Jekyll

Creating a Static Knowledge Base with Jekyll

Build a structured, scalable knowledge base using Jekyll. Learn how to organize content, enable internal linking, and create a durable static documentation system.

Brandon Himpfen

13 Apr 2026 — 4 min read

Photo by matthew Feeney / Unsplash

A knowledge base is not just a collection of articles. It is a system for organizing, retrieving, and maintaining information over time. When it is done well, it reduces duplication, improves clarity, and creates a durable layer of understanding across projects.

For the Himpfen platform, a static knowledge base aligns with a broader goal. Content should be structured, reusable, and independent of any single tool or interface. This is where Jekyll becomes a practical choice.

Why a Static Knowledge Base

Most knowledge bases are built on dynamic systems. They rely on databases, dashboards, and complex plugins. That works at scale, but it introduces friction.

A static knowledge base removes that complexity. Content becomes files. Structure becomes folders. Deployment becomes predictable.

This approach has a few important properties.

It is version controlled. Every change is tracked through Git.

It is portable. The entire knowledge base can move across platforms without reconfiguration.

It is fast. Static files are served directly without runtime processing.

It is durable. There are fewer dependencies that can break over time.

For a platform like Himpfen, where content spans multiple domains such as travel, technology, and systems thinking, this kind of stability matters.

Why Jekyll

Jekyll is simple by design. It converts Markdown files into a static website using layouts and templates.

It also integrates cleanly with GitHub Pages, which makes publishing straightforward.

More importantly, Jekyll encourages structure. Collections, front matter, and layouts create a natural framework for organizing knowledge.

If you are already working with static sites on the Himpfen platform, this fits naturally alongside other projects such as tools, datasets, and long form analysis.

Structuring the Knowledge Base

A knowledge base should not feel like a blog. It should feel like a reference system.

Start by defining collections instead of relying on posts.

# _config.yml
collections:
  kb:
    output: true
    permalink: /kb/:path/

This creates a dedicated content type for knowledge base entries.

Each article lives in a _kb folder:

_kb/
  getting-started.md
  jekyll-setup.md
  content-structure.md

Each file includes front matter that defines its metadata.

---
title: "Setting Up Jekyll for a Knowledge Base"
description: "How to initialize and structure a Jekyll-based knowledge base."
category: "setup"
tags: ["jekyll", "knowledge-base"]
---

This metadata becomes important later for navigation, filtering, and internal linking.

A knowledge base needs consistent presentation. Readers should always know where they are and how to move to related content.

Create a dedicated layout:

<!-- _layouts/kb.html -->
<!DOCTYPE html>
<html>
<head>
  <title>{{ page.title }}</title>
</head>
<body>
  <nav>
    <a href="/kb/">Knowledge Base</a>
  </nav>

  <article>
    <h1>{{ page.title }}</h1>
    <p>{{ page.description }}</p>

    {{ content }}
  </article>
</body>
</html>

Then apply it to all knowledge base entries:

---
layout: kb
---

From here, you can build category pages, tag indexes, and cross references.

Internal Linking as a System

A knowledge base becomes valuable when entries connect to each other.

Instead of writing isolated articles, think in terms of relationships.

For example:

A page on Jekyll setup should link to deployment workflows.
A page on content structure should link to SEO and metadata practices.
A page on datasets should link to how they are consumed in applications.

In Jekyll, internal linking is simple.

See also: [Content Structure](/kb/content-structure/)

Over time, these links form a network rather than a list.

On the Himpfen platform, this approach aligns with how hub pages are built. Articles should not stand alone. They should reinforce a broader system of understanding.

Adding Search Without Complexity

Search is often the reason people choose dynamic systems. However, a static knowledge base can still support it.

A simple approach is to generate a JSON index:

[
{% for doc in site.kb %}
{
  "title": "{{ doc.title }}",
  "url": "{{ doc.url }}",
  "content": "{{ doc.content | strip_html | escape }}"
}{% unless forloop.last %},{% endunless %}
{% endfor %}
]

This file can then be used with a lightweight client side search library.

The result is a fast, dependency light search experience that does not require a backend.

Deployment and Maintenance

Once set up, deployment becomes straightforward.

Push changes to your repository. GitHub Pages or your hosting pipeline builds the site automatically.

Maintenance shifts from managing infrastructure to improving content.

This is an important distinction. A knowledge base should evolve through refinement, not constant rebuilding.

Integrating with the Himpfen Platform

The knowledge base should not exist in isolation. It should connect to the rest of the platform.

On Himpfen, articles can reference deeper knowledge base entries when a topic requires more detail.

For example, a technical article about building data pipelines can link to a knowledge base entry explaining JSONL structures or validation approaches.

You can also surface knowledge base content through hub pages. This reinforces the idea that the platform is a connected system rather than a collection of separate sites.

If you are building or expanding your own implementation, you can explore the Himpfen Knowledge Base here:

Home | Brandon Himpfen Knowledge Base

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