🌐 English | 繁體中文 📖 Why Code2n8n? Read the Code2n8n manifesto — why enterprises need n8n more in the AI-coding era, not less.

The Code2n8n positioning: AI Coding (Claude Code / Codex / Antigravity) is great at writing code. n8n is great at making code manageable by an enterprise. This pack is the bridge — describe a requirement, or point at an existing system (Apps Script / Express / Lambda / Docker stack), and get a runnable n8n workflow that IT, operations, and managers can all read, audit, hand off, and govern.

📊 The whole pack in one picture: Natural-language intent or an existing program system → Code2n8n Skill Pack (Cookbook + 2,061 reference workflows + DSL v1.2 + 15 manifest skills + 4 enterprise patterns) → decides what logic stays as code vs lifts into an n8n node → emits a reviewable, hand-off-able, cross-system n8n workflow. by n8n Taipei Ambassador Morris Lu

This pack does more than turn sticky notes into workflows. It supports two directions:

Path A: from zero
natural language / yellow sticky note
  → sticky-note-to-workflow
  → n8n workflow

Path B: port an existing system
Apps Script / Express / Lambda / Netlify Functions / Docker stack
  → code-to-workflow (inventory, dedicated security gate, partition, port, validate, version/rollback evidence)
  → code modules + n8n workflow + migration docs

Code2n8n does not transliterate every line of Python or JavaScript into nodes. It re-partitions the system: complex algorithms stay in code, while triggers, cross-system wiring, retries, human approvals, notifications, and execution history lift into a visible, manageable workflow.

AI Coding solves "how is the function built"; Code2n8n solves "how is the capability modularized and audited"; n8n solves "how the modules cooperate across the whole enterprise."

Full evidence table further down. The bar above is what immediately backs the two-path claim — if any of these three case studies disappear, the claim weakens.

🛠️ Responsibility boundary: The third block of the hero diagram ("n8n Enterprise Orchestration") lists SSO / IAM / audit log / HA — n8n Enterprise ships these out of the box, the Pack does not reimplement them. The Pack's job is to make sure Code2n8n-produced workflows land cleanly on top (IAM-friendly, queue-safe, rollback-traceable). The split between Pack / n8n Enterprise / your IT, and the workflow-design rules that follow, are in docs/enterprise-setup.md.

This entire project was authored using AI Agentic IDEs (Antigravity / Claude Code) — from spec to n8n workflows, every artifact was produced through human-AI agent collaboration.

This Skill Pack is itself a working demo of Agentic Engineering:

• skills/ — plugin.json registers 15 Claude Code / Antigravity skills; each SKILL.md is co-authored by humans and AI
• .agent/workflows/ — Antigravity-native agentic workflows (e.g. /install-n8n-pack one-shot installer)
• cookbook/ — 8 natural-language → workflow examples showing how to "talk to" the AI
• spec/sticky-note-three-layer.md — Three-layer structure spec that forces reviewable AI output
• research/patterns.md — 7 canonical skeletons + anti-patterns mined by AI from 2,061 real workflows
• reference-workflows/ — AI training corpus (Zie619/n8n-workflows, MIT, secrets scrubbed)

• Developers / PMs learning how to use an AI agent as an engineering teammate
• Teams who already have Apps Script, Express, Lambda, Netlify Functions, or a Docker stack and want to evaluate what stays in code vs what moves into n8n
• Teams evaluating whether Antigravity / Claude Code can replace hand-written skills / workflows
• Anyone curious what real human-AI co-authored engineering output looks like

💡 In other words: this isn't just "a Skill Pack for n8n" — it's also an open case study of how AI agents build a real product.

Once you install this Skill Pack, you can author your own n8n workflows with the same agentic approach — no node syntax to learn, no code to write:

Typical interaction (30-second mental model):

You ──> AI: "Every weekday 9am, pull Shopify orders, build a daily
             report, email it to the boss; on failure post to Slack #ops"

AI ──> You: ✅ workflow.json generated (Schedule → Shopify → Code → Email + Error → Slack)
             ✅ Yellow sticky: your original requirement, preserved
             ✅ Blue sticky: which credentials, constraints, test method
             ✅ Deployed to your n8n via API, webhook URL: https://...

