Skip to main content

Always know where you stand: Setting up a live status line in GitHub Copilot CLI

Yesterday I introduced to you statusline command. It allows you to configure a persistent, live bar at the bottom of your CLI session that shows whatever your script prints — token usage, context percentage, current model, cost estimates, session duration, and more.

I promised that we would setup something like this:

 █████░░░░░ 50% 64.0k/128.0k | ✱ Sonnet 4.5 | ~$0.04 | ⏱️ 00:12:34

But yesterday we only got as far as showing this:

Hello from PowerShell status line!

If you missed the previous post, go read it first, before continuing here. Back? Let's continue...

Now we have confirmed the script runs and we've the payload, we can now focus on creating a production-ready version. It extracts context usage, model name, cost (from the payload if available, estimated otherwise), and session duration.

I first created my own version but while researching this post I discovered this blog post by Madis Kõosaar: Customize GitHub Copilot CLI Status Line. He created a much nicer version of what I was trying to achieve.

As he created it for bash with a Linux focus, I adapted it to work on Windows using Powershell. Replace the original Powershell test script with the following code:

Two things were important to get the output correct. First I had to explicitly set the output encoding to UTF-8:

# Force output encoding to UTF-8 without BOM to ensure proper display of special characters in the status line.
$utf8 = New-Object System.Text.UTF8Encoding $false
[Console]::OutputEncoding = $utf8
$OutputEncoding           = $utf8

Second, save the file with a UTF8 BOM. I did this by clicking on the encoding in the footer in VSCode and choose Save with encoding from the command pallet:

Then choose, save UTF-8 with BOM from the list:

This is how the result looks like:

Feel free to adapt it further to your needs.

Popular posts from this blog

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...