How does the skills network work?

How does the skills network work—and why time may hide more than it erases

The skills network works like a living map of capability. But it doesn’t just show what you know—it shows when you knew it. In other words, how does the skills network work? It connects people to opportunities based on both the type and timing of skills. That means time isn’t just a footnote. It’s a filter. The network favours freshness. But not all skills have short shelf lives.

A five-year-old certification might not be outdated. It might just be buried. Not gone. Just out of sight. We treat time as a straight line. But inside these networks, things aren’t so clean. Recency fights with reliability. That push and pull defines who gets seen—and who doesn’t.

Let’s dive into how this hidden system works. And why some of your most useful skills might be invisible and not expire.

How Does the Skills Network Work? Visibility vs Value

At its core, the skills network links people, tasks, and knowledge. It pulls in data from CVs, courses, job posts, portfolios, and profiles. Then, it uses that data to determine the right match for a role or a task. But it doesn’t treat all skills equally.

Recent skills show up first. A course finished last month ranks higher than one taken five years ago. A project coded in 2024 beats one from 2020, even if they show the same technique.

Why? The network assumes newer means better. In many fields, that’s fair. In tech, finance, marketing, or law, tools change fast. If you haven’t used them lately, they may not work the same way. But this logic doesn’t fit every skill. Communication, leadership, or creative problem-solving age differently. They don’t expire in the same way.

Yet the network still treats them as if they do.

The Hidden Bias of Recency

Imagine two designers. One did great work in 2019 but has been quiet since. The other finished a course in 2024 but has no real-world projects. Who gets ranked higher? In most systems, the fresher activity wins. Even if the older work was richer, deeper, or more tested.

This is how the skills network works: it’s time-sensitive. It prizes what’s recent over what’s proven. That creates a hidden bias. A gap between what’s useful and what’s visible.

And that’s not just unfair—it’s inefficient.

Do Skills Expire? Not Always

Some skills fade with time. Programming languages change. Legal frameworks shift. Tools become obsolete. But core skills? They deepen. A teacher who led classrooms in 2015 hasn’t lost that skill in 2025. A project manager who built strong teams five years ago still knows how to do it today.

So the real issue isn’t whether the skill still exists. It’s whether the system still sees it.

Why Do Older Skills Get Lost?

There are three reasons the network loses track of older skills:

1. Algorithms punish silence. If you haven’t updated your profile, added new courses, or posted recent work, your data sinks.
2. Formats change. If your past work isn’t tagged in the new language or categories the network uses, it becomes invisible.
3. Data gets buried. Profiles and CVs highlight the latest activity. Older wins get pushed down—even if they matter more.

That’s not about value. It’s about visibility.

What Makes a Skill “Fresh”?

In most systems, “fresh” means recently used, learned, or shared. The network checks the last time you touched a skill. Posted about it. Got endorsed for it. Took a course on it. Wrote about it. Showed it.

But using a skill doesn’t always mean announcing it. If you’ve solved a hundred small problems quietly, that’s still valid experience. The system just doesn’t see it unless you surface it.

So freshness, in this case, is performative. It’s about evidence, not essence.

The Problem With Performative Freshness

Here’s the catch: not all valuable work gets documented. Many people don’t update LinkedIn every time they improve something. Not every promotion includes a public post. Most freelancers don’t log every gig. Parents returning to work don’t have neat timelines. Veterans have years of leadership that rarely fit standard forms.

So performative freshness favours those who frame and share. Not always those who’ve done the most.

That shapes how the skills network works—and who it rewards.

How Can We Surface “Invisible” Skills?

To fix this, we don’t need to erase time. We need to design for it better. Here’s how:

• Weight consistency, not just recency. If someone showed a skill across 10 years, that should matter more than one new course.
• Add decay options by domain. A coding language from 2010 may not hold up. But storytelling or negotiation from 2010 might be more refined.
• Support reflection. Give people tools to reframe older work in today’s context. What did that 2017 project teach them that’s still valid now?
• Let others validate. Endorsements or reviews from peers can revive older skills with fresh trust.

Time matters. But we need to treat it with nuance, not just metrics.

What Can You Do to Stay Visible?

Even if the system is flawed, you can work with it. Here’s how to keep your skills from going dark:

• Revisit old wins. Don’t just share new stuff. Reflect on past projects and post what you learned from them.
• Use new language for old skills. Match your 2016 leadership work to today’s frameworks. The network picks up keywords.
• Stay lightly active. Even one update a month helps. A comment, a course, a blog post—all keep the light on.
• Curate, don’t just collect. Don’t stack every course or skill. Highlight the ones that matter—and explain why.

The goal is to feed the system without faking it. Stay honest. Stay present.

How Does the Skills Network Work With Gaps?

This is where things get tricky. Many people have gaps. Career breaks. Illness. Family. Travel. Redundancy. These aren’t weaknesses. But the network often reads them that way.

Because the algorithms want activity. They read silence as loss. That’s not human—it’s mechanical.

The real challenge? To show that what didn’t get logged still taught you something. A year caring for family builds empathy. A year travelling sharpens adaptability, and a year out of work might have included learning, resilience, or clarity.

These don’t fit clean tags. But they count.

And if the network doesn’t see them, we need to make it look closer.

Networks Don’t Expire Skills—People Do

Let’s be honest. It’s not just the system. Recruiters often skim. Managers pick the safe bet. Hiring still leans toward now over then.

But the network reflects those choices. It doesn’t invent them. It just amplifies them.

That means we all shape the way the system treats time. If we reward depth, the network will too if we ask about the why, not just the when, things shift.

We can choose to treat old skills as seeds, not fossils.

Skills Are Not Fruit. They Don’t Rot

Skills grow. Or they sleep. Some change shape. Others stay solid. Time doesn’t spoil them—it just changes their context. A five-year-old skill is like an heirloom tool. Maybe dusty. Maybe heavy. But sharp in the right hands. What matters is not the date. It’s the depth. Not the freshness, but the fit.

So… Do Skills Expire?

No. Not in the way the network suggests. They just fade from view if not refreshed, reused, or reframed. And that’s not failure. That’s friction. There’s a gap between human learning and digital tracking. We can close that gap. With better systems. Better prompts. Better reflection. And better questions.

So the next time you wonder if your skill is out of date, ask instead: “Is it expired—or just invisible?” Because in the skills network, time hides more than it erases. And you may still be holding value that just needs to be seen again.

Reclaim your invisible skills. Enrol in our online Personal Development courses at Training Tale and stay visible where it matters.