See What Your Engineering Team Can Actually Do
What does skills visibility look like for engineering teams?
For engineering leaders
Know what your team can actually do. Surface capabilities you did not know existed. Identify language and framework gaps. Staff projects on validated skills, not assumptions. Know what exists internally before you hire externally.
For engineers
Validated skills that travel with your career. Know where you stand. See where to develop. A profile based on demonstrated capability, not self-assessment.
What Skills Intelligence provides today
Everything described here is live and shipping.
Granular proficiency scoring
Novice-to-Expert scale (1–5) per skill. Two engineers with the same Java score get differentiated profiles.
Evolving profiles
Proficiency change indicators: Improved, Newly Verified, Unchanged. Track development over time.
Org-wide dashboard
Proficiency distribution by technology, language, and skill across your organisation.
Coverage analysis
Single points of failure flagged by skill and team. Know before it becomes a project risk.
AI readiness scoring
Prompt engineering, AI debugging, and output evaluation capabilities assessed.
Skills Programs
Container for multiple assessments with lifecycle controls, periodic scheduling, and self-serve access.
VS Code for internal assessments
Same IDE as Screen and Interview. Code Replay for managers. Feels like work, not a test.
AI-generated feedback
Actionable development guidance on every coding task. Engineers get something back, not just a score.
Not live today:
Actionable development guidance on every coding task. Engineers get something back, not just a score.
How is validated skills data different from what HR systems provide?
Most enterprise skills data comes from self-reports, manager estimates, or AI inference. Validated skills data comes from observing engineers demonstrate capability in a structured assessment environment. It tells you what someone has shown they can do, not what they claim or what an algorithm guesses.
The data quality spectrum
Low fidelity
Self-reported
Engineers list skills on profiles. Rarely updated. Poorly calibrated.
Medium fidelity
AI-inferred
Inferred from Jira, GitHub, job titles. Captures activity, not proficiency.
High fidelity
Job-simulated
Engineers demonstrate capability on realistic tasks in a structured environment.
How the main platforms compare
| Platform | Skills data source | Engineering depth | Primary buyer | Best for |
|---|---|---|---|---|
| Codility | Job-simulated in VS Code | Deep: per-skill proficiency from realistic tasks | Engineering leaders | Validated, granular engineering skills data |
| iMocha | Assessments + AI inference | Broad: tech and non-tech | HR / TA / L&D | Cross-department skills assessment |
| Workera | AI-adaptive assessments | Broad: eng, data, product, sales | L&D / C-suite | AI readiness, workforce-wide verification |
| Pluralsight | Course completion + Skill IQ quizzes | Learning-oriented, self-reported | L&D | Upskilling benchmarks (not for hiring) |
| HackerRank | Coding + 360-degree reviews | Tech-origin, expanding | HR / L&D | High-volume developer hiring |
| SkillPanel | AI inference (TechWolf) | Broad workforce | CHROs / HR ops | Combined assessment + skills management |
| Workday | AI inference (TechWolf) | Broad workforce | CHROs / HR ops | Skills data inside existing HCM |
| Eightfold AI | Deep learning inference | Broad workforce | CHROs | AI-powered talent matching |
The cost case in numbers
$5,770
Upskill
Average cost to develop existing talent
vs
$14,170
New hire
Average cost to hire externally
5x
More predictive than education-based hiring
McKinsey
25%
Higher retention after 2 years for skills-based hires
McKinsey
40%
More likely to stay 3+ years with internal mobility
How does Skills Intelligence fit alongside other tools?
The skills intelligence landscape is crowded, but most products serve HR buyers, rely on inferred data, or focus on learning rather than operational decisions. Skills Intelligence is designed to complement your existing stack by providing a verified data layer specifically for engineering capabilities.
Where existing tools fall short for engineering
Enterprise platforms like Workday, SAP, and ServiceNow all offer skills management capabilities. These are valuable for broad workforce planning across all departments. But they are designed for HR generalists, not for engineering leaders who need to know whether their team has sufficient depth in React Native for a mobile rebuild or enough Terraform experience to handle an infrastructure migration.
