--- title: "What is a skills taxonomy? Definition and examples | Findem" description: "A skills taxonomy is a shared, structured list of skills that groups and names them consistently. Learn how taxonomies work, with ESCO and O*NET examples." url: "https://www.findem.ai/glossary/skills-taxonomy" --- # What is a skills taxonomy? ← Back to glossary HR & Workforce Planning A skills taxonomy is a structured, shared vocabulary of skills. It gives each skill a standard name and description, groups related skills into categories, and often links skills to occupations or roles. Organizations use one so that job descriptions, employee profiles, learning content, and workforce reports describe the same skill in the same way. ## How a skills taxonomy works Public classifications show the common parts. [ESCO](https://esco.ec.europa.eu/en/about-esco/what-esco), the European Commission's multilingual classification of skills, competences, and occupations, describes itself as working like a dictionary that describes, identifies, and classifies occupations and skills. Its [skills pillar](https://esco.ec.europa.eu/en/classification/skill-main) holds more than 13,000 concepts in a hierarchy with four sub-classifications: knowledge, skills, attitudes and values, and language skills and knowledge. Each concept has one preferred term, a set of non-preferred terms, and a description, and ESCO links its skills to more than 3,000 occupations. In the US, the Department of Labor's [O*NET Content Model](https://www.onetcenter.org/content.html) organizes information about jobs and workers into six areas. It keeps worker characteristics such as abilities and work styles separate from worker requirements such as skills, knowledge, and education, and both separate from the work activities and tasks of each occupation. Most skills taxonomies include some version of these parts: - **Standard names:** One preferred label for each skill, with synonyms mapped to it. - **Hierarchy:** Groups that run from broad categories down to specific skills. - **Types:** A way to tell skills apart from knowledge, abilities, or attitudes. - **Links to work:** Connections between skills and the occupations, roles, or tasks that need them. Frameworks draw some of these lines differently. ESCO separates skills from knowledge but makes no distinction between skills and competences. NIST's [Playbook for Workforce Frameworks](https://www.nist.gov/itl/applied-cybersecurity/nice/nice-framework-resource-center/playbook-workforce-frameworks) builds its model from task, knowledge, and skill statements, defines skill statements as what a learner can do, and notes that its work roles are not the same as jobs or position titles. ## Why a skills taxonomy matters for talent teams - **Matching people to work:** ESCO says its machine-readable concepts let online platforms match jobseekers to jobs based on skills and suggest training to people who want to reskill or upskill. - **Finding gaps:** NIST says employers use workforce frameworks to run workforce assessments, identify gaps, and improve recruitment and retention. - **Clean reporting:** Findem's guide to [unifying the talent tech stack](https://www.findem.ai/blog/talent-tech-stack-integration) lists normalized job and skill taxonomies as a data quality control, so titles, skills, and statuses map to a shared vocabulary instead of free-text values that break rollups. It suggests tracking the percentage of records that use approved values. ## How Findem approaches skills taxonomies A taxonomy gives a skill a name. Findem's [Success Signals](https://www.findem.ai/why-findem/success-signals) add evidence about how experience shows up in a career. They are verified patterns in a person's background, behavior, and impact, created by recruiters, search professionals, and people leaders. One example is transferable competency mapping, which shows how military leadership translates into operational roles. Findem counts more than 200 signal categories and 75 to 100 Success Signals per profile. Findem's [glossary](https://www.findem.ai/glossary) describes attributes as verifiable facts derived from 3D data, such as scope increases, technical depth, tenure at a specific company stage, and industry specialization. Attribute search finds talent based on those verified attributes rather than keywords or job titles. In the [Data Labeling Engine](https://www.findem.ai/platform/data-labeling-engine), recruiters, researchers, and domain specialists define the patterns that signal potential, performance, and fit, and Findem continuously refreshes and enriches signals as careers change. ## Further reading - [Unifying your talent tech stack: Strategies for seamless integration and data quality](https://www.findem.ai/blog/talent-tech-stack-integration) ## Related terms - [Skills-based hiring](https://www.findem.ai/glossary/skills-based-hiring) - [Skills intelligence](https://www.findem.ai/glossary/skills-intelligence) - [Attributes](https://www.findem.ai/glossary#term-attributes) - [Skills gap analysis](https://www.findem.ai/glossary/skills-gap-analysis) - [Success Signals](https://www.findem.ai/glossary#term-success-signals) ## Sources - [What is ESCO?](https://esco.ec.europa.eu/en/about-esco/what-esco) European Commission - [ESCO skills and competences](https://esco.ec.europa.eu/en/classification/skill-main) European Commission - [The O*NET Content Model](https://www.onetcenter.org/content.html) O*NET Resource Center, sponsored by the US Department of Labor - [Playbook for Workforce Frameworks](https://www.nist.gov/itl/applied-cybersecurity/nice/nice-framework-resource-center/playbook-workforce-frameworks) National Institute of Standards and Technology - [Success Signals](https://www.findem.ai/why-findem/success-signals) - [Data Labeling Engine](https://www.findem.ai/platform/data-labeling-engine) - [Findem glossary](https://www.findem.ai/glossary) - [Unifying your talent tech stack](https://www.findem.ai/blog/talent-tech-stack-integration) The DNA of talent made visible. Success Signals turn recruiter expertise into labeled indicators of performance and fit, giving AI the context to find stronger matches and reduce rework. [See Success Signals](https://www.findem.ai/why-findem/success-signals) [Request a demo](https://www.findem.ai/request-demo) ## Frequently asked questions. **What is the difference between a skills taxonomy and a skills ontology?** Usage varies. A taxonomy usually means a hierarchy that groups and names skills. Ontology is often used for a richer model that also records relationships, such as which skills relate to each other or to occupations. Many published frameworks, including ESCO, combine a hierarchy with links to occupations. **What is the difference between a skills taxonomy and a competency model?** A skills taxonomy is usually a shared vocabulary of skills across many roles. A competency model usually describes the competencies and proficiency levels needed for a specific role or job family. Frameworks draw the line differently, and ESCO, for example, makes no distinction between skills and competences. **What are examples of skills taxonomies?** Public examples include ESCO, the European Commission's multilingual classification of skills, competences, and occupations, and the O*NET Content Model from the US Department of Labor. NIST's NICE Framework uses task, knowledge, and skill statements for cybersecurity work. **How do you build a skills taxonomy?** Most teams start from a public framework such as ESCO or O*NET rather than a blank page. They choose preferred names, map synonyms, group skills into categories, link skills to roles, and assign an owner to keep it current as work changes. **How does Findem relate to a skills taxonomy?** Findem adds evidence about how experience shows up in people's careers. Success Signals are verified patterns created by recruiters, search professionals, and people leaders, and attribute search finds talent by verified attributes rather than keywords or job titles.