Desktop AI Skills and Workforce Manager

Desktop AI tools spread through companies the way spreadsheets did: brilliantly, informally, and with no record of what anyone configured. One engineer has an excellent set of Claude skills; the team next door rebuilt something similar and worse; nobody knows which repositories have instructions, which MCP servers people connected, or what any of it is allowed to reach.

Status: product concept, in active development. This is something we are building, not something you can buy today. We publish our roadmap because the engineering thinking behind it is the useful part — and because we would rather show you the design than imply a finished product.

Architecture diagram: approved skill and prompt registry, repository instruction packages, MCP connection catalog and policy profiles distributed through versioning and staged rollout with automated skill tests and usage analytics.

Desktop AI Skills and Workforce Manager is designed to convert those isolated experiments into a repeatable corporate capability — without taking the tools away from the people who are getting value from them.

What It Is Designed To Do

  • Approved skill and prompt registry — the good configuration one person wrote becomes the one everyone gets.
  • Repository instruction packages — consistent agent instructions across codebases.
  • MCP connection catalog — a sanctioned list of what desktop agents may connect to.
  • Policy and permission profiles — different roles get different reach, deliberately.
  • Versioning and staged rollout — changes go to a pilot group before the whole company.
  • Automated skill test suites — a skill that silently degrades after a model update gets caught.
  • Usage and performance analytics — which skills are actually used, and which are theatre.
  • Employee training and attestation — adoption tracked as capability, not licence count.

The Problem It Actually Solves

Most enterprise AI adoption stalls in exactly this gap: the tools are bought, a few people become genuinely fast with them, and that capability never becomes organisational. Distribution and governance are the missing half. This builds directly on our Claude and Codex and AI skills work.

Tell us if this matches a problem you have — early input shapes what we build first, and we will give you an honest view of where it stands.