🎯 The core idea: Users don't need to memorise n8n node syntax — clear requirements are enough to get a structured, reviewable, maintainable workflow. To claim it's production-ready still requires credential setup, live validation, and a security audit.

If you already have code, don't rewrite it into a sticky note. Just say:

"Use code-to-workflow to inventory this project, decide what stays in code vs moves to n8n; do the security audit first, then emit SDD, workflow, and validation results."

See 02-USAGE-MODES.en.md (three intent-driven modes) and 03-FIRST-WORKFLOW.en.md (15-minute hands-on); for porting existing code, go straight to code-to-workflow.

You drop a yellow sticky note on the n8n canvas and write (in any language):

Every day at 9 AM, fetch sales data and email the daily report to my boss.
On failure, notify Slack #ops.

You ask AI to build it. The canvas now shows a complete workflow:

┌─ Yellow sticky: your requirement (preserved as-is)
├─ Middle: AI-generated nodes (Schedule → HTTP → Code → Email)
└─ Blue sticky: AI's notes (credentials needed, assumptions, limitations, how to test)

No code. No syntax to learn. No need to memorize n8n node names.

The first three start from intent. The fourth starts from existing code. Full Code2n8n methodology: skills/tigerai/code-to-workflow/SKILL.md.

TigerAI-Code2n8n-Skill-Pack/
├── README.md / README.zh.md ← You are here
├── CODE2N8N.md              ← Code2n8n manifesto (positioning + thesis)
├── 01-INSTALL.md/.en.md       ← Install
├── 02-USAGE-MODES.md/.en.md   ← Three intent-driven usage modes
├── 03-FIRST-WORKFLOW.md/.en.md ← Hands-on tutorial
├── 04-FAQ.md/.en.md           ← Common questions
│
├── cookbook/                  ← 8 copy-paste recipes (each has plain-language + DSL fold)
│   └── 00-INDEX.md/.en.md
│
├── skills/                    ← 14 skill folders on disk; plugin manifest registers 15 entries
│   ├── _vendor/                  6 vendor n8n-skills (MIT)
│   └── tigerai/                  8 TigerAI execution skills
│       ├── code-to-workflow/        ← Marquee: existing code / system → n8n
│       ├── n8n-security-governance/ ← Security + version control + CI/CD + rollback gate
│       └── n8n-code-to-native/      ← Code node → native n8n nodes
│
├── spec/                      ← Technical specs (for engineers)
│   ├── sticky-note-three-layer.md
│   └── sticky-note-dsl.md
│
├── examples/google-workspace-admin-workflow/    ← 1,373-line Apps Script → n8n
├── examples/line-ai-customer-service/           ← Cloud LINE CS → n8n + admin UI
├── examples/line-ai-customer-service-onprem/    ← On-prem Docker + Qdrant RAG (NOT deployable as-is)
├── examples/tigerai-flagship/ ← 3 enterprise-grade examples (with SDD)
├── reference-workflows/       ← 2,061 public workflows (AI corpus)
├── research/                  ← Research artifacts
├── tests/                     ← Three rounds of acceptance reports
│
├── CHANGELOG.md / VERSION
├── LICENSE                    ← Pack-wide MIT license
├── install.sh / install.ps1   ← Install scripts (supports Claude Code & Antigravity)
├── .agent/workflows/          ← Antigravity-exclusive workflows (e.g., /install-n8n-pack)
└── plugin.json                ← Skill manifest

⚠️ plugin.json currently registers one extra maintenance skill, install-tigerai-n8n-pack, whose folder is not yet committed to the repository — that's why the manifest has 15 entries while the on-disk skills directory has 14. Either add the missing folder or remove the stale entry before the next release.

