Find skill gaps. Prove they're gone.

KC Skills is built for the full loop, not just the test. It uses AI to generate assessments from the skills you choose, maps results into a live skill matrix, then auto-assigns the training to close each gap and reassesses to prove it worked. The gaps KC Skills finds become learning paths in KC LMS, and completion reads straight back into the matrix on its own. You measure capability, act on it, and prove the change from one connected platform.

With KC Skills you never start from a blank page. Pick the skills a role needs — or let AI suggest them — and KC Skills generates the assessment automatically. Results populate a Skill Matrix that maps proficiency by person, team, and role, plus a My Skills profile for every learner. From there, gaps auto-assign the matching training in KC LMS, and completion feeds back into the matrix on its own. One data model keeps your skills, assessments, and learning aligned across KnowledgeCity, so the matrix stays current without manual upkeep.

KC Skills closes the loop for you. When an assessment surfaces a gap, its Gap-to-Training Loop auto-assigns the matching learning path in KC LMS, with no manual enrollment. That assignment lands instantly, and completion reads straight back into the skill matrix so proficiency updates on its own. Run it for one person, or roll assess → train → reassess out to a whole group with the Campaign Engine. The result: a gap never sits idle waiting for someone to notice it.

KC Skills is designed to prove change, not just spot it. After training, it reassesses automatically and shows proficiency climbing on the same skill matrix that flagged the gap — a clean before-and-after leaders can trust. Skills-Drift Tracking then watches scores over time so you catch slippage before it becomes a problem. Those same completions also land in Comply's audit trail as evidence. You get board-ready proof that training actually moved capability, all from one connected platform.

KC Skills includes a Skill Tree Builder for exactly this. You build a hierarchical taxonomy that mirrors your org — domains, skills, and sub-skills — and AI Skill Suggestions proposes what each role should cover so you're not starting from scratch. That tree powers every assessment, the skill matrix, and the gap-to-training loop, keeping one shared definition of a skill across KnowledgeCity. Because the whole platform runs on one data model, the taxonomy you build in KC Skills also informs KC Map and learning paths in KC LMS — define it once, use it everywhere.

KC Skills has a Campaign Engine for this. Instead of running assessments one at a time, you roll out assess → train → reassess to an entire group in a single campaign: everyone is assessed, gaps auto-assign the right learning paths in KC LMS, and the group is reassessed to confirm the lift. Progress rolls up into the skill matrix in real time. There are no training and assessment tools to wire together, so the campaign moves an entire group through the full loop and the results stay in sync.

KC Skills includes Skills-Drift Tracking to keep proficiency honest over time. Skills fade, tools change, and teams turn over — drift tracking shows how scores move month over month and flags where a team is sliding backward, so you can reassess and retrain before it hurts. Because KC Skills sits on KnowledgeCity's one data model, a detected drop can trigger fresh learning paths in KC LMS automatically, closing the loop again without manual work. You get an early-warning system for capability, not a one-time snapshot that goes stale.