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Using Claude Opus 4.6 in Github Copilot
The model selection in Github Copilot got richer with the addition of Claude Opus 4.6. The Model capability along with the addition of agents makes it a powerful combination to build complex code which requires many hours or days. Claude Opus 4.6 is better in coding skills as compared to the previous models. It also plans more carefully, performs more reliably in larger codebases, and has better code review as well as debugging skills to catch its own mistakes. In my current experiment, I used it multiple times to review its own code and while it took time (understandably) to get familiar with the code base. After that initial effort on the evaluation, the suggestions for fixes/improvements were on dot and often even better than a human reviewer (me in this case). Opus 4.6 also can run agentic tasks for longer. Following the release of the model, Anthropic published a paper on using Opus 4.6 to build C Compiler with a team of parallel Claudes. The compiler was built by 16 agents from scratch to get a Rust-based C compiler which was capable of compiling the Linux kernel. This is an interesting paper (shared in resources). Using Claude Opus 4.6 in Agentic Mode In less than an hour, I built a document analyzer to analyse the content, extract insights, build knowledge graphs and summarize elements. The code was built using Claude Opus 4.6 alongwith Claude Agents in Visual Studio Code. The initial prompt built the code and in the next hour after a few more interactions - unit tests were added and the UI worked as expected specifically for rendering the graphs. In the second phase, I converted the capabilities into Agents with tools and skills making the codebase Agentic. All this was done in Visual Studio using Github Copilot. Adding the complexity of Agentic execution was staggered across phases but the coding agent may well have built it right in the first instance with detailed specifications and instructions. The Agent could also fix UI requirements and problems in graph rendering from the snapshot shared in the chat window. That along with the logging was sufficient to quickly get to an application which worked as expected. The final graph rendering used mermaid diagrams in javascript while the backend was in python. Knowledge Graph rendering using mermaid What are Agents? Agents perform complete coding tasks end-to-end. They understand your project, make changes across multiple files, run commands, and adapt based on the results. An agent runs in the local, background, cloud, or third-party mode. An agent takes a high-level task and it breaks the task down into steps. It executes those steps with tools and self-corrects on errors. Multiple agent sessions can run in parallel, each focused on a different task. On creating a new agent session, the previous session remains active and can be accessed between tasks via the agent sessions list. The Chat window in Visual Studio Code allows for changing the model and also the Agent Mode. The Agent mode can be local for Local Agents or run in the background or on Cloud. Additionally, Third Party Agents are also available for coding. In the snapshot below, the Claude Agent (Third Party Agent) is used. In this project Azure GPT 4.1 was used in the code to perform the document analysis but this can be changed to any model of choice. I also used the ‘Ask before edits” mode to track the command runs. Alternatively, the other option was to let the Agent run autonomously. Visual Studio Code - Models and Agent Mode The local Agentic mode was also a good option and I used it a few times specifically as it is not constrained by network connectivity. But when the local compute does not suffice, the cloud mode is the next best option. Background agents are CLI-based agents, such as Copilot CLI running in the background on your local machine. They operate autonomously in the editor and Background agents use Git worktrees to work in an isolated environment from your main workspace to prevent conflicts with your active work. How to get the model? The model is accessible to GitHub Copilot Pro/Pro+, business, and enterprise users. Opus 4.6 operates more reliably in large codebases, offering improved code review and debugging skills. The Fast mode for Claude Opus 4.6, rolled out in research preview, provides a high-speed option with output token delivery speeds up to 2.5 times faster while maintaining comparable capabilities to Opus 4.6. Resources https://www.anthropic.com/news/claude-opus-4-6 https://www.anthropic.com/engineering/building-c-compiler https://github.blog/changelog/2026-02-05-claude-opus-4-6-is-now-generally-available-for-github-copilot https://code.visualstudio.com/docs/copilot/agents/overviewIntegrate Agents with Skills in Github Copilot
The past year saw the rise of Agentic workflows. Agents have a task or goal to accomplish and build context, take actions using tools. Tools while affective in surfacing the requisite sources and actions can easily increase in numbers causing context bloat, high token consumption. Agent Skills was proposed in a recent Anthropic paper to address the above challenges. Agent Skills are now supported in Visual Studio Code (Experimental) and can be used with Github Copilot. It works across Copilot coding agent, Copilot CLI, and agent mode in Visual Studio Code Insiders. Copilot coding agent is available with the GitHub Copilot Pro, GitHub Copilot Pro+, GitHub Copilot Business and GitHub Copilot Enterprise plans. The agent is available in all repositories stored on GitHub, except repositories owned by managed user accounts and where it has been explicitly disabled. An Agent Skill is created to teach Copilot on performing specialized tasks with detailed instructions while also being repeatable. At its core, Agent Skills are folders which contain instructions, scripts, and resources that the Copilot automatically loads when relevant to the query. On receiving a prompt, Copilot determines if a skill is relevant to your task and it then loads