Using AI to Write Shell Scripts That Automate API Documentation Builds and Publishing

Reference documentation such as Javadoc, Doxygen, or OpenAPI output is central to API documentation work, and it must be built, staged, and published with almost every release. Because these tasks are repetitive and error-prone, they are prime candidates for automation. Shell scripts, which run from the terminal and process files, are a simple way to reduce this tedium and free technical writers to focus on content quality rather than manual production steps. AI tools can accelerate the creation of these scripts, even for writers who are not expert programmers.

Consider a team that supports Java API documentation generated with Javadoc. Manually, the process involved retrieving a release build link from a build management system, configuring a build command, switching to a special branch, building reference output for multiple API variants, copying generated files into the documentation directory, inserting notes into pre-release builds, pushing content to a staging server, and setting up a changelist for engineering review. This took 10 to 15 minutes of careful, tedious steps. Each small content fix, such as correcting code links or adjusting comment wording, required repeating the entire sequence, making the process increasingly frustrating.

The author, a shell scripting novice, used AI tools to build the automation step by step over about two days. To protect confidential details, queries were written as generalized pseudo-code describing each piece of logic: running a command, capturing part of a response, storing values in variables, passing parameters to another command, unzipping output, copying files, and inserting notes into HTML. The AI returned working code most of the time and explained the shell concepts along the way. Pseudo-code prompts work well, and if the AI suggests tools unavailable in your environment, steer it back toward shell scripts or technologies your team already supports. Some technical familiarity is required, but technical writers often already have it or can learn it.

Many technical writing teams delegate reference documentation to engineers because the build configuration seems too complex. However, this removes writers from one of the most valuable areas of API documentation. Reference content is high value, and writers contribute meaningfully through work on field definitions, cross-references, incomplete descriptions, and clarity. Writers do not need to be developers to improve language-specific reference docs. Developers using an API are mainly interested in the data it returns, and that data is often poorly described. Automating build and publishing tasks with scripts lets writers remain key players in reference docs without losing energy to manual production.

Beyond reference builds, scripts can support many authoring and publishing tasks. Examples include generating new pages with the required frontmatter and sections based on document type, uploading images and returning ready-to-paste HTML, generating and publishing PDF versions, triggering builds from events such as closed bugs or code releases, identifying stale pages by correlating edit dates with analytics, notifying groups when documentation changelists are created, running linters for style, Markdown formatting, link validity, and image alt tags, converting formats with tools like Pandoc, archiving content after releases, and updating search indexes or pinging stakeholders after publishing. Combining scripts with other command-line tools expands these possibilities considerably.

Using AI for scripts has downsides. An experienced engineer may review AI-generated code and offer high-level suggestions, exposing that the writer understands the code only superficially. Writers must then decide whether to admit AI assistance and how to incorporate feedback. Heavy, constant reliance on AI can also lead writers to create scripts and apps they cannot explain, shortcutting the learning of syntax and logic. Choosing between learning a language thoroughly or relying on partial understanding for a one-time task is a legitimate decision, but it should be made consciously.

Over-reliance can also produce a sense of hollowness and apathy. When writers lose self-sufficiency, they may come to see themselves as intermediaries relaying instructions between people and machines rather than creators of information or code. This can lead to boredom and reduced care in the work. A practical remedy is to study the generated code carefully. Most AI tools explain their output, which makes the code a strong starting point for learning. Treating AI-generated scripts as a springboard rather than a finished answer keeps writers engaged and builds lasting expertise.

AI tools give technical writers scripting capabilities across shell scripts, Python, command-line interfaces, and more. Shell scripting is especially useful for automating tedious parts of the build and publishing process, which matters increasingly as docs-as-code and continuous documentation workflows become standard. Writers should pursue automation wherever possible while also taking responsibility for understanding the code they rely on, so that they remain capable, engaged, and central to the documentation process.