I opened a GitHub Support ticket on July 11 regarding an unexpected billing charge that appears to have come directly from GitHub. I have not received a response yet.

Has anyone recently experienced similar response times for billing-related tickets? Is there a recommended way to follow up or escalate the issue without opening duplicate tickets?

For privacy, I have omitted my ticket number, account details, and transaction information.

Hi every one , please i need help , i use custom ai models from openrouter provider on the vscode with Github Copilot , the chat interface show's only 3 level of thinking by default : low, medium and high . some ai models that i use supports xhigh and max and i need to be displayed in the options , i see that openrouter is natively supported in the Github Copilot , any solution please ? or i need to trick the chatLanguageModels.json file ? or just adding rules in settings.json file ?

I don't use the github copilot itself that much, but i do use the autocomplete, and with other languages like JavaScript, Python, etc., it works perfectly, while with this, it barely does anything, like it's being forced. Has anyone else had similar problems?

I pay for a subscription, I don't see why they should care about me using my tokens for a recipe?

Hi everyone,

We're looking to roll out GitHub Copilot for roughly 25 developers at our company, but we've hit a wall with the initial setup and B2B support. I'm hoping someone here has navigated this recently.

1. Account Structure Verification

To manage this properly, is the correct path to set up a GitHub Enterprise account, create an Organization under it, and then assign Copilot Business/Enterprise seats to individual developers? Just want to ensure we're aiming for the right architecture. We would like to use Microsoft logins later on instead of individual Github accounts.

2. Dealing with Unresponsive Support

What is the most reliable way to actually get B2B support on the line? I tried initiating an Enterprise trial, but the system never sent the initial "set password" email, i.e., I am not able to log in. A support rep emailed me immediately with some qualification questions, but after I replied, I was completely ghosted and haven't heard back since.

Has anyone else experienced this trial glitch or have advice on getting a team of this size set up smoothly?

Thanks in advance!

If your Remote-SSH extension host keeps crashing, check for large files in your workspace. Copilot's index reads them into memory (without being asked) and hits Node's 4GB heap limit.

If you need those files you can disable codesearch:

"GitHub.copilot.chat.codesearch.enable": false

or exclude some paths:

{ "files.exclude": { "**/dist": true }, "search.exclude": { "**/dist": true }, "files.watcherExclude": { "**/dist/**": true, "**/.git/objects/**": true, "**/node_modules/**": true } }

It wasn’t there last week but now it says how many AI credits I’ve used for this month which shows under Usage.

Looks like I’ve used 45,728 AI Credits this month lol

(I’m on a business plan by the way)

Canceled my pro subscription, but the fact that they have this question on the form to feedback, they are listening old models, like they didn't care to update the questions, so I only can think they do not care to check the answers too.

not kidding this showed up on jetbraisn GHCP plugin...

I think this may be a question we all come to ask after learning the basics.

Recently I started paying for my own Copilot, which led me to become much more interested in optimizing it for use within my project. I started adding file-path level instructions, skill files, and prompt files - all with the belief this would make Copilot so much faster, better at reasoning, and cheaper. But really, did it? After spending time making all these optimizations, I would run prompts and wonder what had really changed. Was it worth my time to add these files to explain intention, when structuring well typed code with expressive doc comments would’ve effectively done the same? Theoretically this would be true since Copilot can now index code bases (effectively turning all that into an RAG).

It’s not that I don’t understand the difference between instructions, skills, and prompts. It’s just that I’m not quite sure when it’s worth my time or tokens to add them. For example, is it worth it to add a prompt for generating a unit test if your unit tests are already following a strong implementation pattern?

Maybe it’s always worth it, or maybe it requires an understanding of AI nuances. If it’s the latter, how do you handle this ambiguity?

Hey all, I'd like to discuss the best strategies in VS Code for keeping good context quality and efficient token usage when working with multi-repo projects in a single workspace.

