node, "Run Once for All Items"; single-item example only
const buffer = await this.helpers.getBinaryDataBuffer(0, 'data'); // (itemIndex, propertyName)
const text = buffer.toString('utf-8');
const length = buffer.length;

return [{
  json: { ...$json, length },
  binary: $input.item.binary,   // pass the binary through, or it's gone
}];

Write by building the slot yourself — base64 the bytes plus a mime type and file name:

const text = 'Hello, world!';
return [{
  json: { ok: true },
  binary: {
    report: {
      data: Buffer.from(text).toString('base64'),
      mimeType: 'text/plain',
      fileName: 'report.txt',
      fileExtension: 'txt',
    },
  },
}];

The Code-node sandbox, helpers, and execution modes are the domain of n8n-code-javascript (and n8n-code-python) — use those for the language-level detail. The one binary-specific thing to remember here: a Code node that returns [{ json: {...} }] without re-attaching binary silently drops the file. See references/BINARY_BASICS.md.

Keeping binary alive across transforms

JSON-only nodes — Edit Fields (Set), Code, IF, and others — can drop the $binary slot from their output. The workflow validates clean and runs without error; the file just isn't there downstream when the email node goes to attach it.

Two ways to keep it:

Pass-through option on the transforming node. Edit Fields has includeOtherFields; a Code node can return binary: $input.item.binary explicitly. Cheapest fix when it's available.
Fan out and Merge by position. Route the source into both the transform and a bypass branch, then recombine with a Merge in combineByPosition mode. The JSON comes from the transform side, the binary survives on the bypass side.

[Source with binary] ─┬─→ [Edit Fields: change JSON] ─┐
                      │      (binary stripped here)     ├─→ [Merge: combineByPosition] ─→ [Email: attach]
                      └──────────────────────────────────┘
                          (bypass — binary passes through untouched)

combineByPosition pairs item N from each input, so the field counts must line up. The connection wiring and the alternatives for many-strip-point chains (upload-early, sub-workflow) are in references/MERGE_FOR_CONTEXT.md.

The agent-tool binary boundary

This is the sharpest edge. An AI Agent talks to its tools (Custom Code Tool, Call n8n Workflow Tool, HTTP Request Tool, MCP tools) over JSON. Binary does not fit through that pipe in either direction. The fix is the same shape both ways: stage the bytes in storage, pass a key/URL through JSON, fetch on the other side.

Inbound — a user uploads a file the agent's tool must operate on:

The chat trigger gives you a files[] array. Split it out and upload each file to private storage under a hashed key.
Re-merge that branch before the agent runs (it's a synchronization barrier, not decoration), and set executeOnce: true on the agent so N files don't trigger N agent runs.
Inject the keys into the agent's system prompt, listing both the original name (human context) and the storage key (what the tool needs), with an explicit "use EXACTLY this key".
The tool receives the key as a string argument and downloads the file from storage itself.

Outbound — a tool generates a file the agent must return:

The tool sub-workflow generates the binary, uploads it to storage, and returns JSON like { "ok": true, "key": "...", "url": "https://...", "mimeType": "image/png" }.
The agent embeds the URL in its reply (or passes the key to another tool).

passthroughBinaryImages: true on the agent only changes what the LLM sees for vision — it does not let tools receive the file, and it's image-only (no PDFs, audio, or video). You still need the upload-and-pass-key pattern for any tool. Full patterns, hash strategy, storage choices, and the long-running-tool variant are in references/AGENT_TOOL_BINARY.md.

Building the tool itself? See n8n-code-tool for the Custom Code Tool contract and n8n-workflow-patterns for the AI-Agent-with-tools shape.

The CDN requirement for chat surfaces

When a workflow generates an image and the user wants it shown inside a chat message:

Binary on the item isn't enough. The chat client renders messages that reference images by URL (or pushes bytes through the platform's own file-upload API). It never reads $binary.
The bytes have to live somewhere a URL can fetch over HTTPS. Upload to an object store or drive first, then embed the returned URL.
n8n has no built-in CDN. The user provides the storage.

Ask which storage they already use rather than defaulting to S3 — object storage (S3, R2, GCS, Azure Blob, Backblaze B2, Supabase Storage) and drive-style services (Dropbox, Google Drive, OneDrive, Box) all work and all change the URL shape. Choose a backend only after checking requirements and current capabilities. For sensitive content, use a signed URL with an expiry rather than a permanently public one. See references/CDN_REQUIREMENT.md.

What's NOT available

$fromAI() cannot carry binary. It fills tool parameters with strings, numbers, booleans, and objects — never file bytes. Pass a storage key instead.
Tool arguments and returns are JSON only. There is no "binary parameter" on an agent tool, in or out.
n8n ships no CDN or public file host. Serving a file over a URL is always something the user's storage does, not n8n.
getBinaryDataBuffer is a Code-node helper. It isn't available in the Custom Code Tool sandbox (see n8n-code-tool).

Where Data Tables live

For persistent tabular storage — reference-counting staged files, tracking which keys are live, dedup — that's the n8n_manage_datatable surface, owned by n8n-mcp-tools-expert. This skill does not cover Data Tables.

