Common Flow is a shared map for human skills - so the skills you've built in any job, place, or life are visible, named, and translatable across contexts. Open methodology, building in public.
We build skills constantly, but most of them stay invisible
Think about everything you've done in the last decade. The job titles, sure. But also the side projects, the responsibilities you took on but never named, the languages you learned, the people you raised, the things you got better at because life made you.
Most of it is invisible. Some of it, even to you.
This isn't a personal failing. The systems we use to talk about skills - qualifications, frameworks, job descriptions - were built for institutional convenience, not for how people actually develop. Skills built in one context don't easily translate to another, and things that don't fit an existing category disappear from the conversation.
Which is where Common Flow comes in. We've set out to change how we visualise our skills and how they work together, so that you can see the full picture of your strengths.
Common Labs
Articles and research that explain what we're building, why we're building it, and how the framework works in practice. The methodology behind the platform is fully indexed by Google Scholar.
It's a Rosetta Stone for skills
Most skills systems are built to be adopted. Common Flow is built to translate.
It works with the frameworks you already use - your qualification system, your industry's competency framework, your employer's job description, your school's curriculum. Common Flow maps between them without asking anyone to abandon what they have.
For individuals, the skills you've built in one context become visible in another. For organisations, the framework you've invested in still works - Common Flow makes it travel.
The work is mathematics and algorithms, not generative AI. Nothing is generated. Same input, same output, every time.
What Common Flow does, in three moves
- Step 1
Maps
Every skill - from any framework, in any language - has a place on a shared 2D map. Two axes, Focus (whether the skill is directed inward or outward) and Mode (whether it's enacted through thinking or doing). So far, the geometry has held across multiple languages, sectors, and frameworks.
- Step 2
Translates
Skills built in one context find their match in another. A military air traffic controller and a civilian operations manager. A nurse and a teacher. A grandmother running a household and a project manager running a team. The geometry can surface where the work overlaps, regardless of what it's called.
- Step 3
Surfaces
Some kinds of work - care, coordination, resilience - are often under-described existing systems. Common Flow surfaces what's implied alongside what's said, so the skills people actually build don't go missing.
It's mathematics, not magic
Skills often seem to work 'by magic' - we know they exist, but we're not quite sure how, and we can't even agree on the basics like what to call them. The framework we've developed is different because it exposes the inner workings of skills and how we develop them.
We developed the framework to solve a problem for ourselves - we wanted to showcase all the ways young people build skills across the things they do in their lives, and not just at school. But along the way the project took on a life of its own and became something bigger. The map gives us a way to place every skill on an equal footing in a shared coordinate system. No more soft vs. hard skills, or wondering which skills are more valuable - instead we can see how skills relate to each other and bundle together into larger competencies.
And it's mathematics, pure and simple. Under the hood, an encoder model maps text onto the 2D geometry - it doesn't generate anything, it just places. No data leaves the system, nothing goes to a third party, and the whole pipeline is open enough to peer into.
Built on work that came before
We aren't the first people to think about how we can understand skills, and there are multiple robust skill classification systems already in existence. Rather than add another raw taxonomy into the space, we wanted to explore the patterns that emerge when you map expert-derived frameworks against each other.
These included national and international frameworks, some of which have existed for decades, and we found that not only did clear patterns emerge from the data, but they also held across decades, countries, and even languages. Frameworks written in Japanese and Chilean Spanish display the same geometry as those written in English.
Our role is not to reinvent the wheel - the work that exists is already excellent, so our job is to translate, not replace.
Questions worth answering
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Common Flow is being built in public. Common Labs publishes the work as it lands — the methodology, the Findings, the limitations, the things that didn't work. Sign up to the newsletter and you'll get the next one when it does.