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Automated Cherry Studio review for local branches, PRs, commits, files, architecture docs, and repository skills.
Passed 7 of 12 test cases with the skill, and 3 of 12 without it.
and 6 more in the full report
6 fixed6 passed either way10 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are reviewing code changes in `src/renderer/hooks/useDataApi.ts` that modify renderer hooks and DataApi contracts. What reference guidance must be loaded for this review?
| Checked | Without skill | With skill |
|---|---|---|
| Loads references/cherry-review-guidance.md prior to evaluating renderer hooks or DataApi changes | Missed | Passed |
The instructions your model reads when the skill loads.
Automated code review for local branches, PRs, commits, and files. Detects
the review target from arguments, then picks the review engine from diff size
and runtime capability. Small diffs use single-agent review
(references/local-review.md). Large diffs use the multi-agent
reviewer–verifier flow (references/teams-review.md) only when independent
subagents are available; otherwise they fall back to single-agent review with
that limitation disclosed. PR targets add worktree setup and GitHub submission
(references/pr-review.md) around the same engine-selection contract.
Cherry Studio-specific review rules live in
references/cherry-review-guidance.md. Target review flows must load that file
for code, mixed, architecture-doc, and project-skill reviews so reviewers can
apply DataApi, service-boundary, renderer hook, React, UI, and type-contract
checks without relying on memory. That reference also defines which internal
docs, internal skills, external skills, and official websites to consult for
each changed area; load only the relevant subset.
All user-facing text matches the user's language.
Apart from the declared categories below, normal review is prompt-free: never ask for mode selection, fix confirmation, finding selection, or submission preview. A leaf flow may request input only when it explicitly declares one of these categories:
test cases passed, vs 3 / 12 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.