the instructions. The skills instructions are executed along with any resources included in the directory structure relevant to the specific skill. One guideline would be to encapsulate into a skill anything which is being done repeatedly. In the example below, we have a skill for creating a github issue for a feature request using a specific template (the template will be referenced by the skill based on the type of issue to be created). The SKILL.md file is very detailed in all the instructions required for supporting multiple github issues related actions. The description is key to understanding the Skill and when the Agent requires a specific Skill, the appropriate instructions are loaded. The loaded Skill is then executed in a secure code execution environment. A further option provided by Agent Skills is reusing the generated code by storing it in the filesystem to avoid repeated execution. In Visual Studio Code, enable the "chat.useAgentSkills" setting to use Agent Skills prior to the run. An Agent can have nested agents which is used to detail sub agents (Nested Agents is also enabled in settings as shown below) and thus decouple functionality. Any prompt in the chat will now have the option to pick from the Agent Skills in addition to the tools available. We can write our own skills, or use those which are shared by others - anthropics/skills repository or GitHub’s community created github/awesome-copilot collection. While skills are very powerful, using shared skills needs to be done with discretion and from a security perspective only use skills shared by trusted sources. Resources https://github.blog/changelog/2025-12-18-github-copilot-now-supports-agent-skills/ https://code.visualstudio.com/docs/copilot/customization/agent-skills
Azure Web App Deployment Failed: 429 Throttling & Policy Errors (Student Subscription)
Hello, I am trying to complete a university lab requiring an Azure Web App (Python 3.9/3.10/3.11 / Linux) running on an App Service Plan to test autoscaling features. I am using an "Azure for Students" subscription. I am unable to deploy the resource regardless of the region I choose. Here is what I have tried: Region : France Central I get a Throttling error : "App Service Plan Create operation is throttled for subscription [My-Sub-ID]. Code: 429" Region : Switzerland North I get a Policy error : "Resource was disallowed by Azure Policy... The objective of this policy is to ensure that your subscription has full access to Azure services with optimal performance." Region : Canada Central / Switzerland North : I attempted to create a new Resource Group and App Service Plan in these regions, but I am still facing deployment failures or throttling issues. I simply need to deploy a Web App with a plan that supports Autoscale. Which region is currently open/unthrottled for Student Subscriptions to create an App Service Plan? Is there a specific workaround to bypass this 429 error for a lab environment? Thank you for your help.
Add claim for personnal App registration
Hello, I want to add the samaccountName in the token for an internal application. Ideally, I would like to do the equivalent of this rule: But if that's not possible, I'd like to have at least the samaccountName for the synchronized accounts. Can you tell me how to do this ? ThanksLooking for a solution - My API clients will be impacted due to Azure TLS 1.0, 1.1 retirement
Microsoft Azure has announced TLS 1.0, 1.1 will be retired by 31-Oct-2024. My clients and devices are making calls through TLS 1.0, 1.1 to my API cloud services / App services. Unfortunately, my devices cannot be upgraded to make calls with TLS 1.2. Considering this situation, how my client devices will continue to work post 31-Oct-2024? It would be great if anyone is experiencing this situation and found any solutions.Logic App Workflow() function returning un-expected results
I am new to Logic Apps, but fairly well-versed in Power Automate. In Power Automate I have a small child flow that I use as an error handler and call it from other flows to send me an email with a link to the failed flow run. I re-created this for my Logic Apps but I have run into a problem. The Error Handler Logic App is triggered by an HTTP request that takes the outputs of the workflow() expression from the calling app. Within the error handler app I parse through the definition to build a clickable link back to the calling app run that failed. When I was testing, the workflow() outputs from my ERROR_TESTER app looked like this: { "id": "/subscriptions/<SUBSCRIPTION ID>/resourceGroups/<RESOURCE GROUP NAME>/providers/Microsoft.Logic/workflows/ERROR_TESTER", "name": "ERROR_TESTER", "type": "Microsoft.Logic/workflows", "location": "centralus", "run": { "id": "/subscriptions/<SUBSCRIPTION ID>/resourceGroups/<RESOURCE GROUP NAME>/providers/Microsoft.Logic/workflows/ERROR_TESTER/runs/<RUN ID>", "name": "<RUN ID>", "type": "Microsoft.Logic/workflows/runs" } } I just caught an error from an actual app I was running. The link didn't work and on further inspection I realized that the failed app's workflow() outputs looked like this instead: { "id": "/workflows/<WORKFLOW ID>", "name": "<LOGIC APP NAME>", "type": "Microsoft.Logic/workflows", "location": "centralus", "run": { "id": "/workflows/<WORKFLOW ID>/runs/<RUN ID>", "name": "<RUN ID>", "type": "Microsoft.Logic/workflows/runs" } } Despite the other differences, I was using the "run.id" property to build the URL to the flow run. Now that it seems to be getting truncated my original error handler app doesn't work. Can anyone enlighten me on why the outputs are so different and potentially how to fix or plan for the differing outputs?
Filter on array length in Azure Cognitive Search
How can I fetch index records by filtering on array length? For ex. I have an array property "Contracts". I want to fetch records where Contracts is a blank array. All I could find in documentation is "any" (Contracts/any) and "all" (Contracts/all) but I want to use something like $filter=Contracts/count eq 0. Is there a way to filter on length of array?
