SkillRep.

SkillRep is an API-first, company-contextual skills taxonomy generator that turns a Company name + URL + Job Roles into a structured list of Skills + Proficiency levels — the foundational data layer that powers right-skilling, upskilling, hiring, and workforce planning in large enterprises.

The wedge is company-context awareness. Generic skill taxonomies (LinkedIn Skills, ESCO, O*NET) are static and one-size-fits-all; SkillRep generates a taxonomy specific to the requesting enterprise, with the long-term roadmap moving from taxonomy to ontology — capturing inter-relationships between skills, prerequisites, and emerging-technology signals that turn a flat skill list into a navigable capability graph.

V1 workflow is intentionally narrow: submit Company name, Company URL, and up to 3 Job Roles through a simple form → triggers an n8n workflow → AI generates the Master Skills library, maps Skills to Roles with Proficiency levels, returns structured output. Powered by GPT-4o for Skill generation — chosen because the task demands deep thinking and nuanced skill creation rather than speed; quality matters more than cost or latency since the output is consumed downstream by L&D systems.

Targeting the skills management software market ($455M–$755M in 2025, double-digit CAGR) inside the broader $47B HR Tech category, with a deliberate niche position: the API-first, role-contextualized skills data layer segment is structurally underserved by both incumbent HR tech (which ships UI-first) and adjacent AI HR tools (which don't go deep on skills semantics). Internal users: HR and L&D teams working on workforce planning and skills-gap closure.