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v0.22.0 — Marquee code-to-workflow skill + on-prem LINE CS case (MIT)

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@MorrisLu-Taipei MorrisLu-Taipei released this 10 Jun 11:56
· 23 commits to main since this release
v0.22.0
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v0.22.0 — Marquee skill code-to-workflow + on-prem LINE CS case (MIT)

Fulfils the CODE2N8N.md promise: the Skill Pack now actually contains the marquee Code2n8n skill the manifesto advertises.

🎯 New marquee skill: skills/tigerai/code-to-workflow/

The methodology for turning any existing program / system (Apps Script, Netlify Functions, Express, Docker stack, …) into a governable n8n workflow, distilled from the three real case studies in this pack.

Triggers: "把這個 repo 移到 n8n" / "Code2n8n 一下" / "port to n8n" / "幫我把這個 Lambda 改成 workflow" / "我的 Python 腳本想丟給營運維護"

7-step methodology:

  1. Source inventory — entry points, side effects, external calls, data stores, UI/backend split
  2. Partition rubric — what stays as code-as-service / what becomes a node / what becomes a connection
  3. Core + entry architecture — for multi-channel systems, one core, N entries
  4. Frontend portability decision tree — A keep original / B point original at n8n / C n8n-hosted spartan UI
  5. Native-first workflow design — declarative nodes everywhere except annotated HTTP fallbacks
  6. 3-layer validation funnel — static lint + n8n REST import + Layer 3 live execution
  7. Documentation outputs recipe — SDD / FRONTEND-SDD / PROVENANCE / FIELD-MAPPING / CREDITS / TODO

Real-world gotchas catalogue covering ~15 patterns across deployment (port collision, shared DB credentials, global Redis), Node/build (Express v5 wildcards, ESM tsx, sandbox crypto), LINE/messaging (raw-body signature, reply-token TTL, multi-event webhooks), Google Workspace (Docs paragraph style, Sheets dropdown, frozen header), and AI/RAG (GPT-5 Responses API, no native Gemini, Ollama Docker network names, Switch-on-active_ai RAG paths).

Hard rules: never strip upstream license, scrub secrets before commit, don't claim n8n can replace a UI, preserve fidelity with PROVENANCE.md pinned to a commit SHA, never declare done until Layer 1+2 both pass, tag every local n8n import with [Claude YYYY-MM-DD].

Registered as tigerai / role: marquee. Skills count 13 → 14.

🆕 New case study: examples/line-ai-customer-service-onprem/

An MIT-licensed practice case that walks the full Code2n8n pipeline end-to-end. Took scorpioliu0953/ai_customer_service (upstream cloud version, MIT) and evolved it into a full on-prem Docker stack:

  • Infrastructure: Postgres + Redis + Qdrant + Ollama containers, replacing Supabase entirely
  • Knowledge: Qdrant vector RAG instead of plain reference file
  • Auth: real-user accounts in a Postgres users table, replacing the shared-token shim
  • Brain: 37-node n8n workflow with Switch on active_ai → three RAG paths (OpenAI / Gemini / Ollama local LLM)
  • Validation: a 5-phase V&V plan (Infra / API / UI / HMR / E2E) with real PASS records
  • Lessons learned: 5 real port-time issues, captured in docs/LESSON_LEARNED.md (port collision, Express v5 wildcard, ESM tsx, shared DB credentials, global Redis)

Ships docs, SDD, DEV_LOG, LESSON_LEARNED, WALKTHROUGH_N8N, supabase_schema.sql, docker-compose + Dockerfiles, the full React + Express src/ tree, and the 37-node n8n_workflow_export.json.

License + attribution: upstream MIT preserved verbatim; CREDITS.md records the full chain (upstream → Morris Lu's on-prem evolution → this pack). Before committing, one hard-coded OPENWEBUI_API_KEY was scrubbed and n8n-backup/creds_backup.json was deliberately excluded.

Sits side-by-side with the existing examples/line-ai-customer-service/ (the cloud version we built in v0.19.0). Readers see the same upstream system on two different Code2n8n paths — cloud-minimum vs on-prem-enterprise — and can choose which fits their environment.

📝 Surrounding updates

  • CODE2N8N.md — surfaces the marquee skill and reorganises the skill / case-study layering
  • Cloud LINE CS README — adds a banner pointing to the on-prem variant
  • plugin.json description — adds marquee + the three case study names
  • VERSION → 0.22.0

The thesis (one line)

AI Coding solves "how is the function built"; code-to-workflow (this release's marquee skill) solves "how is the capability modularised"; n8n solves "how the modules cooperate across the whole enterprise."

Read the full manifesto.

🤖 Generated with Claude Code

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