Anti-patterns

Anti-pattern	What goes wrong	Fix
Reading file contents from `$json`	Bytes live in `$binary`; `$json` is empty or metadata only	Read `$binary.<key>`, or `getBinaryDataBuffer` in a Code node
HTTP download without `responseFormat: "file"`	Bytes arrive as mangled text in `$json`, not clean binary	Set `responseFormat: "file"` on the HTTP Request node
Code node returns `[{json:{...}}]`, no `binary`	The file is silently dropped downstream	Re-attach `binary: $input.item.binary` in the return
JSON transform (Edit Fields/IF) eats the binary	Email/upload node finds nothing to attach	Pass-through option, or fan out + Merge by position
Passing an uploaded file into a tool via `$fromAI`	`$fromAI` can't carry binary; the tool gets nothing	Pre-stage to storage, inject the key in the system prompt, tool fetches by key
Assuming `passthroughBinaryImages` lets tools see the file	It only affects what the LLM sees, and only for images	Still need the upload-and-pass-key pattern for tools
Tool returns raw binary to the agent	Tool output is JSON; bytes don't survive (and bloat context)	Upload, return `{ key, url }` in JSON
Posting `$binary` to a chat surface and expecting an image	Chat clients render by URL, not raw bytes	Upload to storage/CDN, embed the URL or use the platform file API
Hardcoding base64 in a Code node	Huge workflow JSON, slow, leaky	Reference via `$binary`, or upload and reference by URL

Reference files

File	Read when
`references/BINARY_BASICS.md`	First time handling binary, or reading/writing the `$binary` slot, mime types, size limits
`references/AGENT_TOOL_BINARY.md`	An agent tool needs an uploaded file, or produces one — the boundary in either direction
`references/MERGE_FOR_CONTEXT.md`	Binary disappears after a JSON transform and you need to re-attach it
`references/CDN_REQUIREMENT.md`	Showing images in a chat surface or anywhere that needs URL-referenced images

Integration with Other Skills

n8n-code-javascript / n8n-code-python: the Code node is where you read/write raw bytes (getBinaryDataBuffer, Buffer.from(...).toString('base64')). Those skills own the sandbox, helpers, and execution-mode detail — this skill owns the rule that binary must be re-attached on return.

n8n-code-tool: the Custom Code Tool sandbox is narrower — no $binary, no getBinaryDataBuffer, no $fromAI. When a tool needs a file, this skill's storage-key pattern is how it gets one.

n8n-workflow-patterns: the agent-tool binary boundary sits inside the AI-Agent-with-tools pattern; the CDN flow is a generate → upload → reply chain.

n8n-node-configuration: responseFormat, binaryPropertyName, includeOtherFields, binaryPropertyOutput are all conditional fields — use get_node to confirm the exact names on the user's version.

n8n-expression-syntax: addressing $binary.<key>.fileName vs $json.body (webhook uploads in particular) is expression territory.

n8n-validation-expert: a dropped binary slot is a silent failure — validate_workflow won't flag it. Confirm presence by inspecting the execution.

n8n-mcp-tools-expert: owns n8n_manage_datatable (Data Tables) and n8n_executions — use the latter to confirm a binary slot actually survived a given node.

n8n-error-handling: storage uploads and downloads fail; the inbound/outbound staging steps need error branches so a missing key doesn't 404 silently.

using-n8n-mcp-skills: the index of how these skills fit together.

Verifying binary survived

Validation won't catch a stripped binary slot — it's a silent failure. Confirm it ran correctly:

n8n_test_workflow (or trigger a real run) to produce an execution.
n8n_executions to pull that execution, and inspect per-node output for the binary slot — it shows presence and metadata even if the base64 is too large to render.
The node where binary last appears is the node before the strip. That's where the pass-through or Merge goes.

Quick Reference Checklist

File contents read from $binary.<key> — never $json
HTTP downloads use responseFormat: "file"
Code nodes re-attach binary on return when the file must continue
JSON transforms either pass binary through or Merge it back (combineByPosition)
No attempt to pass binary into/out of an agent tool — keys/URLs through JSON instead
passthroughBinaryImages used only for LLM vision, not as a tool channel
Chat-surface images uploaded to storage; the URL is embedded, not the bytes
Storage backend chosen with the user (not defaulted to S3); signed URLs for sensitive content
Binary presence confirmed by inspecting the execution, not by validation

Remember: two slots, side by side. Data rides in $json, files ride in $binary — and the moment a file has to cross an agent tool or reach a chat surface, it travels as a URL, not as bytes.

Limitations

Storage limits, binary modes, and node-specific field names vary across n8n versions and hosting configurations.
An n8n validation pass cannot prove that file bytes survived a live execution; inspect execution data with a safe sample.
This skill does not choose a storage provider or authorize uploading sensitive data to one.

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

sickn33 / antigravity-awesome-skills

skills/n8n-binary-and-data/SKILL.md

View on GitHub ↗

License MITImported 11/8/2026Version v2Bundled 4 files

Illustrative Want to see what sickn33/n8n-binary-and-data would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

n8n Binary and Data

When to Use

Three common binary-data rules

The two slots

Producing binary

Reading and writing binary in a Code node

Keeping binary alive across transforms

The agent-tool binary boundary

The CDN requirement for chat surfaces

What's NOT available

Where Data Tables live

Anti-patterns

Reference files

Integration with Other Skills

Verifying binary survived

Quick Reference Checklist

Limitations

SkillSafety

Source

Related skills

More in the registry