#Skills Intelligence

Skills Intelligence is TechWolf's skill data layer. It turns the work data synced into your tenant into evidence-based skill profiles: for each employee, the skills they've demonstrated; for each job, the skills it requires. Profiles stay current as new data comes in: no surveys, no self-assessments, giving every tool and use case built on top a reliable picture of your workforce.```

#Data sources

Skills Intelligence infers skills from the data sources we can work with per entity. Each entity needs its required sources to produce a profile; optional sources broaden and sharpen it.

Jobs

• Required: job titles and job descriptions.
• Optional: vacancies linked to the job, and other job-related data such as existing skill frameworks.

Employees

• Required: each employee's working history and current job.
• Optional: resumes, education history, learning history, projects, certificates, performance feedback, goals, skill notes, and tickets.

Courses

• Required: course titles and course descriptions.

Vacancies

• Required: vacancy titles and vacancy descriptions. Vacancies play a double role: they receive a skill profile of their own, and they feed the skill profile of the job they are linked to.

#Key attributes

Skills Intelligence is organised around a set of connected pieces of data:

• Skill profile, the set of skills inferred for one subject, with the evidence behind each skill. Employee skill profiles and job skill profiles are the two main kinds, and the same concept extends to vacancies and courses.
• Employee skills, the skills each employee has, inferred from the data the client provides about their employees.
• Job skills, the skills a job needs, inferred from the organisation's job data.
• Vacancy skills and course skills, the skills an open job requires and the skills a course develops.
• Proficiencies, the skill level attached to a profile: the target level for a job, and the level seen in the data for an employee.
• Critical skills, the skills that most drive performance in a job.
• Skill ontology, TechWolf's universal knowledge graph of skills, including how skills relate and co-occur. Skill adjacencies, how closely skills relate to one another, are an inherent part of the ontology and are used when comparing profiles.
• Skill taxonomy, the organisation's own hierarchy of skill domains and clusters, used to organise and report on skill data.
• Skill-based matching, comparing profiles by how well one covers another's required skills.

#How it works

Skills Intelligence produces every profile the same way, whatever the subject:

• Infer. Specialised models, tuned to each type of data, read each source and extract the skills it evidences.
• Aggregate. Skills from across the sources are combined into one profile using two principles: frequency, where a skill seen across more sources is more likely to belong, and reinforcement, where related skills seen together strengthen each other.

Inference is recurring: profiles are kept current as new data arrives, and TechWolf updates its underlying models every quarter. The models are trained only on publicly available job-posting data, never on customer data.

#Availability

As a data layer, Skills Intelligence delivers its data wherever it is needed:

• Console: explore the skill data and govern job skill profiles.
• Exports: skill data is extracted as CSV or Excel files, on demand or on a recurring schedule, ready to load into a data warehouse or open in a spreadsheet.
• API: Skills Intelligence is API-first; the SkillEngine API exposes skill profiles and matching for programmatic access.
• Agent: the Analyst Agent answers skill questions in natural language, drawing on the same data.
• Assistant: employees see the skills inferred for them and confirm, adjust, or add skills, inside the tools they already use.
• Partner integrations: skill data flows back into HR platforms such as Workday and SAP SuccessFactors, and into reporting environments such as Visier and Crunchr.