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Quality-oriented mining software repositories studies require more than access to commits, issues, pull requests and CI logs. They require a reproducible way to represent how technical, social, temporal and defect-related evidence is linked across repository history. Existing MSR infrastructures provide important support for repository mining, but they do not directly expose a project-level, time-aware empirical model in which cross-artifact quality evidence can be queried, inspected and reused across alternative study designs. This paper presents QModel, an emerging infrastructure for reproducible GitHub mining in empirical software quality studies. QModel stores Git, GitHub, CI, timeline, reaction, graph, churn and SZZ-style candidate-defect evidence in a project-centric relational schema. Its SQL-based compilation layer allows researchers to define feature-target datasets over arbitrary analysis units, including pull requests, issues, commits, files, time windows and custom cross-artifact objects. We evaluate QModel on ansible/ansible and facebook/react, mining 76,475 commits, 70,509 pull requests, 45,908 issues, 2,174,020 timeline events, 315,235 file-change records and 446,161 CI records. The early results show that QModel can materialize analysis-ready datasets with computable target, graph, churn and provenance features, while also making evidence gaps explicit when historical links, CI records, or fixing evidence are incomplete. These results support QModel as an empirical infrastructure for making quality-oriented repository operationalizations explicit, reusable and systematically evaluable.