Every team has that one small, annoying chore no one talks about. It doesn’t break anything. It doesn’t threaten deadlines. It just sits there, quietly stealing minutes, breaking flow, and reminding developers that even simple work can feel messy.

For Maya’s team, that chore was cleaning up log files. Every morning, someone had to delete yesterday’s debug logs, archive the important ones, and reset the folder so their tooling didn’t choke on old data. It took three to five minutes. Not a catastrophe, but when multiplied across a week and across a team, it became a tiny tax on everyone’s routine.

One Wednesday morning, after repeating the same set of clicks for the third day in a row, Maya muttered, “There has to be a better way to automate this process and save me time.”

The Idea that Snowballed

Later that day, Maya opened her editor, determined to automate the cleanup. She was going to build a helper script that the team could drop into /scripts and forget about. She knew exactly what she wanted:

• Delete all *.logfiles older than 24 hours
• Keep files tagged as important
• Compress the rest into a simple archive
• Run silently unless something fails

This was the kind of task everyone always says they’ll automate someday but never do. However, this time, Maya decided to use her Continuous AI to accomplish these tasks.

Prerequisites

To follow along practically with Maya’s approach, ensure your environment is set up as follows:

• Continue extension installed in your VS Code
• A local or cloud LLM that can handle agent mode loaded in your Continue Extension. Check out how to load an LLM locally.
• Knowledge of JavaScript and NodeJS

If you don’t have these tools configured yet the necessary or knowledge, that’s fine; you can still follow Maya’s story, which focuses more on the underlying concepts of automation and collaboration than the setup details.

Implementing the Solution with Continue.dev

Step 1: Initializing the Environment

Maya creates a repository and initializes a node project in it:

npm init --y

Then she creates a file named cleanup.js inside her new repository. This is where Maya’s script will be written.

Step 2: Utilizing Continue Agent Mode

She hit the Continue.dev extension, picked a model, selected agent mode, and typed: “Generate a small Node.js script in cleanup.js that cleans the logs, tmp, and .cache folders safely. Add dry-run support and meaningful console output.”

Step 3: The Generated Logic

Continue responded instantly with a scaffold Maya would normally spend ten minutes writing. Then it added safeguards she hadn’t thought of, such as checking whether the directory exists, handling unreadable files gracefully, and confirming write permissions before attempting deletion.

The Script Logic: How it works

The script generated by Continue is small, but it packs a lot of convenience into a few lines. This tiny cleanup tool does one job: remove common clutter folders from your project, things like temporary files, logs, and caches. It runs safely, gives clear feedback, and supports a dry-run mode so you can preview what would happen before deleting anything. Here is a quick explanation of the code:

a. It defines what clutter to remove: The script starts by listing the folders that usually accumulate junk:

const TARGETS = ["logs", "tmp", ".cache"];

You can add or remove directories at any time—this is the “cleanup list.”

b. It checks if you want a dry run: It looks for the –dry-run flag when you run the script:

const isDryRun = process.argv.includes("--dry-run");

• If --dry-runis present, nothing gets deleted.
• Instead, it prints what would have been removed. This makes the script safer and beginner-friendly.

c. It tries to remove each directory safely: All deletion logic lives in one helper function:

function removeDirectory(dirPath) {
if (!fs.existsSync(dirPath)) {
console.log(`⏭️  Skipped: ${dirPath} (not found)`);
return;
}

If a folder doesn’t exist, the script skips it gracefully without throwing any errors or crashes. d. It respects dry-run mode: Before deleting anything, the script checks:

if (isDryRun) {
console.log(`💡 DRY RUN: Would remove ${dirPath}`);
return;
}

This ensures you’re always aware of what will happen. e. It deletes the directory (only when safe): deletion uses Node’s modern removal function:

fs.rmSync(dirPath, { recursive: true, force: true });
console.log(`🧹 Removed: ${dirPath}`);

• { recursive: true } allows removing folders with content
• { force: true } avoids errors if files are locked or missing Everything is handled cleanly with a single command. f. Processes each target folder: The main logic loops over every folder in TARGETS:

TARGETS.forEach((target) => {
const fullPath = path.join(process.cwd(), target);
removeDirectory(fullPath);
});

• process.cwd() ensures it cleans your current project
• Each folder is resolved, then passed to the cleanup function This keeps the script small but effective.

g. Prints friendly, meaningful feedback: Each run starts and ends with simple messages:

console.log(isDryRun ? "🔍 Running in dry-run mode..." : "🧼 Running cleanup...");
console.log("✨ Done!");

Documenting the tool

The script worked perfectly, but Maya wanted the whole team to use it easily. She leveraged Continue’s context-awareness to generate a README.md instantly: “Generate a short README explaining how to run this script.” Again, the agent produced a clean, copy-ready document with usage examples, requirements, warnings, and optional flags she could add later.

Maya pushed the update and shared the repository link in the team chat. This script was a small win, which didn’t change the project architecture or optimize performance but removed friction and benefited everyone.

Why These Tiny Wins Matter

In reality, not every improvement has to be a grand rewrite or a flashy migration.

• Developers spend far less time coding than assumed: Multiple industry studies show that only 20–30% of a developer’s day is spent writing new code; the majority goes to reviews, tooling friction, debugging, documentation, and coordination.
• Interruptions are disproportionately expensive: Research summarized by Atlassian and Microsoft shows that interruptions can double the time it takes developers to complete tasks and increase defect rates. Even the smallest recurring tasks can impose mental overhead, context switching, and re-orienting, which directly reduces productivity.

Small time savings compound quickly: A 5-minute daily task equals ~2 hours per developer per month. Hence, removing short, recurring tasks quietly recovers hours of focus time per engineer over a sprint.

Lowering the automation barrier changes team culture: When automation is easy, developers stop tolerating friction and start fixing it. Continuous AI significantly reduces developer effort, enabling teams to automate by default.

In Summary

While this guide is written using a developer persona to illustrate the workflow, every step described reflects how I personally use Continue to automate repetitive development tasks and improve productivity.

A script that takes a couple of minutes to put together can save hours over time. That’s often how automation starts, small, practical helpers that quietly reduce friction. The more you practice noticing these moments and turning them into scripts, the sharper that skill becomes. Tools like Continue fit naturally into that workflow, helping you create and share as you go.