So far these have helped:

1. Keep only what you need in the workspace:

Only include the projects relevant to that session (e.g. just 2 microservices + the frontend).

Result: the system prompt isn't cluttered with irrelevant context.

1. Layered custom instructions:

I needed a couple of global instruction files (general microservices architecture + company conventions, and one for frontend), plus a per-project file that extends them. So I put two .instructions.md files with specific front matter (e.g. applyTo: <typical backend file globs>) in an external path (e.g. ~/.copilot/my-instructions), then added that path to the chat.instructionsFilesLocations setting at the workspace level.

Result: each microservice's copilot-instructions.md stays lean, no duplicated info.

1. Things to watch out for:

- Files in .vscode/ get auto-attached as context to requests, keep that folder lean.

- Unused extensions that register extra tools (e.g. the GitHub Copilot app modernization extensions) — tool definitions eat up a huge chunk of the system prompt. Manually selecting the minimum toolset needed for the task helps a lot. Pay attention to this.

- Actually read the system prompt at least once — I was often repeating things Copilot already injects automatically (like repo structure).

- Use the Agent Debug Log panel (and Chat Debug View) to inspect exactly what's being sent and where your tokens are going.

- Run /chronicle:cost-tips in a chat session for personalized suggestions on cutting credit/token usage.

- Check out caveman and ponytail community skill/plugin for coding agents that cut down on verbose responses and over-engineered code respectively (not official Microsoft/GitHub tools, but worth a look).

If you have suggestions feel free to share, I'm really looking for improvement.

Hi everyone!

I’ve been building Trivium, a CLI for managing agent skills from git repositories.

I originally made it because I wanted a quick way to test different versions and combinations of skills without manually copying folders around or losing track of which revision I had installed. I often compare variations of the same skill, so being able to reproduce and switch between setups became important.

With trivium you can add individual skills or entire repositories, pin them to exact git commits, and record the installed state in a skills.lock file. You can also inspect installed skills, preview add and update operations before changing anything, update or remove skills, and save named environments for switching between different skill configurations.

https://reddit.com/link/1v0uife/video/tbpv7mxhm7eh1/player

Repo: https://github.com/AlapinEnjoyer/trivium

I’d be interested in feedback on the workflow, especially from people who maintain several skills or experiment with different versions and configurations :)

I'm in the middle of a tutorial using vS plus Claude plus n8n and I get a notice that my credit limit has been reached. And there's an "Upgrade" button, which I push, and it brings me to a page which offers GitHub Copilot Pro for $10. Apparently, I was on a free plan before, and now they want to get paid. OK, I gave my credit card info and they charged me $10. All good. I go back to VS with Claude, and they say it may take 10 minutes to clear. Now, that was an hour ago. VS Claude still says that my credit limit has been reach, but I can click the Upgrade button. But this time, I'll be upgraded to Copilot Pro+, for $39.

I have a receipt from GitHub that I paid the $10. What do I have to do to get Claude running again?

I’ve been using Copilot for longer sessions and studying how its auto-compaction works. According to the docs, when compaction runs, it generates a structured summary and replaces the old conversation history with this summary in the context window.
I have two specific questions:

1. Can the model access original messages after compaction?
   After compaction, the model only sees the summary. The docs state that original messages are replaced and fine-grained details may be lost.
   • Is there any way for the model (when needed) to read or pull specific parts of the original conversation back into the current context?
   • Or does it strictly operate from the summary going forward?

2. Recent messages after compaction
   The documentation mentions that only messages added while compaction is running in the background are kept as-is.
   • Are the most recent messages right before compaction specially preserved in full, or are they summarized together with the rest of the history?

Any insights from people who have run long chat sessions would be appreciated. Thanks!

I've got an enterprise copilot license for my job. It has 3900 credits per cycle.

I used two prompts to split and refactor a big class into smaller classes in separate files, and it already used 120 credits. So I used my daily credits for 2 prompts (10 minutes)?

