Most teams don’t lack information — they lack a way to search product, design, and engineering context as one system; this article explores what happens when you turn Linear and Figma into searchable markdown and let humans and AI reason across both.
Product intent is in Linear. Design detail is in Figma. Implementation reality is in code. Each system is useful on its own, but the moment you need to answer a question that crosses all three, things get awkward. You open tabs, search manually, ask in Slack, and reconstruct the answer from fragments.
That is tolerable for humans. It is much worse for AI.
At Gigaverse, we wanted something different: not a new source of truth, and not a replacement for Linear or Figma, but a shared text layer that makes product and design context searchable, diffable, and usable by both humans and agents.
That is the role of two repositories:
aviadr1/issueclawaviadr1/figmaclaw
Together, they turn Linear and Figma into git-native markdown that can be searched alongside code. Once that exists, a different class of workflow becomes possible.
TL;DR
This article is about a simple idea with surprisingly large consequences:
Mirror Linear and Figma into markdown, keep that mirror up to date, and use it as a searchable working memory layer for your team.
If you read on, you should come away with:
- a clear mental model for what
issueclawandfigmaclawdo - a practical sense of why this is useful beyond “exporting markdown”
- real examples of the kinds of questions this unlocks
- a private-by-default way to adopt the same pattern in your own team
The short version is that this makes it much easier to answer questions like:
- Where does this feature already exist in design?
- What did product actually decide here?
- Is this designed on both web and mobile?
- Does the design still match the current implementation?
- What changed in one system that has not propagated to the others?
That is where the real value starts.
The problem: product knowledge is fragmented by tool boundaries
Modern product development is full of cross-system questions.
Not “what does this ticket say?”
Not “what does this Figma page look like?”
But questions like:
- What is the current shape of this feature from spec to design to implementation?
- Which design patterns are duplicated or inconsistent?
- Is this bug contradicting the spec, the design, or the code?
- Which features have been explored deeply in design but barely implemented?
- Where are we at risk of building from stale assumptions?
These are not rare edge cases. They are everyday questions.
And most tools are not built to answer them well.
Linear is good at tickets, docs, and status.
Figma is good at visual design and flows.
Git is good at code and history.
The problem is everything that lives between them.
The core idea
The idea behind aviadr1/issueclaw and aviadr1/figmaclaw is straightforward.
-
issueclawmirrors the Linear side into markdown -
figmaclawmirrors the Figma side into markdown - that markdown lives in git, alongside the rest of your technical context
Once you do that, you get something new:
- Linear stops being only a UI you browse
- Figma stops being only a canvas you click through
- git becomes a searchable memory layer for product, design, and engineering
That is the key shift.
This is not about replacing the original tools. The source systems remain the source of truth. The markdown mirror exists to make them more searchable, more comparable, and more usable in workflows that span multiple systems.
What aviadr1/figmaclaw does
At a high level, figmaclaw turns Figma pages into markdown.
Each page becomes a .md file with two layers:
- a structural layer with things like page IDs, frame IDs, flow information, and metadata
- a readable layer with summaries, sections, descriptions, and flow representations
That gives you something that is simultaneously machine-friendly and human-friendly.
Machines can diff it, hash it, update it incrementally, and search it cheaply.
Humans can read it, grep it, review it, and paste it into AI workflows without having to browse Figma frame by frame.
In the Gigaverse setup, this created a large text mirror of the design workspace: hundreds of markdown files representing pages across dozens of Figma files. That was enough to turn design discovery into a search problem instead of a browsing problem.
What aviadr1/issueclaw does
issueclaw does the complementary job on the Linear side.
It mirrors issues, docs, and related artifacts into markdown so they can live in git as searchable text. That makes product context easier to diff, easier to inspect historically, and far easier to combine with design and code context.
The important point is not just that the data is exported.
It is that once the product layer and the design layer are both present as text, they can be searched together.
That is what unlocks the more interesting workflows.
Why this is more useful than it sounds
If you hear “we exported Figma and Linear into markdown,” it sounds mildly convenient.
That undersells it.
The real benefit is that you get a new reasoning layer across systems that normally do not compose well.
At Gigaverse, this led to a few capabilities that stood out immediately.
1. Full-text search across designs
This was the clearest immediate win.
Once Figma was mirrored into markdown, questions like these became trivial:
- Where do polls appear across the product?
- Which screens mention notifications?
- How much dark mode coverage do we have?
- What onboarding UI already exists?
Those are annoying questions in raw Figma. They become easy when design is searchable as text.
This alone is a strong enough use case to justify the mirror.
2. Feature tracing from spec to design to implementation
One of the strongest tests was tracing Gigaverse’s “going live” feature end to end.
By combining Linear docs, Figma markdown, and implementation issues, we could reconstruct:
- the intended product behavior
- the relevant web and mobile flows
- the implementation work across teams
- and contradictions between different sources
That is not just convenient retrieval. That is actual cross-system understanding.
3. Reconstructing design decisions
Another strong use case was looking at stage widgets.
The Figma side showed iterations, comparisons, and changing layout ideas. The Linear side showed what had been decided and how implementation tracked those decisions.
That let us answer not just “what screens exist?” but “what was explored, what direction won, and how far did it get?”
