This feels self-selective to how some people work, because it requires using MCP to contribute.
I'm a reasonably heavy AI user, and have some custom skills/MCP servers I'd share, but there is no way in hell I'm connecting to some arbitrary MCP server and connecting my Github account to it. Noppppeeeee.
ShareYourAISetup folks create a skill that user can run locally that collects information re their setup, formats it and sends to dedicated endpoint. Result = fairly uniform reports where people share what they're comfortable sharing.
No requirement from given user to spend their time. Users see what the skill does.
I guess atm they dont know what questions they want to be asking
One MCP server for doing automated code quality checking, using Valknut, Jscpd, and Lizard to output scores, combine/aggregate them. That's then give to the model so that it can see if there's a mess of code dupe or if there was significant architectural regression. It's allowed to say "This is worth it" but it ground it a bit and stops really stupid, short sighted, hacked-in features
Another uses https://github.com/thejens/id-token-nicer so that the model doesn't bork a big ID without being noticed. It's told to translate in and out of that as necessary.
I had it write it's own skill for making sure to follow some sane commenting/style guides per language, that way it doesn't just give me massive files with no comments.
Finally, a last one lets Antigravity or Codex call out to local models as workers, for long running background tasks where a cheap, dumb model is fine. Saves on overall token usage.
On top of that, the way people work is notable: Do you use worktrees for agents? Why/why not? Do you have only one subscription? What level? I'm using both Codex at $20/mo and Antigravity at (promo) $5/mo right now. Antigravity's TOS says you MUST use their harness, which informs some of my skill setup/workflow. Do you make your agents.md self modifying, write it yourself, or not use one at all? Do you use a sandbox or YOLO it?
Even just how you prompt matters. On Antigravity/Gemini, I can give it a big list-o-TODOs and have it make a very detailed implementation plan itself, carry it out, and generally do the thing, all unsupervised. Codex, from what I have experienced, isn't as good at that. Doing the planning as a separate step, writing to a file, then telling it to step-by-step it with commits for each step makes it work okay again though.
I don't especially want to interact with the site, but I have built two tools:
An agent sandbox: https://github.com/pjlsergeant/byre -- a truly gigantic amount of thought and effort has gone into it. It's really focused on developer experience. I have used it all day every day for really quite a while. It's a low-magic wrapper over Docker / Podman. I would encourage you to ask your agent to code-review it!
An agent-to-agent message board: https://github.com/pjlsergeant/dogpark -- this is much less mature, but a good amount of thought has gone into the design, so if that's something you need, please check it out.
As always with smallish tools/services like these whipped up with an LLM, I find there's more value in seeing the "source code", i.e. the prompt itself.
Turns out it's quite useful in this instance, and I just made a small couple of tweaks to get a summary of my own workflow.
FWIW, this is what I modified it to:
Help me share how I work with AI with my teammates.
Use our existing conversations and relevant local tools within my permissions to discover my agents, harnesses, skills, connections and working practices. Keep secrets, raw configuration and private project details out of the draft. Explain how my tools work together and develop a concrete workflow where the evidence supports it, rather than listing installed software. Draft from what you know without a questionnaire; ask one short question only if an essential gap prevents a useful draft. If local inspection is unavailable, use our conversation without claiming otherwise. Save a private draft, show me the exact preview, and wait for my explicit approval before publishing.
Do you plan to synthesize the data and show top 2 Popular setups? This would be for newbies who want to start with a powerful setup, and for those looking to discover new tools. Thanks!
I been working and making things with AI agents since Windsurf days, here's what works for me after experimenting and working for a while with these things. As far as the agent goes I find whatever the latest OpenAI model is out worked best for me. This company has burned me the least and I like the models. I tried many but consistency of OpenAI models cant be beat IMO. Though we are post honeymoon phase now I feel like so I am now experimenting with cheaper alternatives like Deepseek 4.1 flash and so on. I don't trust Anthropic as the downgrade my models consistently and its rare i get to use what I pay for. I wont even get in to discussing Google agents for obvious reasons, grok I never used though Grok bot looks interesting.
