Automating Repetitive Development Tasks with Scripts

I still remember the project. Every time I started a new microservice, I had to manually create the same six directories, copy over three boilerplate config files, run `npm install` on the same five packages, and then initialize a git repository. It only took five minutes, but I was doing it multiple times a day. It was five minutes of pure, mind-numbing repetition that chipped away at my creative energy before I even wrote a single line of meaningful code.

The turning point: why I started scripting

Honestly, the initial push wasn\'t about saving time. It was about a mistake. One late evening, I forgot one of the config files, and it took me a frustrating hour to debug why the new service wouldn\'t connect to the database. That was my \'aha\' moment. The problem wasn\'t the task\'s duration; it was its fragility. It was a process begging for human error. I realized that spending thirty minutes writing a script to do it for me wasn\'t a cost; it was an investment in consistency and my own sanity.

My first simple scripts: where to start

I didn\'t try to build some all-encompassing automation framework. My journey started small, with the tools I already had in my terminal. I think this is the most effective way to begin, as it builds on knowledge you already possess.

Bash for simple file operations

My first script was a simple Bash script. It was barely ten lines long. It created the directory structure, used `touch` to create empty files, and `echo` to pre-fill a README. It wasn\'t elegant, but it was reliable. Every new service was now identical. That small win was incredibly motivating. It showed me the power of encoding a process into a repeatable, error-free command.

Python for more complex logic

Soon after, I needed to process a set of log files to find specific error patterns. Doing a `grep` was getting complicated, and I needed to count occurrences and format the output. This is where I turned to Python. Its file handling and data manipulation libraries made it perfect for tasks that were a bit too complex for a simple Bash one-liner. This taught me to choose the right tool for the job: Bash for orchestration and file system tasks, and a language like Python or Node.js for data processing and API interactions.

How I leveled up my automation game

Once I got comfortable, I started seeing automation opportunities everywhere. I created command-line aliases in my `.zshrc` file for my most common Git command sequences. Instead of typing `git add . && git commit -m \"wip\" && git push`, I just type `gpush`. This seems trivial, but reducing the friction for common actions keeps me in a state of flow. I also began leveraging the scripting capabilities built into tools I already used, like adding scripts to my `package.json` file to run tests, linters, and build processes with a single command.

The biggest mistake I made (and how you can avoid it)

My biggest pitfall was over-engineering. Flushed with success, I tried to write a single, monolithic script to handle every possible project variation I might ever encounter. It became a tangled mess of conditional logic that was harder to maintain than the manual process it replaced. The lesson was crucial: automate the 80% of the task that is always the same. Keep your scripts focused on solving one specific, recurring problem. Complexity is the enemy of good automation.

The real payoff isn\'t just time

Today, my library of personal scripts is one of my most valuable assets. Yes, they save me time. But more importantly, they save me mental energy. By automating the repetitive, mechanical parts of my job, I\'ve offloaded the cognitive burden of remembering tedious steps. This frees up my mind to focus on what I actually love doing: solving complex problems and building great software. That, I’ve found, is the true power of automation.