That is a much more interesting kind of query.
4. Platform parity analysis
This turned out to be a very practical question:
- Which features exist on both mobile and web?
- Which are intentionally platform-specific?
- Where are the gaps?
That kind of audit becomes much easier once the design layer and the issue layer are searchable in one place.
5. Design system audits
This is another category that gets dramatically better once design becomes text.
Instead of clicking through files manually, you can search for:
- empty states
- loading states
- modal patterns
- permission flows
- error states
That makes inconsistency much easier to spot.
The most interesting capability: drift detection
The biggest surprise was that the highest-value use case was not search.
It was drift detection.
Once specs, issues, and designs were all searchable together, it became much easier to spot cases where they disagreed.
At Gigaverse, this surfaced real examples of:
- contradictory toggle defaults across issues, product docs, and QA expectations
- terminology drift between active ticket language and newer design language
- design elements that remained in Figma after they had effectively been removed from the product direction
- exploratory design states with no matching implementation work
This is where the system stopped feeling like a retrieval convenience and started feeling like infrastructure.
Search saves time.
Drift detection saves teams from bugs, confusion, and rework.
Why not just use Figma MCP directly?
Because the two approaches are good at different things.
Figma MCP is best when you already know what you want to inspect. It is excellent for exact visual truth, precise frame inspection, and prototype details.
The markdown mirror is best when you do not yet know where to look.
That distinction matters.
For broad search, inventory, audits, and AI context-building, markdown is dramatically more efficient. It is faster to search, cheaper to process, and much more compact for LLM workflows than raw API payloads or screenshot-heavy browsing.
The best workflow is not one or the other.
It is:
Use the markdown layer to narrow the search space.
Use Figma MCP only for the specific frames where visual precision matters.
That division of labor turned out to be extremely effective.
How figmaclaw stays current
A one-time export would be mildly useful.
What makes figmaclaw interesting is that it is incremental.
At a high level, it works like this:
- tracked Figma files are checked for changes
- unchanged files or pages are skipped
- changed pages are updated
- changed frames are the ones that get re-enriched
That means the system does not need to rebuild the entire design workspace every time someone makes a small edit.
This matters for both speed and cost.
It also means the mirror can stay reasonably close to the actual design source of truth without turning every sync into a huge batch process.
Why sync and enrichment are separate
This is one of the smartest parts of the architecture.
There are really two different problems here:
- keeping the mirror structurally up to date
- making the mirror richly readable
The first part is deterministic and cheap.
The second part is interpretive and more expensive.
So the system treats them separately.
The sync layer updates the structure.
The enrichment layer turns that structure into useful prose.
That is a better architecture than trying to do everything in one pass.
It means the mirror can stay fresh even while enrichment is still running, and it means you can prioritize the highest-value parts of the design corpus first.
In our case, enrichment is currently in progress and the whole mirrored system should be enriched within a few hours. That matters because it removes the biggest remaining weakness we saw: pages that were structurally present but not yet richly described.
What this means for AI workflows
This pattern is especially powerful if you are serious about AI-assisted development.
LLMs do much better when the input is:
- textual
- structured
- scoped
- and semantically meaningful
A good markdown page description is a far better input than raw design JSON or a folder full of screenshots.
That means this mirror is useful not just for human search, but for:
- implementation assistance
- code review context
- architectural reasoning
- product/design retrieval
- and agent workflows that need to move across systems cleanly
This is one of the reasons the approach feels increasingly important. It is not just about convenience for humans. It is about building a better substrate for machine reasoning too.
How to use this privately on your own team
You do not need to make any of this public.
The default setup should be private.
A simple model is:
- keep Linear and Figma as your source systems
- mirror them into a private git repo with
aviadr1/issueclawandaviadr1/figmaclaw - run sync and enrichment in your own environment
- let your team and internal tools search the resulting markdown
A minimal repo might look like this:
product-memory/
linear/
figma/
The practical flow is:
- create a private repo
- install
aviadr1/issueclawand point it at your Linear workspace - install
aviadr1/figmaclawand point it at the Figma files you want mirrored - run the initial syncs
- schedule periodic syncs via cron or CI
- run enrichment privately and commit the results back into the repo
I have not verified the live README commands for both repositories line by line in this session, so I would use each repository’s current README and CLI help for the exact installation syntax. The important point is architectural: the whole pipeline can remain private end to end.
Who should care
This is especially useful for teams that already have:
- serious product work in Linear
- serious design work in Figma
- fast-moving engineering
- and growing use of AI in development or internal tooling
In other words, this is useful anywhere the real answer to a question usually lives across multiple systems rather than inside one.
The takeaway
The real point of aviadr1/issueclaw and aviadr1/figmaclaw is not markdown.
It is that they turn two important systems of record into a shared, searchable knowledge layer.
That layer is useful because it helps humans and AI reason across product, design, and engineering as one system rather than three disconnected tools.
At Gigaverse, that already made search, feature tracing, platform comparison, and design audits much easier.
The deeper win was drift detection.
That is what made the whole approach feel important.
If you are building software with Linear, Figma, git, and AI-assisted workflows, this pattern is worth serious consideration.
It gives your team something most tool stacks are missing:
a working memory that spans the actual shape of product development.
Top comments (0)