For skills I make my own, but most important is the custom setup i have. Voice is how I use all of my agents. I have an extremely well optimized voice setup that i custom built so I can talk to my agents and also hear them. The voice stack itself is very low latency and high quality. asr (parakeet v3), tts (omnivoice) take no more then 400-450 ms total as far as latency budget is concerned, rest is on the agents actual decode speed. IMO this setup is crucial for all antigenic work, i can express myself a lot better with speech and also give a lot more context and nuance with voice, i rarely type. I still look at the terminal window because my agent knows to keep the technical details in text form versus barfing them at my voice channel, plus terminal gives me lots of other important data about the agents direction and what hes doing, nothing custom here though. I cant emphesise how important voice is though, it has to be practiced to really understand.
As the models got better I now trust them with longer and longer tasks though I still don't use /goal feature as it has never worked out well for me. Theres no need for micromanagement any more but you still need to be there to steer the ship somewhat. BTW, codex cli compaction is garbage so I made my own custom implementation that works a lot better and allows the thread to be used indefinitely without issues. I strongly suggest everyone makes a new thread after extensive use if you havent made your own implementation.
Theres about a billion other things I could get in to like subagents, cohort groups, orchestration layers, etc... But this is a good start imo.
It'd be useful to see "approx monthly cost" or something similar. I love seeing other people's setups, but the first post I clicked on describes at least $400/mo in subscription plans.
I was immediately hoping for some local-AI setups that do real work, on limited resources. Unfortunately it looks like this is still waiting on more people to share.
Not a SWE, but I do Freetoken (backend) + unsloth (front) to run qwen 3.6 35B A3B NVFP4 (~20GB) in 8GB VRAM + ~20GB RAM. Getting around 50 t/s on a 3070. I don't think I can even share through this site.
Are people actually paying API pricing for claude to hook it up to Pi or openclaw or is there a secret trick? The subscription price is palatable but for me the token price generally isn't.
I'm using https://pi.dev/packages/pi-claude-agent-sdk to use the claude sub, but it has the drawbacks of being forced through the claude-sdk. You would still be using the claude system prompt etc., which is one of the main reasons I started using Pi over claude-code.
For my workflow I primarily use a Codex subscription, but farm out adversarial reviews to Fable to clean up unnecessary gpt-ish code (lots of over-engineering). All my UI planning is done with fable, but implemented with OAI agents once I have a solid very specific plan.
Love the idea! Would love it if you could add a method to share your setup that doesn't involve MCP... I don't use MCP servers in my setup but I wish that didn't then exclude me from being able to share my setup.
my setup heavily involves using tailscale to secure everything, because a lot of the tools I have are internal and need to be protected at all costs. tailscale is so awesome! highly recommend to secure your networks
just certain dashboards that display my personal financial information, housing stuff, and some goals/trackers I have around projects I run.
I do work mainly on my desktop, but let's say I'm traveling right, I'd have to turn it off and was missing access to some pretty important information, so I ended up getting a VM and hosting some of those things on there instead. I still use both, but things I want available, I'll host it on my "homelab" and secure both machines via tailscale. it's pretty easy and it ensures that only my phone, my main mac, and my homelab have access to each other and nothing else.
The setups I wrote down three months ago are already half wrong, tools churn that fast. Do entries show a last-updated date so I can tell what's stale?
This is a wonderful idea - didnt know I was looking for this until I started browsing. I found it helpful with surfacing that which I didnt-know-I-didnt-know.
I'm a reasonably heavy AI user, and have some custom skills/MCP servers I'd share, but there is no way in hell I'm connecting to some arbitrary MCP server and connecting my Github account to it. Noppppeeeee.
No Codex, Claude, or Pi... now you have peaked my interest with your setup :P
No requirement from given user to spend their time. Users see what the skill does.
I guess atm they dont know what questions they want to be asking
I don't know why this has to be reinvented?
I have some custom skills/MCP servers I use:
one MCP server for using https://usefeyn.com/blog/pulpie-pareto-optimal-models-for-cl... ran locally so that reading web pages is cheaper and uses up less context
One MCP server for doing automated code quality checking, using Valknut, Jscpd, and Lizard to output scores, combine/aggregate them. That's then give to the model so that it can see if there's a mess of code dupe or if there was significant architectural regression. It's allowed to say "This is worth it" but it ground it a bit and stops really stupid, short sighted, hacked-in features
Another uses https://github.com/thejens/id-token-nicer so that the model doesn't bork a big ID without being noticed. It's told to translate in and out of that as necessary.
I had it write it's own skill for making sure to follow some sane commenting/style guides per language, that way it doesn't just give me massive files with no comments.
