Our hardware-design goal was to generate machine compatible degradation data to aid halideperovskite stability research. We wanted the degradation chamber to fulfill four intertwined requirements to reach machine compatibility: The ideal device should be capable of producing degradation data with sufficiently high throughput for the machine learning algorithms. Linked to this requirement, the device should be low cost to enable expansion when necessary. Experimental variance can lead to erroneous conclusions, especially in high-throughput experimental workflows guided by machine learning, (garbage in, garbage out). Thus, the data should have low variations and preferably no physical contacts to the samples. These criteria require judicious selection of the response variable for the in-situ degradation system. Direct observations of phase transformations via XRD normally require removing samples from the aging chamber, perturbing the aging process. In situ electrical measurements provide useful information, but require electric contacts that can be prone to (intermittent) faults. Thus, we opted for a non-invasive optical signature as a proxy for sample degradation. Because spectroscopy is a widely accepted method in kinetics experimentation, we propose that optical changes in the visible light spectra can be a viable substitute when samples must be observed noninvasively in-situ especially for materials like lead halide perovskites which have degradation pathways in the visible spectrum. Color change is often used in chemical experimentation as an indication of reactivity. In food science, automated color change analysis has been instrumental in quality assessment and sorting - . General chemistry pedagogical experimentation with kinetics in acid and base reactions lean heavily upon color change indicators , and more machine-driven versions using smartphone cameras have since automated the analysis of optical changes to determine rate law parameters .