Skillsync lets you move your AI chats across every coding agent. Most of our work exists as conversations, which are currently scattered across our agents. Though stored locally, these conversations use different formats. This is annoying because you cannot simply switch between agents without starting over. We get locked into a single provider and their agent as we invest in skills and memories over time. Skillsync acts as a universal converter. It moves the entire session, including all the messages, reasoning and tool calls so you can pick up right where you left off.
Skillsync collects all your sessions in one place and makes them searchable. It breaks down what each session is carrying, including which loaded skills the agent is actually using, making stale context easy to spot. You can also create shared workspaces to sync sessions across your team. You can build your own closed loop systems. Everything runs locally except when you share to workspaces.
The core is an open-source Rust engine called txcript (https://github.com/skillsynchq/txcript). It translates a session from one agent's on-disk format into another's, mapping conversation, reasoning, and tool history. Think ffmpeg or pandoc, but for agent sessions.
On top of that engine is a local-first desktop app. Your agent sessions are normally scattered across different tools' folders in formats you'd never read by hand; the app surfaces them in one place with a UI that makes them actually readable, the conversation, the reasoning, and the tool calls, so you can revisit what happened, move a session into another agent, or share it with a teammate.
Skills and memory are stored as portable, human-readable markdown you own, and exposed to any agent over MCP for search and selective retrieval.
Sessions and translation run locally on your machine. The one thing that leaves is what you explicitly share into a team workspace.
It’s been surprising to us to see how locked-in people already are without realizing it. You don't notice you're trapped in one agent or harness until you try to leave, and by then you've got months of sessions stuck in a format only that tool can read. The lock-in is invisible right up until it's expensive.
Once sessions are portable, they stop being disposable logs and become something you can actually build on, spotting patterns in how you work, handing a session off to someone else, letting a non-technical teammate pick up where an engineer left off. The session turns out to be the unit of collaboration, and right now it's being thrown away.
Before Skillsync, Nars and I built an open-source payment orchestrator that let merchants route across many processors instead of getting locked into one (30k+ GitHub stars). Fighting vendor lock-in by making incompatible systems interoperate was the whole job. We came into YC with a different idea, but the more we lived inside coding agents the more we saw the same problem from the other side: your context is locked into whatever agent you start with, because every agent stores sessions differently and none of it moves.
Some of the things we’re seeing people do with Skillsync are:
- Moving a session between Claude Code, Codex, and Cursor mid-task, including when you hit a usage limit on one and want to keep going on another.
- Thinking through a problem in a browser Claude chat, then handing the whole thread to Claude Code or Codex to build.
- Sharing research sessions with a team. Everyone uploads their sessions, so teammates and their agents can build on each other's work instead of repeating it.
It's available as a Mac app, CLI and MCP. Here's the demo: https://youtu.be/7hVhSnSKGl8. Would love to hear your feedback and answer questions!
i do the same for apple notes and imessage. so very portable
https://apps.apple.com/us/app/exporter-notes-to-markdown/id6...
Also curious, do you further process the synced files on dropbox at all? how are you using them today?
In my experience 95% of the session transcript I don't care about.
But there are some that I want to keep and maybe come back one month later.
Usually it's not even engineering-related. Maybe doing some research on visas, relocation.
But sometime also doing project research and sometime I want to come back to the same topic 2 month later(because new things came up) and I want my new agent to go and read prior conversation.
And shipping sessions to colleagues is critical as well. My team relies heavily on a homegrown /resume skill that forces the harness to read every part of a Claude Code JSONL into context, letting us merge colleagues' sessions into our own.
What I thought this might be from the title, though, is a separate but related problem. The more semi-technical people in a company who start to use agentic systems, the more likely they are to create a vital playbook as a "skill" - but one not designed for easy tracking in a repository, that relies on local state, references their name, references other skills and rules that they've developed for their own work, etc. Cleaning these, figuring out what should be promoted across a team, figuring out when someone makes an update whether that's something that deserves human review, syncing across people who haven't used git/pull requests before - it's a big challenge that's highly context-dependent. If skills are the place where guardrails exist, "who watches the watchmen?"
There's an interesting duality here, because a colleague being able to ship a session where they iterated on such a skill, to a colleague who can understand the context of their change, is incredibly valuable. But it still relies on a human in the loop, and doesn't scale as a result. I think there's a really interesting design space here.
https://github.com/gitsense/chat/tree/readme-scale-knowledge
Since I only support Pi natively, your solution will make it possible for codex/claude/etc. users to discover their conversations in my app. Will definitely include this as workaround for making codex, claude and other sessions available for discovery.
you can use that internally for this
The only thing that seems to be missing is synching but I have a workaround for that but it won't be near realtime. For example, Pi changes are usually a second or less behind the Pi session log.
With txcript, it will probably be 5 seconds which is fine since the use case will mostly revolve around discovery, organizing and inspection.
API call to server
Then
Server leverages chrome plugin to send your llm call through the plugin , through the ui, wait for response , intercept, repackage and reroute it back out as a server response.
Then that $20 sub stretches to all uses cases for a developer.
The case I hit all the time is that an idea strikes on my commute, so I pull out my phone and start brainstorming in the Claude app. By the time I'm home I've done the research and chased down the tangents in that conversation. I open my laptop, use Skillsync to continue it in Claude Code, and start on the implementation.
I’m not encouraging one do this. I mostly route between the free models on OpenRouter and OpenCode free models, and I mostly write simple apps where I can casually oversee even a small model and get awesome results.
But if you are out there spending 200 dollars a month, consider a few hacks, because that’s just overpriced. We are developers after all, they aren’t supposed to be able to trick us :)
Edit:
Piracy, with the above , would look like a universal server that routes between all provider UIs. That means the end user doesn’t even give a shit which provider it goes to, serious token usage, serious theft.
Someone could make this , I suppose. In a way, commodification is a clean solution to the inevitable theft. It’s better it just be so darn cheap and ubiquitous.
Have you tested to see if there was any degradation happening when switching from one model to the other ? I mean some must be inevitable (maybe not!), but how much?
Because syncing is trivially solved by just telling the agent to keep a work journal in markdown
The markdown journal is a fair way to do it, and a popular one. It has a couple of limits though.
A journal comes out of a prompt, so it records one way of looking at the session. Whatever that prompt didn't ask about is gone, and you usually find out which detail you needed much later. But the raw session still has it.
Being able to unify the schema lets you do things notes can't. You can continue a teammate's session on your own machine, or pick up a claude.ai conversation in Claude Code or Codex. The new agent gets the transcript: all the decisions in the trajectory, not a summary of it.