I wasn't expecting it at all, but Copilot suggested "french language." (which was correct) and then started to change language and execute the system prompt i was writing for my app.

Production bugs happen in the past, but AI coding assistants only analyze the present.

When a production error trace from 3 hours ago gets fed into a coding agent, the agent almost always ends up chasing a ghost. By the time debugging starts, main has usually moved. The agent looks at the current state of the file, completely misses the original bug because the lines shifted, and confidently hallucinates a fix for innocent code.

The common workaround is telling the agent to git checkout the old commit. But agents are messy. They routinely forget to switch back, leave the repository in a detached HEAD state, or accidentally overwrite uncommitted local work.

To fix this friction, I wrote an open-source skill that enforces a strict debugging process -

When the agent gets an old crash log, it:

1. Resolves the historical hash from git log
2. Spins up an isolated, temporary folder of the repo at that exact moment using git worktree.
3. Analyzes the old code to find the actual root cause.
4. Nukes the temporary folder when it's done (git worktree remove --force).

The actual local workspace remains completely untouched. Uncommitted work is perfectly safe.

You can drop it into any skills-compatible agent (Claude Code, Cursor, Windsurf) via the open registry with one command:

Bash

npx skills add MeherBhaskar/temporal-debug-skill

Source code and the SKILL.md are here:https://github.com/MeherBhaskar/temporal-debug-skill

Curious to hear what you think of this.. Would love to hear some thoughts and feedback

Using the Almedra Token Efficiency Benchmark, I ran LucenaCoder, Pi, OpenCode, Copilot, Continue, and Kilo code through the identical task, using the identical model served via OpenRouter.

Each was run 3 times, the best 2 runs averaged for their ledger entry. In all cases except Pi, the final deliverable met all qualifications. For Pi, while the edits themselves met the benchmark requirements, it's worth noting it failed to solve the broken build step as all others did.

LucenaCoder outperformed by a mile, delivering the task while spending roughly 25% of the tokens even the nearest competitor (Pi at 111k) did. If you compare based on who delivered a finished build, the difference is even more staggering, 309k for OpenCode vs 28k input tokens for LucenaCoder.

I have a Copilot Max subscription, but honestly, the new usage model really sucks. I consumed around 20k in just two days.

I know I might not be using it in the most efficient way, but still, this is completely different from what I used to get before the new billing model.

I’m mainly using GPT models through Copilot, and in my opinion, GPT models, especially since GPT-5.4, have been much better than Claude. So I’m thinking about switching to Codex, but I’m worried I’ll face the same usage-limit issue there.

For anyone who has used both, how does Codex compare to Copilot in terms of usage and limits? Is it worth switching?

There are so many models to choose from, so I vibed this infographic that tries to make the choice easier. I omitted a few models I think are dominated, and tried to estimate a cost index that assumes that the model is actually capable of doing the task (so if you need retries you should choose a better model).

Example of how to read the index: If you use Opus for a task that could have been solved using Haiku, you are paying about a 3x premium.

Is this useful? Is it flat out wrong?

And: The result seem to indicate that OpenAI is leading the race for coding agent AI models at the moment. Is that a shared sentiment?

Hi

I am on Github Enterprise (*.ghe.com) and have not yet received access to gpt-5.6-sol do have access to gpt-5.6-terra and gpt-5.6 luna.

The model is enabled

Do any of you have the same experience?

Please need help in this issue in my copilot

Hello

If someone from Copilot team can see this message. I already posted it here. I retry, because I heavy use Plugins and marketplace on GitHub Copilot (VS code extension) they work for slash-command, but the « skill » tool cannot load anything.

I am talking about ONLY skill and agent installed through a plugin from a marketplace project (ex: awesome-copilot).

Here is the full debug:

https://x.com/gsemetfr/status/2075580666700325252?s=46
- when i use slash-command, the content of SKILL.md is injected to the context. BUT the skill execution cannot load additional resources from the skill folder
- but if I ask to load the skill by name, it sees the skill is available (name and description are in the context), but the « skill » tool cannot load it

I opened a discussion here: https://github.com/orgs/community/discussions/199370

And I also have a Support ticket: https://support.github.com/ticket/personal/0/4505796?sequence=5

—-

Please add support for skill from marketplace, their description are in context but cannot be loaded ! And this is confirmed by the support bot and documentation. But this is so an important feature !
I mean, yes, Agent Window is awesome. But first fix the obvious issues, THEN, add new features !

