Browse all 9 articles in this open-source series. Nine practical chapters on the AI review CLI, including source selection, fifteen isolated Go checks, deployment controls and an honest cost experiment.
Featured starting point
Understand the review CLI, the difference between selection and execution, and a small evaluation task before granting access to a repository.
Suggested learning path
01 → 09
Understand the review CLI, the difference between selection and execution, and a small evaluation task before granting access to a repository.
Choose a Git scope, inspect preview output and save a review result without confusing a file-selection preview with a completed model review.
Prepare a pinned CLI rollout, protect configuration and separate model access from permission to post review comments in CI.
Follow deterministic scope selection, model-assisted grouping and review execution while resolving a documentation mismatch against the pinned implementation.
Use fifteen isolated Go cases to understand template exceptions and the boundary between an environment-filename helper and the complete secret-path filter.
Design a controlled review comparison that records tokens, latency and human acceptance without treating an upstream benchmark as a guaranteed saving.
Understand what path filters protect, how configuration can execute commands and why privileged CI review needs a separate permissions review.
Compare review approaches by scope preparation, model ownership and acceptance evidence instead of selecting solely from star counts or benchmark claims.
Turn the selector into a learning project that explains exclusions and keeps planned coverage separate from executed review results.
