What if your entire product system — everything from spec to shipped code — could be searched like a single document?
Most teams don’t realize this is possible. And it’s a problem.
Right now, your product intent lives in Linear. Your design detail lives in Figma. Your implementation reality lives in git. Each system is individually brilliant. But the moment you need to answer a question that crosses all three — like “Is this designed on both web and mobile?” or “Does the current code match what product actually decided?” — you’re stuck. You open tabs. You search manually. You ask in Slack. You piece together fragments.
Humans tolerate this friction. AI absolutely does not.
The Real Problem Isn’t Missing Information
It’s missing connections.
Your team probably has excellent Linear documentation. Your designers probably have beautiful, detailed Figma files. Your engineers probably have well-organized repos. The issue is that these three systems don’t compose. They’re like three brilliant libraries with incompatible catalogs — useful if you need something from one shelf, but useless if you need to cross-reference across all three.
Here’s the thing: most tools are designed to be the source of truth for their domain. Linear owns tickets. Figma owns canvas. Git owns code. None of them were built to answer questions like “what is the complete shape of this feature from spec to design to implementation?” or “which design patterns are duplicated across our product?” or “where are we building from stale assumptions?”
These are everyday questions. And they’re hard to answer right now.
Enter the Markdown Mirror
Two repositories — aviadr1/issueclaw and aviadr1/figmaclaw — exist to solve this.
The idea is deceptively simple. Instead of replacing Linear or Figma, these tools create a git-native markdown mirror of both systems. Your Linear tickets become searchable .md files. Your Figma pages become structured markdown with both machine-readable metadata and human-readable summaries. That markdown lives in git, alongside your code.
Once that exists, everything changes.
“Mirror Linear and Figma into markdown, keep that mirror up to date, and use it as a searchable working memory layer for your team.”
This isn’t just “exporting data.” That undersells it. The real shift is that your product context and design context are now both text — text that can be diffed, searched, versioned, and fed to both humans and AI agents without anyone having to manually browse through tabs.
Linear stops being only a UI you browse. Figma stops being only a canvas you click through. Git becomes a searchable reasoning layer that spans product, design, and engineering.
How figmaclaw Actually Works
figmaclaw takes Figma pages and turns them into markdown files with two layers baked in.
First: the structural layer. Page IDs, frame IDs, flow information, metadata — everything a machine needs to diff it, hash it, or update it incrementally.
Second: the readable layer. Summaries, sections, descriptions, flow representations — everything a human needs to understand what’s on the page without opening Figma.
The result? Machines can process it cheaply. Humans can read it, grep it, paste it into AI workflows. At Gigaverse, this created a text mirror of their entire design workspace — hundreds of markdown files representing pages across dozens of Figma files. Suddenly, design discovery became a search problem instead of a browsing problem.
Why This Actually Matters (The Workflows You Unlock)
Let’s get specific.
Without this, answering “Where does this feature already exist in design?” means someone manually clicking through Figma pages, or posting in Slack, or asking whoever designed it last week. With a markdown mirror that’s searchable and git-native, you grep. Or you feed it to an AI agent. You get an answer in seconds instead of minutes (or never).
Now scale that across every cross-system question your product team asks:
- What did product actually decide here? (Search Linear + Figma together.)
- Is this designed on both web and mobile? (Search design patterns across both platforms.)
- Does the design still match the current implementation? (Diff design markdown against code.)
- What changed in one system that hasn’t propagated to the others? (Git history tells you.)
- Which features have been explored deeply in design but barely implemented? (Search and compare depth.)
These aren’t edge cases. These are daily questions. And most teams answer them poorly because the systems that contain the answers aren’t built to talk to each other.
The Philosophy Here Matters
this isn’t about replacing Linear or Figma.
It’s not about building another “source of truth” that your team has to keep in sync. That would be awful. The source systems stay authoritative. You change a ticket in Linear, the mirror updates. You redesign something in Figma, the markdown reflects it. The mirror is always a reflection, never the primary target.
But here’s why that matters: because the mirror is git-native, it becomes part of your technical workflow. It can be reviewed in pull requests. It can be diffed. It can be searched alongside code. It can be fed to AI agents that need product context. It becomes a shared language between product, design, and engineering.
What’s the Catch?
The honest answer is still being written.
this pattern works best if your team’s workflows are already fairly mature and git-friendly. If you’re still manually syncing things across tools, adding another layer might feel like busywork at first. The real payoff compounds over time — it’s the difference between answering a cross-system question in 30 seconds versus 30 minutes.
Also, keeping the markdown mirror up to date requires some automation. The tools handle that, but you need to think about where the sync happens — CI/CD pipeline? Scheduled job? Webhook? It’s not magic, but it’s far simpler than manual export-and-paste workflows that most teams have today.
The Bigger Picture
This matters because it represents a shift in how technical teams think about knowledge infrastructure.
For years, we’ve accepted that product, design, and engineering would live in incompatible silos. We created status-sync meetings and Slack bots and tribal knowledge to paper over the gaps. We wrote documents that fell out of sync. We became expert tab-jugglers.
But what if knowledge composition across systems became the default instead of the exception? What if your product spec, your design system, and your code could reason about each other natively?
That’s not a minor convenience. That’s a fundamental shift in how fast teams can move and how many mistakes they can avoid.
These two open-source repositories are proof that it’s possible. And they’re available for any team that wants to try it.
🧬 Related Insights
- Read more: A Production Bug Made 73% More Revenue Than Any Feature We Built. Here’s What It Taught Us.
- Read more: Docker Just Made Hardened Images Free and Open Source—Here’s Why That Matters
Frequently Asked Questions
How do I set up issueclaw and figmaclaw for my team?
Both are open-source and available on GitHub. You’ll need Linear and Figma API access, a git repo to store the markdown, and a way to trigger the sync (CI/CD, scheduled job, or webhook). The repositories include documentation and examples. Start with a pilot project if you’re not sure.
Will this slow down my design or product workflow?
No. The mirror is read-only from Linear and Figma’s perspective. You keep using those tools exactly as you do now. The markdown export happens automatically in the background. The only change is that your team gains a new search layer — it’s pure addition.
Can I use this if my team uses Jira instead of Linear?
Not with these specific tools, but the pattern is portable. Both tools are open-source, and community members have discussed adaptations for other systems. The core idea — mirror source systems into git-native markdown — works with any API-accessible tool.
What if I use Penpot or Sketch instead of Figma?
figmaclaw is Figma-specific, but again, the pattern is replicable. You’d need to adapt it for your design tool’s API, but the structure — metadata layer plus human-readable summaries — is universal.