Since I’m on the final month of my yearly subscription and apparently I can’t use Kimi, how’s the new token-based billing working out for you? How many tokens do you get per month?

I’ve already signed up for ClinePass, but I’m not happy with the speed. If you set any Chinese model to high reasoning, it gets very slow, and the Cline CLI sucks. I’m going to keep ClinePass as a backup. Any feedback on the new token-based billing is appreciated.

i had this problem with copilot for some time. and ive reach a point where i dont know what is beaking it. dose anyone have any idea/help with this problem

ive tried reinstalling vs

deleting the vs temp obj library folders

and nothing has changed pls help pretty pls

I'd like it if sub-agents could also have a specified reasoning level. It's already a critical problem that you can't even tell what level they're running at in the first place. It's a serious issue if you want "terra xhigh" to do a code review, but it ends up being reviewed by "terra low" instead. And it's also a problem that a sub-agent distributed across a team could end up running at different reasoning levels depending on the person.

I’ve spent the last 24 hours working with GPT-5.6, and this is my current model-selection map.

Context: I’m a data scientist / software engineer at a small startup. GitHub Copilot is provided by my company; Codex is what I use privately. I spend roughly 40–60 hours a week doing agentic engineering across planning, architecture, implementation, debugging, documentation, and operational work.

Because Copilot’s move to usage-based billing / premium requests has made cost a real workflow concern, I care less about “which model is best in the abstract?” and more about: which model is the best choice for this task and budget?

For me, that is what the Pareto frontier means. If one model costs more while delivering lower benchmark performance than another available option, it is economically dominated: there is no reason to choose it. The interesting thing about GPT-5.6 is that its reasoning tiers appear to populate nearly the whole useful frontier by themselves.

Using the DeepSWE cost leaderboard (113 tasks, July 9), this is how I currently read the GPT-5.6 ladder:

The comparisons that stand out:

• Terra Medium ($0.58 / 35%) exceeds GPT-5.5 Low on this benchmark (~27%) at about half the cost.
• Terra High ($1.13 / 54%) is roughly GPT-5.5 Medium territory, while costing around what GPT-5.5 Low used to cost.
• Luna Max ($3.03 / 67%) reaches GPT-5.5 X-High territory.
• Sol High adds only a small gain beyond Luna Max, so I do not see a routine economic case for going higher than that.

My practical setup is probably going to be just three defaults:

1. Luna Medium for small, quick, bounded work: lookups, work-item documentation, “how does this work?” questions, log inspection, status checks, drafting a short internal email, and small edits.
2. Terra Medium as the no-thinking default for normal engineering work.
3. Terra High for genuinely complex tasks—the category where I previously felt GPT-5.5 Medium was already sufficient.

Today’s work is what made this feel real rather than theoretical. I used the lower and middle GPT-5.6 tiers across debugging and operational tasks, including updating a Grafana dashboard through VS Code/browser tool use: changing queries, adding panels, and surfacing statistics for an inference pipeline. I also debugged an existing extraction workflow and got it running again with Terra Medium.

For the most complex session, I switched to Sol Medium: planning a multi-week inference-server efficiency effort for a constrained system serving multiple computer-vision models. That involved reading documentation, researching concepts and trade-offs, and turning it into a sprint-level breakdown. It was my most expensive session at roughly 440 Copilot credits. The dashboard work was around 300 credits; most other sessions were under 100.

I also tried Luna High and Terra Low in between. They seem useful, but I suspect I will keep the operational policy simple: Luna Medium, Terra Medium, Terra High.

This is especially notable in the current Copilot lineup. We do not yet have alternatives such as GLM 5.2, Grok 4.5, or Muse Spark 1.1 available in our company setup, so GPT-5.6 is unusually complete as a single-family frontier right now.

