How FresherHub works
FresherHub tells you how ready you are for a specific tech role and what to fix first. Here is what happens, and exactly how the score is worked out.
The four steps
Pick your target role
Choose one of 11 tech roles, such as Backend Developer or Data Analyst, and tell us your career stage.
Add your evidence
Upload your resume. Connect GitHub so we can look at your public repositories. You can skip GitHub, but your score will usually be lower because we have less proof to go on.
Get your readiness report
We check your evidence against every requirement of the role and give you a score, category breakdown, and the blockers holding you back.
Work through your next best action
Your plan is ranked by how much each action would move your readiness. As you add real evidence, such as a tested, deployed project, your score updates.
How we score readiness
Every role has a published list of requirements, grouped into categories. For each requirement we look for evidence in your resume, GitHub, projects and profile, and rate how convincing the best piece is. A claim on its own counts for much less than work we can actually see.
| Level | Counts as | What it means |
|---|---|---|
| Missing | 0% | We found nothing for this requirement. |
| Claimed | 25% | You say you have it, for example in your resume's skills list, but nothing we can check shows it. |
| Observed | 60% | We can see it in your work. For example, a repository has a few test files. |
| Demonstrated | 80% | Your work clearly shows it. For example, a repository has several test files, or a CI workflow that runs checks automatically. |
| Strong | 100% | The clearest proof we recognise for that requirement. |
A requirement below "Observed" is shown as a gap. One at "Demonstrated" or above is shown as a strength.
How the categories add up
Within a category, each requirement has its own weight, and the category score is the weighted average of its requirements. Your overall readiness is the weighted sum of the category scores. The weights differ by role. Here is one example:
| Category | Share of your score | Includes |
|---|---|---|
| Fundamentals | 10% | JavaScript, Git |
| Backend | 25% | Node.js, REST APIs, authentication, validation and errors |
| Databases | 15% | MongoDB, SQL |
| Production | 30% | automated tests, Docker, a deployed service, CI/CD, logging |
| Professional | 20% | how your GitHub, resume and LinkedIn present your work |
So for a backend role, proving you can ship and run a service (tests, Docker, deployment) counts for more than listing languages.
Which gap to fix first
Not every gap matters equally. For each requirement we look at three things: how much it counts in the role, how far your evidence is from full strength, and whether the role treats it as a must-have. A must-have requirement counts twice. Multiply them together and the biggest results become your top blockers (we show up to eight), labelled high, medium or low impact.
That ranking is what orders your plan, so your next best action is the one that closes the most important gap.
AI explains; it never scores
- The score, the gaps and the blockers all come from the rules above. Give it the same evidence and it gives the same result.
- AI helps read your resume and writes the plain-language explanation of your result. It is handed the finished numbers and cannot change them.
- Evidence levels map to fixed numbers in our code. AI is never allowed to supply a number.
- Clicking "done" on a task does not add evidence by itself. Your score moves when there is new evidence to check.
Want to know what happens to your resume along the way? Read the privacy policy.
See it for yourself
Look at a full sample report first, or check your own readiness for free.