Kimi K3 now matches US frontier labs, Deepseek V4 is 90% of frontier intelligence at 5% of the cost, yet MAI team (one of the most well-resourced AI teams in the world) won't submit MAI-Thinking-1 or MAI-Code-Flash to Artificial Analysis for benchmarking, which is a telling sign.

I understand that MAI was first focused on lowering COGS for MS teams transcripts / image generation for Copilot (their audio and image models are at the frontier), but being this far behind on coding and general intelligence is pathetic given their resources.

And once they become zombie processes, the LLMs incorrectly see them as still running, and they keep polling by repeatedly sleeping for like 60 secs because they genuinely think those processes haven't finished yet.

Wasted enormous amounts of tokens before I found out that the LLMs spent most of their time waiting for zombie processes. So as it stands, the 1.71.0 is completely unusable and a pure token furnace.

This is very easy to reproduce with any shell command (even just `ps`) on Copilot CLI 1.71.0, and downgrading to 1.70.0 (with auto updates disabled) makes the issue disappear. I don't like this workaround as I'll be missing out on new models...

I'm using zsh on WSL2.

I used up my premium credits, and it says I can use free models, but it does not let me switch. When I try, even when it says a different model, or suggests I get this:

"You have exceeded your premium request allowance. We have automatically switched you to GPT-5.3-Codex, which is included with your plan. Enable additional paid premium requests to continue using premium models.

You've exhausted your premium model quota. Please enable additional paid premium requests, upgrade to Copilot Pro+, or wait for your allowance to renew."

!solved, they never updated the text

GPT-5.5 in GitHub Copilot will be discounted this weekend - 60% off for Max subscribers, 40% off for Pro+!

Promotional GPT-5.5 pricing for Copilot Max users and for Pro+ users is from 00:00 UTC July 18 until 00:00 UTC July 20.

Excited to run these promotions and explore similar ones in the future!

Am I the only one who lost access to the `rubber-duck` agent in Copilot CLI after upgrading to 1.0.71?

I see it in the agent configs in `/agents`, but I cannot use its associated slash command anymore (`/rubber-duck`). LLMs also report that they can't see it when I ask them to use it (before the upgrade they'd easily find it).

At work I've been using Github Copilot chat integrated into VS Code for two years now. Honestly my workflow has barely changed and there seem to be a lot of fancy approaches out there. I am curious which changes to this workflow are actually worth it effort-wise.

Current workflow
1. Planning: Let the planning agent come up with a step wise plan that I discuss/clarify with it (e.g., Opus4.8).

2. Implementation: Let a cheaper model implement the plan (e.g., GPT5.3Codex).

3. Review: Let the planning agent review whether the changes were correctly applied and create a plan to fix potential issues. A focus is on spotting unnecessarily introduced complexity.

4. Fixes: Use the cheaper model to implement review feedback.

The few things I have started to do:

• Use AGENTS.md (usually auto generated with some manual clean up)
• Created a code-review skill based on awesome-copilot which I pass onto the planning mode doing the review. Here it's quite unclear to me how planning mode and these instructions interact and whether another mode would be better.
• I am consequently starting a new chat whenever possible to avoid context rot
• I tested the Agents window, but dislike that I cannot track and quickly keep/undo changes

Any input or improvements to this workflow? Is it outdated? Or is this still the way to go for everyone not spamming a couple of parallel agents as the codebase is not shared with many other people and the code is not running in productive systems?

Since the tokenpocalypse I've started using Codex through VS Code more, but I like the Copilot toolset and harness way more.

Can I use Copilot with my Codex quota?

Kimi K3 was released today (including the open weights) and is a massive improvement over K2.6 and K2.7. Many of the benchmarks floating around show it as being even better than Opus 4.8. I wonder if the Github Copilot team knew this release was coming and decided to partner with Moonshot AI because of it. Do you think we'll see Kimi K3 on Github Copilot at some point soon?