Skill Overview

In its match models, AdeptID uses skills directly associated with candidates and jobs and also infers skills based on job titles and education. Skills are defined as competencies at specific tasks or familiarity with specific subjects and tools acquired through education or experience. In AdeptID’s data model, industry certifications such as Six Sigma or CISSP are classified as a type of skill.

Skill Schema

Skill Example

{
  "name": "Auto Body Repair",
  "skill_cluster": "Vehicle Repair",
  "skill_type": "Specialized"					
}

Skill Taxonomy

Use Cases for Skill Taxonomy

• Skill Standardization: AdeptID extracts skills from free text entered by users, submitted as part of resumes, job descriptions and similar documents. Skill extraction and standardization are satisfied by the Skill Extraction endpoint, which accepts individual skills and large blocks of text such as resumes, job descriptions, etc.

• Skill Inference: In order to infer the broadest possible range of skills that a candidate possesses, AdeptID infers additional skills based on the skills that a candidate has listed on a resume and based on the job titles of their work experiences. Similarly, we infer skills for jobs based on the title and articulated skills. This use case is performed as part of the job matching process.

• Job Matching: AdeptID uses its skill taxonomy to evaluate whether the candidate has the skills required for a specific job opening or an occupational pathway. This use case is satisfied with the Career Matching family of endpoints.

• Match Report: As part of our assessment between a candidate and a job or occupation, we provide a Match Report which explains the match score while providing details on the assessed fit in work history, skills, education, and certifications.

Skill Taxonomy Overview

Our skill taxonomy includes tens of thousands of unique skills which are in turn organized into clusters of similar skills and a skill type: specialized, baseline or certification.