Finally, a last one lets Antigravity or Codex call out to local models as workers, for long running background tasks where a cheap, dumb model is fine. Saves on overall token usage.
I also have a few skills from the internet setup. https://github.com/AlmogBaku/debug-skill being the most important one.
On top of that, the way people work is notable: Do you use worktrees for agents? Why/why not? Do you have only one subscription? What level? I'm using both Codex at $20/mo and Antigravity at (promo) $5/mo right now. Antigravity's TOS says you MUST use their harness, which informs some of my skill setup/workflow. Do you make your agents.md self modifying, write it yourself, or not use one at all? Do you use a sandbox or YOLO it?
Even just how you prompt matters. On Antigravity/Gemini, I can give it a big list-o-TODOs and have it make a very detailed implementation plan itself, carry it out, and generally do the thing, all unsupervised. Codex, from what I have experienced, isn't as good at that. Doing the planning as a separate step, writing to a file, then telling it to step-by-step it with commits for each step makes it work okay again though.
An agent sandbox: https://github.com/pjlsergeant/byre -- a truly gigantic amount of thought and effort has gone into it. It's really focused on developer experience. I have used it all day every day for really quite a while. It's a low-magic wrapper over Docker / Podman. I would encourage you to ask your agent to code-review it!
An agent-to-agent message board: https://github.com/pjlsergeant/dogpark -- this is much less mature, but a good amount of thought has gone into the design, so if that's something you need, please check it out.
Turns out it's quite useful in this instance, and I just made a small couple of tweaks to get a summary of my own workflow.
FWIW, this is what I modified it to:
Help me share how I work with AI with my teammates.
Use our existing conversations and relevant local tools within my permissions to discover my agents, harnesses, skills, connections and working practices. Keep secrets, raw configuration and private project details out of the draft. Explain how my tools work together and develop a concrete workflow where the evidence supports it, rather than listing installed software. Draft from what you know without a questionnaire; ask one short question only if an essential gap prevents a useful draft. If local inspection is unavailable, use our conversation without claiming otherwise. Save a private draft, show me the exact preview, and wait for my explicit approval before publishing.
For skills I make my own, but most important is the custom setup i have. Voice is how I use all of my agents. I have an extremely well optimized voice setup that i custom built so I can talk to my agents and also hear them. The voice stack itself is very low latency and high quality. asr (parakeet v3), tts (omnivoice) take no more then 400-450 ms total as far as latency budget is concerned, rest is on the agents actual decode speed. IMO this setup is crucial for all antigenic work, i can express myself a lot better with speech and also give a lot more context and nuance with voice, i rarely type. I still look at the terminal window because my agent knows to keep the technical details in text form versus barfing them at my voice channel, plus terminal gives me lots of other important data about the agents direction and what hes doing, nothing custom here though. I cant emphesise how important voice is though, it has to be practiced to really understand.
As the models got better I now trust them with longer and longer tasks though I still don't use /goal feature as it has never worked out well for me. Theres no need for micromanagement any more but you still need to be there to steer the ship somewhat. BTW, codex cli compaction is garbage so I made my own custom implementation that works a lot better and allows the thread to be used indefinitely without issues. I strongly suggest everyone makes a new thread after extensive use if you havent made your own implementation.
Theres about a billion other things I could get in to like subagents, cohort groups, orchestration layers, etc... But this is a good start imo.
I do this for a lot of things actually. If the bot can't see/read something, take the content and dump it to a file, then have the bot read that.
Gets around a lot of red tape of asking for approval for "integrations" or when companies are snippy.
For my workflow I primarily use a Codex subscription, but farm out adversarial reviews to Fable to clean up unnecessary gpt-ish code (lots of over-engineering). All my UI planning is done with fable, but implemented with OAI agents once I have a solid very specific plan.
I also pretty much exclusively work at my desktop - if you use multiple systems I can see where this matters.
I do work mainly on my desktop, but let's say I'm traveling right, I'd have to turn it off and was missing access to some pretty important information, so I ended up getting a VM and hosting some of those things on there instead. I still use both, but things I want available, I'll host it on my "homelab" and secure both machines via tailscale. it's pretty easy and it ensures that only my phone, my main mac, and my homelab have access to each other and nothing else.
For my phone I have tailscale + https://termrover.sh/, but https://getmoshi.app/ is also pretty good (herdr integration is paywalled).
Thank you for putting this together!
Cool project!