> ## Documentation Index
> Fetch the complete documentation index at: https://docs.matchr.in/llms.txt
> Use this file to discover all available pages before exploring further.
# How match scoring works
> What goes into your match score, and how to read the result
When you open a LinkedIn job posting, Matchr reads the job description and compares it against your active resume across three factors:
| Factor | Weight | What it measures |
| -------------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------- |
| Skills overlap | 50% | How well your resume's skills cover the required and nice-to-have skills in the posting, judged on substance, not exact keyword matches |
| Semantic fit | 30% | How closely your resume's overall content aligns with the posting, beyond just listed skills |
| Experience fit | 20% | Whether your years of experience meet what the posting asks for |
These combine into a single score from 0 to 100.
## Reading the result
Below the score, the side panel breaks the match into three sections:
* **Why it fits**: the specific things in your resume that are driving the score up.
* **Gaps**: specific, concrete gaps between the posting and your resume, not generic advice.
* **Suggestions**: ideas for addressing the gaps, informed by what's already in your resume.
A score of 75 or higher generally means a strong match worth applying to; scores from roughly 55 upward are still worth considering. Below that, the gaps are usually significant enough to be worth closing first, either by hand or with the [resume optimizer](/optimizer/overview).
Revisiting a job you've already scored doesn't cost another credit, Matchr caches the result for that resume and that posting. Re-scoring only happens if you switch to a different resume or the posting changes.