Orchard - AI Skills Manager

The roleThis is a new kind of role: part knowledge engineer, part workflow builder, part internal product owner. You will sit behind our Transformation Lead operationally, taking requirements from across the business and turning them into production-ready AI skills that the wider team can reach for on every project.You are not just a prompt engineer. You will understand how the whole system works: how skills connect, how Claude's context window and tool use interact, how to structure knowledge so it can be retrieved reliably. And you will constantly be asking whether what we have is the most efficient way to do it.You report to the Transformation Lead and work closely with discipline leads across strategy, creative, accounts, and development. You will also interface with the broader Enero Group AI community as Orchard's skill-building capability becomes a reference point for the group.What you'll doSkill writing and productionThis is the core of the role.Write, test, and iterate AI skills: system prompts, workflow logic, tool configurations, and usage documentation, to a high production standardTake requirements from the Transformation Lead and discipline leads, translate them into functional skill specs, build the skill, and validate it works as intended before releaseJoin existing skills together into coherent, efficient systems: not just individual prompts but end-to-end workflowsConstantly review what exists and ask: is this the right way to do it? Is this efficient? Does this still reflect how we actually work?Iterate based on real-world use, fixing failure modes, tightening outputs, and updating skills when models or agency processes changeKnowledge management and infrastructureOwn the AI skills library: structure, versioning, naming conventions, metadata, and access, so every skill is findable and usable across the agencyManage the skills repository (GitHub or equivalent), including version control, branching, and review processesHelp build Orchard's organisational memory by documenting how we work, how our knowledge is structured, and where things live, in a form the AI system can actually useRun regular audits to retire outdated skills, flag gaps, and maintain quality standards across the libraryQuality, testing and performanceBuild and maintain testing frameworks to validate skills before releaseEstablish and uphold the release standard: the bar every skill must clear before it goes into useTrack usage, adoption, and impact across the agency; surface patterns, gaps, and failure modes to the Transformation LeadMaintain a structured feedback loop with teams: discovery before building, ongoing input after deploymentBest practice and standardsKeep pace with advances in prompt engineering, agentic design, and model behaviour, and bring new thinking back to the agencyMaintain Orchard's internal AI skills best practice guideEmbed responsible AI principles into every skill: bias checks, output validation, and appropriate human oversight