Learning platforms like Pluralsight (opens in a new tab) measure skills as a diagnostic for learning recommendations. Their Skill IQ assessments are useful for identifying learning gaps, but Pluralsight is explicit that these assessments are not designed for hiring or employment decisions (opens in a new tab).
Skills intelligence platforms like iMocha (opens in a new tab) and Workera (opens in a new tab) offer broader assessment and verification across all roles. If you need to assess skills across your entire workforce, they are worth evaluating. If your primary need is deep, validated intelligence specifically about your engineering team’s capabilities, that is where Codility is focused.
Codility as the engineering verification layer
Skills Intelligence currently integrates with Workday (OAuth 2.0), SAP SuccessFactors, BambooHR, and Personio (via StackOne), with a migration in progress that will expand coverage to 85+ HRIS integrations. Skills data can also be exported via Open API. Bulk user import supports up to 10,000 employees.
Think of it like this: your HRIS tells you that you employ 200 engineers. Codility tells you what those engineers can actually do.
What happens to your assessment platform when hiring slows?
When hiring volume drops, most assessment platforms sit unused. Skills Intelligence changes this: the value shifts from “who should we hire?” to “what can our team do, and where should we invest?”
You have seen this before: a downturn hits, hiring freezes go into effect, and the assessment platform you signed a contract for during a growth phase becomes shelf-ware.
Skills Intelligence breaks this dependency. When hiring slows, the questions engineering leaders face do not go away. They intensify. Which capabilities are we at risk of losing? Can we redeploy engineers from completed projects to new ones? Where should we invest limited training budget for maximum impact?
These are questions that validated skills data answers directly. And answering them becomes more urgent, not less, when hiring is not an option.
The retention case in numbers
89%
Say upskilling costs less than hiring
Pluralsight 2025
107%
More likely to place talent effectively
Deloitte
8%
Of organisations have reliable skills data
Gartner
What frameworks exist for engineering skills?
SFIA is the most widely adopted formal framework. Many organisations build custom progression frameworks. Skills Intelligence does not impose a framework. It provides the validated data that sits beneath whatever framework you use.
Skills covered
~60 skills across the Engineering Skills Model, designed by occupational psychologists. 1,124 tasks in the library.
Java
Python
TypeScript
JavaScript
React
Next.js
LangChain
LangGraph
Terraform
Kubernetes
Angular
Vue.js
Django
Node.js / Express
Go
Rust
SQL / PostgreSQL
MongoDB / MySQL
Redis
Playwright
Selenium
iOS
Android
Azure
AWS
Flutter / Dart
Symfony
Ruby on Rails
Laravel
SAP
Oracle
Bash
C- React Native
Which assessment method best predicts job performance? (Sackett et al., 2022)
Correlation with job performance across decades of IO psychology meta-analytic research.
Structured interview
.44
Job knowledge tests
.40
Work history
.36
Work sample tests
.33
Cognitive ability tests
.31
Personality tests
.29
Job experience (years)
.07
Years of education
.06
★ Codility assessments are work sample tests conducted in a realistic VS Code environment. Combined with structured interviews (also supported in the platform), this draws from the top of the validity hierarchy.
Source: Sackett, Zhang, Berry & Lievens (2022), Journal of Applied Psychology
Codility-specific criterion-related validity studies with customer partners are planned for end of 2026. When we have that data, it will appear here.
Our philosophy
Signals, not conclusions
We are transparent about what Skills Intelligence can and cannot tell you.
Assessments provide meaningful, actionable signals. They are not infallible predictions. We think that honesty is more useful than overclaiming, and we think engineering leaders can tell the difference.
An assessment captures how an engineer performed on a specific task at a specific point in time. It is a strong signal of capability. It is not a complete profile of everything that person can do. We provide evidence to inform decisions, not automated verdicts.
Frequently asked questions
What is Skills Intelligence?
A capability within the Codility platform that gives engineering leaders a validated view of the skills across their team, built on assessment performance data. It covers screening, team capability mapping, and workforce planning.