Guides
A clear definition of skill graphs, why they matter, and how they differ from static resumes.
A skill graph is a structured, visual representation of a person's capabilities and the relationships between those capabilities. Unlike a flat list of skills on a resume or LinkedIn profile, a skill graph captures depth, adjacency, evidence, and growth trajectories — turning career development from guesswork into a navigable system.
Think of it as the difference between a spreadsheet of city names and an actual map with roads, distances, and terrain. Both contain location data, but only the map lets you plan a route.
Every skill graph is built from three primitives: nodes, edges, and levels.
Each node represents a distinct skill area. Skills can be broad ("Backend Engineering") or specific ("PostgreSQL query optimisation"). Most useful graphs combine both, creating a hierarchy where broad nodes contain more specific child nodes.
A well-structured graph typically has 15–30 top-level nodes grouped into 3–6 domains. Each domain might expand into finer-grained skills as you add detail. For example, a "Data Engineering" domain might contain nodes for SQL, Spark, Airflow, data modelling, and pipeline testing.
Edges represent the relationships between skills. These relationships can be:
Edges are what make a graph more powerful than a list. They answer the question: "given what I already know, what is easiest or most valuable to learn next?"
Each node carries a depth level. Common scales include:
| Level | Description |
|---|---|
| Exposure | You have seen it, read about it, or completed a tutorial |
| Working | You can use it to deliver outcomes with guidance |
| Proficient | You can use it independently and make design decisions |
| Expert | You can teach it, architect systems with it, and handle edge cases |
Levels should be tied to evidence, not self-assessment alone. A shipped project, a published paper, a passed certification, or a production incident you resolved are all valid evidence anchors.
Most people carry a vague sense of what they are good at. A skill graph forces you to externalise that knowledge and confront the gaps. You might discover that you have deep backend skills but no observability experience — a gap that would block a promotion to staff engineer.
Instead of "I should learn more about data," a skill graph lets you say: "I am proficient in SQL and working-level in Spark. To reach the data engineering role I want, I need to get to proficient in Spark and add exposure to Airflow." That specificity turns learning from an obligation into a targeted plan.
Managers, mentors, and hiring teams can look at a skill graph and immediately understand what someone brings to a team. Promotion discussions move from subjective impressions to structured evidence.
A resume goes stale the moment you stop editing it. A skill graph grows with you — each project, certification, or role transition adds new nodes and evidence. Over months and years, it becomes a complete picture of your professional trajectory.
| Dimension | Resume | Skill Graph |
|---|---|---|
| Structure | Linear, chronological | Networked, relational |
| Update cadence | Updated before each job search | Continuously maintained |
| Depth | "5 years of Python" | "Proficient in Python with evidence from 3 production services and 2 open-source contributions" |
| Relationships | Skills listed independently | Shows how skills connect and transfer |
| Growth planning | No built-in forward view | Highlights gaps relative to target roles |
| Evidence |
A resume is optimised for a recruiter scanning for keywords. A skill graph is optimised for you — for understanding where you stand, where you want to go, and what the shortest path looks like.
Skill graphs are not limited to software engineers, although that is where they originated. They are used by:
The fastest way to build your first skill graph is with Skill Graph's generator. Upload your CV or transcripts, and the AI extracts your skills, groups them into domains, and assigns initial levels. From there, you refine, add evidence, and start planning.
For a hands-on walkthrough, see the How to Build a Skill Graph guide.
No. Skill graphs work for any role where capabilities matter — including design, operations, sales, marketing, and leadership. The node structure adapts to any domain.
A monthly review is usually enough. Add extra updates when you complete major projects, earn certifications, change roles, or receive significant feedback.
A skills matrix is a flat table of skills and ratings. A skill graph adds relationships (edges) between skills, making it possible to see adjacencies, prerequisites, and transfer paths — information a flat matrix cannot represent.
Yes. Skill Graph includes a shareable public link for your graph. You control what is visible, and the graph format gives recruiters a richer signal than a PDF resume.
| Implied by job titles |
| Explicitly linked to artifacts |