n8n-agents
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain,…
Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular
Claude Code
$ npx -y skills add czlonkowski/n8n-skills --skill n8n-subworkflows --agent claude-code
Codex
$ npx skills add czlonkowski/n8n-skills --skill n8n-subworkflows -a codex -y
Cursor
$ npx skills add czlonkowski/n8n-skills --skill n8n-subworkflows -a cursor -y
Gemini CLI
$ npx skills add czlonkowski/n8n-skills --skill n8n-subworkflows -a gemini-cli -y
GitHub Copilot
$ npx skills add czlonkowski/n8n-skills --skill n8n-subworkflows -a github-copilot -y
OpenCode
$ npx skills add czlonkowski/n8n-skills --skill n8n-subworkflows -a opencode -y
Windsurf
$ npx skills add czlonkowski/n8n-skills --skill n8n-subworkflows -a windsurf -y
name: n8n-subworkflows description: Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular workflows, "Define Below" inputs, waitForSubWorkflow, mode each vs all, or exposing a workflow as an agent tool. Covers typed sub-workflow inputs, all-vs-each execution, verb-first naming for discovery, stateless vs stateful design, and splitting by input shape.
A sub-workflow is a reusable function. An **Execute Workflow Trigger** declares typed inputs, the body does the work, and the last node returns the output. A caller invokes it through an **Execute Workflow** node like any other step.
That framing buys you the things functions buy you everywhere: encapsulation, reuse, testability, replaceability. It's the primary reuse mechanism in n8n, and it's badly underused. Without it, the same logic gets copy-pasted across workflows — then a bug gets fixed in two places, the third copy gets missed, and your "identical" copies quietly drift apart.
This skill is about when to reach for a sub-workflow, how to define its input/output contract so callers (and agents) can actually use it, how to call it correctly (`all` vs `each`, blocking vs fire-and-forget), and how to name it so it gets found instead of rebuilt.
---
Everything else is judgement. These two are not.
Before you write logic for a generic problem, check whether a sub-workflow already does it. The community MCP can't filter workflows by tag, so the **name is the discovery surface**:
n8n_list_workflows() # scan the library
n8n_get_workflow({ id: "<candidate>" }) # read its inputs/outputs + bodyIf something fits, use it and tell the user ("I found `Subworkflow: Parse RFC2822 date` — using that"). If nothing fits, build it *with a discoverable name* so the next search finds it. The discovery convention (verb-first prefixes) lives in **NAMING_AND_DISCOVERY.md**.
The trigger has two input modes. **Default to "Define Below"** with explicit typed fields. Define Below is the only mode that gives callers a schema to fill — it's what lets an AI agent pass values via `$fromAI` and what lets structured callers map fields cleanly. Passthrough has no schema, so the trigger can't be wired as a clean agent tool and structured callers have nothing to bind to.
Two exceptions, and only two:
Outside those two cases, passthrough is a bug. See "Inputs and outputs as a contract" below.
---
You're about to write a chunk of logic. Run it through this:
Could this plausibly be needed in another workflow?
└─ Yes → extract.
Is it a generic concern (auth, retry, parsing, formatting, ID generation)?
└─ Almost always → extract. These are the canonical reusable sub-workflows.
Is it >5 nodes and conceptually one thing?
└─ Probably extract, even if reuse isn't certain. It's better isolated.
Is it one HTTP call with no logic around it?
└─ Don't. A sub-workflow that's just trigger → HTTP → return adds a boundary
for nothing.
Is it tightly coupled to this one caller's data shape?
└─ Don't extract yet — fix the data shape first, or you just relocate the coupling.The reasons to extract go beyond reuse:
A 20-node workflow is fine *if it's mostly a linear sequence of Execute Workflow calls and decisions* — each node has one purpose, and you inspect a section by opening the sub-workflow it calls. A 20-node workflow of inline transformations is not fine. If yours has 15+ nodes and isn't mostly sub-workflow calls and branches, extract more.
---
Both are first-class. The choice is about intent and what the contract promises.
**Stateless** — input in, output out, no I/O beyond that. The default for pure logic. When you need it again, you call it without worrying about side effects firing.
**Stateful (deliberate)** — reads or writes external state *behind a clean contract*. This is the repository pattern: the sub-workflow abstracts the storage operation so callers think in domain terms, not SQL.
Why build these as sub-workflows: callers think `get customer by id` instead of writing the query; you can swap the store (Postgres → Supabase, native node → HTTP) w
Expert Claude Code skills for building flawless n8n workflows using the n8n-mcp MCP server
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain,…
Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads,…
Write, review, or debug JavaScript in an n8n Code node. Use when a Code node is already in the workflow (editing it, fixing an…
Write Python in n8n Code nodes (native Python, `language` pythonNative, n8n 2.x). Use when the user explicitly wants Python in a…
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the…
Wire n8n error handling so failures are loud, structured, and recoverable. Use when building any webhook/API workflow, a…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…