1. This file → 01-INSTALL.en.md → 03-FIRST-WORKFLOW.en.md
2. After your first workflow runs, browse cookbook/00-INDEX.en.md for your scenario
3. Stuck? → 04-FAQ.en.md

1. This file → 02-USAGE-MODES.en.md
2. Read tests/REPORT-3.en.md: historical acceptance baseline (v0.9.0 R3)
3. Browse any of the three Code2n8n case studies under examples/ for current evidence
4. Browse examples/tigerai-flagship/: enterprise-grade SDD examples

1. This file → spec/sticky-note-three-layer.md + spec/sticky-note-dsl.md
2. Porting existing code: skills/tigerai/code-to-workflow/SKILL.md
3. Security, version control, CI/CD, and rollback gate: skills/tigerai/n8n-security-governance/SKILL.md
4. Building from scratch intent: skills/tigerai/sticky-note-to-workflow/SKILL.md
5. skills/tigerai/n8n-api-bridge/SKILL.md: n8n REST API SOP
6. research/patterns.md: 7 standard skeletons + anti-patterns

1. Read CODE2N8N.md first to understand the "keep in code / lift to flow" split
2. Use code-to-workflow for source inventory, partitioning, and Step 1.5 security audit
3. Compare against the three empirical case studies: Google Workspace admin, LINE cloud, LINE on-prem
4. Pass static lint + n8n REST import, then end-to-end with real credentials
5. If security findings remain unfixed, per marquee skill hard rule §3, publish a SECURITY-CAVEATS.md — see the on-prem LINE CS example for the template

1. This file → run 01-INSTALL.en.md end-to-end
2. Read 04-FAQ.en.md to prepare for team questions
3. Hand the entire folder to teammates and ask them to start at this README

┌─────────────────────────────────────────────────────┐
│ 🟡 Layer 1 (yellow sticky): User intent              │
│    "Every day at 9 AM..."                            │
│    ← AI never modifies this. Always the source of    │
│      truth.                                          │
├─────────────────────────────────────────────────────┤
│    Layer 2: AI-generated nodes & connections        │
│    Schedule → HTTP → Code → Email                   │
├─────────────────────────────────────────────────────┤
│ 🔵 Layer 3 (blue sticky): AI's commentary            │
│    • Why each node was chosen                        │
│    • Required credentials                            │
│    • Assumptions and known limits                    │
│    • How to test                                     │
└─────────────────────────────────────────────────────┘

The third case deliberately preserves the upstream POC's security defects and documents them in SECURITY-CAVEATS.md. This isn't "failed acceptance swept under the rug" — it's Code2n8n's core principle: AI-written software that runs is not automatically software an enterprise can deploy.

The numbers below were the real-environment baseline for three-layer workflow generation as of v0.9.0 R3; the current pack version is v0.22.2, and the three Code2n8n case studies above are the v0.22.x evidence layer that supersedes pure-generation acceptance.

Details: tests/REPORT-3.en.md.

Current version: see VERSION. All changes: CHANGELOG.md.

The whole pack is now MIT-licensed. See the root LICENSE file.

• skills/_vendor/: MIT — from czlonkowski/n8n-skills, see skills/_vendor/LICENSE
• reference-workflows/: MIT — from Zie619/n8n-workflows. API tokens, bearer tokens, and other secrets present in the original files have been replaced with placeholders (e.g. YOUR_API_TOKEN_HERE) before redistribution.
• examples/line-ai-customer-service-onprem/: derived from MIT-licensed scorpioliu0953/ai_customer_service, attribution chain in the example's CREDITS.md.
• The rest (TigerAI-authored skills, cookbook, specs, docs, install scripts, manifesto, marquee code-to-workflow skill): MIT (Copyright (c) 2026 Morris Lu / TigerAI).

Full third-party notices: THIRD_PARTY_NOTICES.md.

Tell Claude / ChatGPT:

"I'm new to this. Following the TigerAI Skill Pack README, currently on [filename], hit [problem]."

The AI will diagnose. Or check 04-FAQ.en.md first.