GitHub Work

A practical release-gate system for deciding whether an AI product is ready to ship. It converts quality, safety, grounding, latency, cost, user outcome and operational reliability into explicit pass-or-fail evidence, with reference scenarios and an offline demonstration.

A measurement model for understanding the complete AI experience, from an unresolved user problem to successful adoption and advocacy. Nine stages, four analytical lanes and 27 metrics connect user progress with model behaviour, product friction and business outcomes.

A structured reasoning skill that challenges the first explanation of a product problem. It separates symptoms from underlying causes, tests assumptions, identifies second-order effects and produces sharper questions before a team commits to a solution or roadmap decision.

An experimental memory architecture for retaining useful knowledge across repeated AI interactions. It explores how strong elements from previous solutions can be selected, combined and refined so later responses improve without simply copying earlier outputs.

An operating system for product-management work built around five specialised agents. Persistent context, Jira access through MCP and proactive workflow hooks support research synthesis, requirements, prioritisation and delivery while keeping product judgement with the human owner.