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GitHub Copilot app for Beginners: Automate Dependabot pull request triage

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I might be biased, but I think Dependabot is pretty amazing. It helps keep my projects up to date, ensuring I’m always using secure libraries. But because there’re frequently new vulnerabilities, there’re frequently new pull requests from Dependabot.

Sometimes it’s a minor version bump. Sometimes it’s a major version upgrade. Sometimes everything will work just fine. And sometimes… well, every single developer has been caught by a breaking change.

How can we best triage these pull requests? The work isn’t particularly difficult per se, but it certainly is repetitive.

It’s the perfect task to offload to Copilot! With GitHub Copilot app automations, you can hand off that first round of review. Instead of manually inspecting every Dependabot pull request, you can create an automation that reviews open pull requests, groups them by risk, verifies CI status, and delivers a summary before your day begins.

Follow the steps below to build a daily Dependabot triage automation.

Step 1: Create a new automation

From the GitHub Copilot app, create a new automation.

You’ll configure two things first:

  • Name: Give the automation a descriptive name, such as Daily Dependabot Triage.
  • Trigger: Decide when it should run.

Available trigger options include:

  • Manual
  • Hourly
  • Daily
  • Weekly
  • When an issue is created

For recurring maintenance tasks like Dependabot reviews, a daily schedule is often a good choice. For example, you might schedule it to run before your workday begins so the results are waiting when you log in.

You can also choose whether the automation runs in the cloud or on your local machine.

Step 2: Describe the task in natural language

Next, tell Copilot what you want it to do.

For example:

Review the open Dependabot pull requests, group them by risk, identify the safe patch and minor version updates, verify that CI is passing for each pull request, and provide a short summary of the recommended next steps.

Because the prompt uses natural language, you can customize it to match your team’s workflow.

Step 3: Select the repository

Choose the repository or project the automation should analyze.

Once you’ve selected the repository, create the automation.

If you want to test it immediately instead of waiting for the scheduled run, choose Create and Run.

Step 4: Review the results

When the automation finishes, Copilot returns a summary instead of a list of individual pull requests.

For example, it might:

  • Group safe patch updates together
  • Separate minor and major version upgrades
  • Identify which pull requests have passing CI
  • Highlight dependencies that require additional investigation

Rather than interrupting your morning with dozens of small decisions, you can quickly identify which updates are ready to merge and which deserve closer attention.

Step 5: Continue the work in a Copilot session

If one of the updates requires additional work, you can continue directly from the automation results.

For example, if the summary identifies a major framework upgrade, you can start a new Copilot session from the results and ask Copilot to help complete the migration.

Because the session starts with the automation’s context, you don’t have to gather the information again.

Review previous automation runs

Every automation run is saved, making it easy to see:

  • When it ran
  • What actions it performed
  • What results it produced

Having a history of each run makes automations transparent. You can always review what happened instead of treating them as a black box.

Turn repetitive work into background work

Dependabot triage is a good example of the kind of recurring task that’s well suited for automation. You describe the workflow once, choose when it should run, and let Copilot perform the repetitive steps automatically.

If you’re just getting started with automations, begin with a task you already perform on autopilot. Let Copilot handle the routine work so you can spend your time on the decisions that require your expertise.

Ready to automate your next recurring task? Create your first automation in the GitHub Copilot app >

The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog.

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Microsoft Formalizes VS Code Agent Host as Open Architecture for Persistent AI Sessions

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After rolling out Agent Host functionality across recent VS Code releases, Microsoft is detailing the architecture behind persistent agent sessions and opening its Agent Host Protocol to third-party clients.
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#560: Building a Research OS: From Django to 30,000 Samples

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In 2020, a gastroenterologist in Glasgow did the math on his new research study and came up with 30,000 samples, arriving over two years from three cities and a dozen hospitals. He asked around about how researchers keep track of that. The answer was Microsoft Excel. Shaun Chuah had written some HTML by hand in Notepad back in high school and that was about the whole of his programming experience, so he opened the Django tutorial and started reading. Six years later that app is Foundry120, holding 10 terabytes of clinical and genomics data with an agentic AI running on top of it.

Episode sponsors

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Guest
Shaun Chuah: github.com

Up and Running with Rust Course: talkpython.fm

Foundry120: www.foundry120.com
Designing Data Intensive Applications: www.oreilly.com
Microsoft Foundry: ai.azure.com
ChatIBD: www.chatibd.com
Blog: shaunchuah.github.io
@drshaunchuah: x.com
github.com/shaunchuah: github.com

Watch this episode on YouTube: youtube.com
Episode #560 deep-dive: talkpython.fm/560
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong

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Download audio: https://talkpython.fm/episodes/download/560/building-a-research-os-from-django-to-30-000-samples.mp3
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Arguing about AI with Billy Hollis

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Ready for a rant? Carl and Richard talk to Billy Hollis about the impact of artificial intelligence on software development. The conversation starts with a listener comment about going all-in on AI to speed up development radically, which raises the question of how much AI is the right amount. What's safe and what is reckless? And how will that position change over time? Are the problems we're having today just growing pains of the tools, or are they systemic to the technology? Lots to debate!



Download audio: https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/74702144/dotnetrocks_2017_arguing_about_ai.mp3
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Kubernetes 1.37, with Dipesh Rawat

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Dipesh Rawat is a Software Developer at IBM, CNCF Ambassador, CNCF Kubestronaut and SIG Docs Tech Lead. A contributor across multiple Kubernetes release cycles, he serves as the Release Lead for Kubernetes v1.37.

 

Do you have something cool to share? Some questions? Let us know:

- web: kubernetespodcast.com

- mail: kubernetespodcast@google.com

- twitter: @kubernetespod

- bluesky: @kubernetespodcast.com

 

News of the week

Links from the interview





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Episode 132 - Windows Server 2025 Security

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In this episode, Michael and Sarah talk to Orin Thomas about security improvements in Windows Server 2025. We realize this is an Azure podcast, but many people deploy Windows VMs!

We also security news about CosmosDB, GitHub Copilot and Quantum development, ASP.NET and PQ hybrid TLS 1.3 and more!

https://aka.ms/azsecpod





Download audio: https://content.rss.com/episodes/8411/3098075/azsecpodcast/2026_08_26_16_25_02_8ebf3efc-0f4a-4c7e-bfff-4e5b881f8274.mp3
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alvinashcraft
19 minutes ago
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