FlowformLab AI Ghostwriter — Build Log
Project Log: n8n Ghost Writer Automation with Local LLM (Ollama)
Summary
This entry documents the initial build and iterative refinement of the
Ghost Writer automation pipeline for FlowformLab — a local,
privacy-preserving content generation workflow powered by n8n and
Ollama.
The objective is to accelerate first-draft creation while preserving
personal narrative, structure, and editorial control.
The pipeline now accepts a topic, generates structured headings, TL;DR and diagrams, writes the article body, and merges outputs into FlowformLab-ready Markdown.
Work Timeline
| Date | Milestone | Key Activities | Output | 
|---|---|---|---|
| Sat, 1 Nov 2025 | Kickoff | Defined goals & writing constraints. Set up n8n / Ollama baseline. | Project charter + architecture outline | 
| Sun, 2 Nov 2025 | Headings Engine | Built 3-heading generator sub-workflow, JSON parser + fallbacks | Stable headings module | 
| Mon, 3 Nov 2025 | TL;DR + Diagram Engine | Added TL;DR + Mermaid diagram subflow w/ resilience to parsing errors | Summary + diagram generator | 
| Tue, 4 Nov 2025 | Body Writer + Merge | Built body writer, added Markdown cleanup + JSON recovery, final merge logic | ✅ End-to-end pipeline working | 
Total time invested: ~4 days (iterative)
Milestone achieved: Fully automated topic → structured draft flow
Deliverables Completed
- ✅ Topic input node\
 - ✅ 3-heading generator + JSON normalization\
 - ✅ Auto-pick + manual override capability\
 - ✅ TL;DR + Mermaid diagram generator\
 - ✅ Body writer workflow\
 - ✅ Markdown assembler with safety checks\
 - ✅ Output: FlowformLab-ready draft (
article_md) 
Architecture Overview
[ Input ] 
   ↓
[ Headings Subflow ]
   ↓ pick
[ TL;DR + Diagram Subflow ]
   ↓
[ Body Writer Subflow ]
   ↓
[ Merge & Clean ]
   ↓
[ Draft Markdown Output ]
Risks & Mitigation
| Risk | Description | Likelihood | Impact | Mitigation | 
|---|---|---|---|---|
| Model drift | LLM breaks JSON or returns unexpected formats | Medium | Medium | Strong prompt guardrails, fallback defaults, JSON repair + post-parsers | 
| Pipeline fragility | Parsing or mapping breaks with unusual model output | Medium | High | Unified normalizer functions + stricter schema validation | 
| Style drift | Output tone doesn’t match FlowformLab voice | Medium | Medium | Style constraints + manual edit pass | 
| Over-automation | Losing personal narrative/voice | Low | High | Human edit always required — tool accelerates, doesn’t replace thinking | 
| Tooling changes | Ollama / n8n upgrades changing behaviors | Low | Medium | Version pinning + regression test prompts | 
Next Steps (Backlog)
| Priority | Task | Notes | 
|---|---|---|
| High | Connect draft output to Decap CMS | Commit drafts to /_drafts | 
    
| High | Add “style selector” input | Reflective / Technical / Audit-PMO | 
| Medium | Create YAML front-matter template system | Consistent FlowformLab layout | 
| Medium | Mermaid diagram refinement layer | Label style consistency + readability | 
| Low | Long-form structuring mode | Sections, ToC, research support | 
Reflection
💡 This pipeline now acts as a personal strategic writing co-pilot — not a content spawner.
I remain the author — this tool accelerates thinking and structure, but FlowformLab stays human-led.
The system proved stable, resilient to malformed JSON, and extensible — great foundations for future automation layers.