Setting Up a Local Reference System
You grab the code from GitHub, drop it into your project folder, run npm install, and hope it just works. That usually doesn't happen on the first try. I spent three hours last month debugging a build failure that came down to Node 16 being installed but the package requiring 18. The error message said nothing about the Node version. It just timed out with a generic ENOENT error that sent me chasing missing files for half an hour before I finally looked at the engines field in package.json and checked node -v. That's the first thing I check now before doing anything else. It's a static reference generator that takes structured data and outputs formatted pages you can host anywhere. The data lives in JSON or Markdown files, the build process compiles everything into clean HTML with navigation and search built in, and you push the output folder to whatever hosting you're using. That's it. No database, no runtime server, just files. People overcomplicate this by trying to integrate it into complex workflows before they've even got a basic build running successfully. Just get the hello-world output working first. Verify the build command produces a folder with index.html in it. Everything after that is incremental improvement. This is where things get annoying. The config file controls how your data renders, what fields display, how the navigation is structured, and whether you get a search index built. A minimal config looks something like this:
{ The source field points to wherever your JSON or Markdown data lives. The output field is where the compiled site goes. Set search to false if you're processing datasets over a few thousand entries — the search index generation adds significant build time and memory usage. I learned that when my CI pipeline started failing because the build container ran out of memory on a dataset with 8,000 reference entries and full text search enabled. Disabled search, build time dropped from 47 minutes to 6, and the site still worked fine for navigation.
"site": {"title": "My Reference", "root": "/"},
"data": {"source": "./data/", "format": "json"},
"build": {"output": "./dist/", "search": true, "nav": "auto"}
}
How the Data Structure Works
Each data file represents a category or topic area. A typical entry has a title, a set of key-value pairs for the reference content, optional tags for filtering, and a slug for clean URLs. You don't need every field. Title and content are the only required ones. Everything else is optional. But skipping tags means you lose the client-side filtering feature entirely, and skipping slugs means the system generates URL slugs from your titles, which breaks if your titles have special characters or spaces that don't encode cleanly. I once had a dataset where 12 percent of the entries failed to generate clean URLs because the titles contained ampersands and parentheses. The build didn't fail. It just produced broken links that were nearly impossible to trace back to the source entries without a script. I wrote a quick Node script that iterated through all the entries, printed the ones with problematic characters in their titles, and flagged them for cleanup. Saved me from spending hours manually cross-referencing broken URLs against the data files.
Get the Full Details
Running the Build
Once your data and config are in place, you run the build command from the project root. The output goes to the folder you specified. Inside that folder you'll find index.html, a search index file if you enabled it, CSS and JS assets, and one HTML page per data entry organized into a directory structure that mirrors your data source. That output folder is what you deploy. It's entirely static. Netlify, GitHub Pages, S3, an internal CDN — it doesn't matter. The files work the same everywhere. Build times vary enormously depending on dataset size and whether search is enabled. A dataset of 200 entries with search takes about 3 seconds on a modern machine. 2,000 entries with search takes roughly 40 seconds. 10,000 entries with search will push past 3 minutes and consume around 800MB of RAM during the build. Without search, those same 10,000 entries finish in about 20 seconds and use roughly 200MB. If your datasets are growing, disable search and implement a server-side search alternative instead. This approach won't scale past a certain point with the search feature turned on.
Known Limitations
It doesn't handle relational data well. If your reference entries need to link to each other in meaningful ways — like a methodology document that references three other documents within the same site — you end up writing manual link markup in your data files. The system doesn't resolve cross-references automatically. I worked around this by adding a links array to my data schema and writing a small post-build script that replaced placeholder strings in the content with actual HTML anchor tags pointing to the relevant slugs. Took an afternoon to write, saved me from manually updating hundreds of entries whenever URLs changed. The templating system is also pretty rigid. You can customize the HTML structure, but the customization options are limited to what the template engine exposes. If you need dynamic content that changes based on user input, browser state, or external API calls, this isn't the right tool. It's designed for static reference material. Period. Trying to bend it into a dynamic application just creates more work than building something appropriate from scratch. Another thing nobody mentions upfront: the generated CSS is unminified by default. For small sites this is fine. For a site with 5,000 pages and heavy traffic, that uncompressed stylesheet becomes a real problem. Run a minification step in your deployment pipeline. Terser or cssnano will cut the CSS payload by roughly 60 percent. I added a single npm script for this and it became part of my standard build flow.
Deployment Notes
Don't commit the output folder to version control. Keep only your data files, config, and source code in your repository. Generate the output on every deploy. This keeps your git history clean and ensures you're always serving freshly built pages. If you do commit the output, you'll eventually have conflicts when multiple people update data files at the same time, and merging compiled HTML is not a fun experience. Set up a simple CI workflow that runs the build and pushes the output folder on every merge to main. GitHub Actions handles this in about 10 lines of YAML. Netlify has built-in support if you point it at the source directory and configure the build command and output directory in the dashboard settings. Either approach works. The manual deploys I used to do before setting this up were a waste of time.
Getting Started With the Ultimate Guide Cheat Sheet
Clone the repository, copy the example data and config files into your project, modify the config to point at your own data directory, and run the build command. If it produces an output folder with index.html, you're past the hardest part. Everything after that is just adding your own content and iterating on the configuration. The learning curve is steeper at the beginning because the documentation assumes you already understand static site generators, but once the build is working, adding new reference material is straightforward. The bottleneck is always data preparation, not the tool itself. The real value shows up when you have a growing body of reference material that needs to stay accurate and searchable without maintaining a database or running a server. That's a common enough scenario in technical teams that this tool fills a genuine gap. It's not the best solution for everything. But for structured static reference content, it does the job reliably once you get past the initial setup friction.
