Skills Assessment
How VibeLevel maps every assessment to a canonical Skill Registry, target role, and seniority level, then scores candidates across seven dimensions.
Skills, not keywords
Every VibeLevel assessment is tied to a standardized Skill Registry — a curated list of skills and technologies — instead of free-text tags. This is what makes scores comparable across candidates, roles, and seniority levels.
What is the Skill Registry?
VibeLevel scores against a centralized, curated Skill Registry rather than arbitrary text tags a manager types in. Each skill is a canonical entry with a stable id, a display label, and a set of aliases that fold common spellings and package names into one skill — for example py and python3 both resolve to Python. Assessment content is tagged against these entries: the tagger must map to an existing skill or log a proposal for review — it never invents a new string. Because every assessment draws from the same list, "React" always means the same thing across your whole candidate pool.
Skills are organized into families:
| Family | Example skills |
|---|---|
| Languages | Python, TypeScript, JavaScript, Java, C++, Go, Rust, Ruby, SQL, Shell scripting |
| Frontend frameworks | React, Next.js, Vue, Angular, Tailwind CSS |
| Backend frameworks | FastAPI, Django, Flask, Express, NestJS |
| Datastores | Relational and NoSQL databases |
| Cloud & DevOps | Docker, Observability and logging (metrics, tracing) |
| API & Integration | REST and service integration |
| Generative AI | Generative AI, LLM integration, Prompt engineering, RAG, AI agents, LLM evals & guardrails, Embeddings and vector search, Vector databases, Model fine-tuning |
| Machine Learning | Classical ML, feature engineering, model evaluation |
| Data | Data processing and analysis |
| Architecture & system design | System design, concurrency, caching, security |
| Engineering fundamentals | Algorithms, data structures, problem solving, testing |
| Non-coding | Subject-matter competencies for writing / GTM roles |
How an assessment is configured
Three registry fields pin an assessment to the right skills and bar.
Target role & role family
Each assessment targets a role, and every role belongs to a role family. A role also carries a default set of core skills, so tagging starts from a sensible baseline. The active role families are:
| Role family | Example roles |
|---|---|
| Engineering | Backend, Frontend, Full-stack, QA / SDET, Security, Production Support |
| Data & AI | AI Engineer (Generative AI), Data Engineer, Data Analyst, Data Scientist |
| Product & Design | Product Management, Designer |
| Go-to-market & Non-technical | Marketing & GTM, Sales, Technical Writer, Customer Support |
| Business & Operations | People / HR, Talent Acquisition, Finance / FP&A, Project Management |
Seniority level
An assessment also targets one of four seniority levels. Seniority is the ownership and ambiguity the task expects — it is separate from difficulty (raw problem hardness). A Senior assessment can be intermediate difficulty, and a hard algorithm puzzle can still be Junior-level if it needs no system-design depth.
| Level | Rung | Experience | The bar |
|---|---|---|---|
| L1 | Junior | 0–2 yrs | A well-scoped task done with guidance — clean, working, readable code. |
| L2 | Mid | 3–5 yrs | A feature owned end-to-end: validation, error handling, core-path tests. |
| L3 | Senior | 6–9 yrs | Production-grade: structured errors, failure-mode tests, clear boundaries, trade-offs visible. |
| L4 | Staff+ | 10+ yrs | Everything Senior, plus handling ambiguity: interfaces others build on, explicit assumptions, restraint. |
Seniority is not difficulty
Difficulty measures how hard the problem is. Seniority measures how much scope, ambiguity, and ownership the candidate must handle. They are set independently.
Required & observed skills
Skills attach to an assessment in two layers:
- Observed (supported) — every skill the assessment content exercises. VibeLevel derives these automatically when the assessment is published, each with a confidence score.
- Required (selected) — the subset the hiring manager chooses to score against. Selection is constrained to the observed set, so a scored skill is always genuinely covered by the work — there is no "scored but not tested" gap.
The required skills map directly to candidate performance telemetry: how a candidate does on those skills flows into the dimension scores and the dashboard reporting below.
The seven evaluation dimensions
Coding assessments are scored 0–10 on seven dimensions, in two groups. The weights below are the defaults — managers can adjust them per assessment.
How they work with AI
| Dimension | Weight | What it measures |
|---|---|---|
| Prompting Effectiveness | 10 | How clearly and effectively the candidate communicates tasks to the AI. |
| AI Pairing Effectiveness | 20 | How well the candidate steers, collaborates with, and course-corrects the AI. |
| Product Thinking & Validation | 15 | Solving the right problem — user/product perspective, edge cases, outcome validation. |
| Problem Solving & Design Thinking | 20 | Whether the candidate drove the decomposition, architecture, and approach. |
What they deliver
| Dimension | Weight | What it measures |
|---|---|---|
| Requirements Completion | 15 | How fully the submitted code meets the assessment's requirements. |
| Independent Code Understanding | 10 | Ability to read, debug, and modify code independently, not just accept AI output. |
| Testing & Verification | 10 | Use of tests, previews, and validation to verify correctness before submitting. |
Writing assessments
Non-coding (writing / artifact) assessments use five dimensions instead: Prompting Effectiveness, AI Collaboration, Strategic Thinking, Deliverable Quality, and Structured Thinking.
Skill Insights on the dashboard
Every scored submission feeds the recruiter dashboard's Skill insights matrix. It ranks the canonical skills your candidates demonstrated and breaks each one down two ways, side by side:
- Candidate score band — Novice → Developing → Proficient → Expert
- Seniority — Junior → Mid → Senior → Staff+
Each cell shows the share of candidates in that group who demonstrated the skill — darker means a higher rate. You can scope the matrix to a single role and switch between coding and writing skills. At a glance it shows demonstrated versus required coverage: which of your required skills the pool is strong on, and which are thin, broken out by seniority.
See Getting Started for how a candidate's own work is separated from AI output, and how to share a report.