Operations¶

bijux-proteomics-lab operations is where practical execution pressure shows up. Maintainers here are proving that planning logic remains executable under real constraints, that outcome handling remains traceable, and that rerun logic does not drift away from actual assay work.

flowchart LR
    change["planning or outcome change"]
    plan["check planning and dependency behavior"]
    execute["check schedule and record handling"]
    interpret["check outcome and rerun interpretation"]
    feedback["check feedback into repository workflows"]
    release["publish updated lab operations surface"]

    change --> plan --> execute --> interpret --> feedback --> release

What Operations Means Here¶

operational truth is whether work can still be planned and interpreted under constraints, not whether a model looks tidy in isolation
lab breakage often appears first as awkward schedules or ambiguous outcomes, not as immediate crashes
release confidence depends on preserving the loop from recommended work to observed result and back again

Start With¶

open Common Workflows when you need the normal route from change to lab-proof release
open Observability and Diagnostics when planning output, schedule behavior, or outcomes stop matching lab reality
open Failure Recovery when a plan, execution record, or rerun interpretation already needs repair
open Release and Versioning before publishing any change that affects planning semantics or outcome promotion

Route From Operational Pressure¶

Local Development and Installation and Setup for reproducible lab-planning work
Deployment Boundaries and Security and Safety for the boundaries around operational records and external integrations
Performance and Scaling when schedule volume, batching complexity, or outcome processing becomes the bottleneck

First Proof Check¶

src/bijux_proteomics_lab/planning.py and outcomes.py
src/bijux_proteomics_lab/repositories.py and serialization.py
packages/bijux-proteomics-lab/tests