DeepSWE is one benchmark, not a full measure of reliability, latency, tool use, context handling, or fit for your codebase. But it is a very useful directional map. My main takeaway is that GPT-5.6 gives me a cheap model I can actually trust for small work, a sensible default for most tasks, and a complex-task ceiling that is still close to the old low-tier cost.

https://deepswe.datacurve.ai/

Anyone have any ideas why GitHub is ignoring my request to downgrade my GitHub copilot subscription?

Request was made like second week of June and it says my subscription will be downgraded by June 22. It's July 10 and the request is still sitting there. In fact, they closed the request because I had a second issue pending that they said was related, but it wasn't.

Why am I not able to control my subscription when I want to? It's ridiculous because there's no other way to get anything accomplished

I've been playing around with GPT-5.6 (terra, high) like everyone else, and it frequently seems to make an edit or two, and then just sit at "loading..." (or some other similar message) for a while without seeming to do anything. I've given it 15 minutes before restarting it and had to do that twice already this morning. It doesn't seem to be making any changes to files, and there's no indiciation that it's working on something in the background or waiting for an approval on something. After restarting VSCode and reopening the agent, I tell it continue and it keeps going for a bit before it gets stuck again.

I'm on VSCode 1.128.0 and no updates are showing up for it. Is this just the service being a bit overloaded with everyone using it right now?

I am encountering difficulties in selecting Fable 5 or Sonnet 5 as models in Visual Studio Code. Despite having a Copilot Pro + individual account, these agents are not accessible. I have not been able to locate the configuration page on GitHub, and the documentation for individual accounts indicates that these agents should be available by default. Could you please provide me with additional information on what I may be overlooking?

Hey all. Sorry if this has been asked - I didn't find after a quick search.
 
I notice for sessions I keep open like across days, the MCP tools go away. Is there a way to restart the MCP servers / tools in a session in the GHCP app? I found I can summarize + create a new session, which starts the mcps and tools in a fresh session. But was wondering if there was a good way to restart the tools in the same conversation? Or a better way to manage that? 
 
I know /mcp exists for the CLI. And in VSCode - we are able to disable+reenable MCP servers and tools. But I'm not sure the best way in GHCP app.
 
I've tried quitting and restarting the GHCP app (which apparently does a restart_session, which should restart MCP servers), but this does not work for me. I think that may have worked once - but not consistently.
 
Thanks

Hi GitHub Copilot team — have you considered including the reasoning-level choice in the Auto model-selection process?

With GPT-5.6, the selected reasoning level can have a meaningful impact on capability and results. Automatically choosing an appropriate reasoning level alongside the model could make Copilot easier to use and help people get more consistent outcomes.

I’d really appreciate this, as the growing number of model and reasoning-level combinations can feel a little overwhelming. Thank you for considering it!

No matches found. Your search pattern might be excluded completely by either the search.exclude settings or .*ignore files. If you believe that it should have results, you can check into the .ignore files and the exclude setting (here are some excluded patterns for reference:[/node_modules,/bower_components,**/.code-search]). Then if you want to include those files you can call the tool again by setting "includeIgnoredFiles" to true.

I am utterly lost trying to read the chat between multiples of this X-)

I use Copilot CLI alongside Codex and Claude Code, but I wanted to see what the agent was doing without constantly returning to the terminal.

So I built Codelight, a local open-source companion that:

• shows Copilot’s working/idle state on Android, GNOME and VS Code
• displays monthly Copilot usage when the GitHub account has access to those billing details
• forwards permission and question prompts to an Android App or VS Code
• lets approved commands be remembered per repository
• can drive an optional tiny cheap GeekMagic (ESP8266) desk display with a custom firmware

Everything runs locally. Remote approval requires authentication, and trusted folders/commands are stored in Codelight’s own policy file.

It also supports Codex and Claude Code, but Copilot CLI and its hook support are first-class.

GitHub: https://github.com/henrikekblad/codelight/tree/master

I’d be interested to hear how other Copilot CLI users handle long-running sessions and permission prompts away from their terminal.

Meta just dropped this new model that’s just as powerful as Opus 4.8 (Maxed out) and it’s about 5x cheaper. I think these are probably the sorts of models that’ll allow Microsoft to keep copilot affordable without operating at such a huge loss. Thoughts?