Nine practical chapters on the AI review CLI, including source selection, fifteen isolated Go checks, deployment controls and an honest cost experiment.
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Follow deterministic scope selection, model-assisted grouping and review execution while resolving a documentation mismatch against the pinned implementation.
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01 → 09
Follow deterministic scope selection, model-assisted grouping and review execution while resolving a documentation mismatch against the pinned implementation.
Compare review approaches by scope preparation, model ownership and acceptance evidence instead of selecting solely from star counts or benchmark claims.
Prepare a pinned CLI rollout, protect configuration and separate model access from permission to post review comments in CI.
Choose a Git scope, inspect preview output and save a review result without confusing a file-selection preview with a completed model review.
Turn the selector into a learning project that explains exclusions and keeps planned coverage separate from executed review results.
Understand the review CLI, the difference between selection and execution, and a small evaluation task before granting access to a repository.
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.
Use fifteen isolated Go cases to understand template exceptions and the boundary between an environment-filename helper and the complete secret-path filter.
