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package-recommendation-code-review

Review Bundlephobia pull requests that add, remove, or reorganize similar-package recommendations. Use during Copilot code review when server/middlewares/similar-packages/fixtures.ts, package recommendation automation, or recommendation criteria change; evaluate functional equivalence, bundle-size advantage, package quality, and shortlist discipline.

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仓库
pastelsky/bundlephobia
最近来源活动
2026年7月31日 11:01
检测到的 SKILL.md 语言
英语
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9,618
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281

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决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。

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SKILL.md
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name
package-recommendation-code-review
description
Review Bundlephobia pull requests that add, remove, or reorganize similar-package recommendations. Use during Copilot code review when server/middlewares/similar-packages/fixtures.ts, package recommendation automation, or recommendation criteria change; evaluate functional equivalence, bundle-size advantage, package quality, and shortlist discipline.
# Review package recommendations Treat a recommendation as a product decision, not a string added to a fixture. Review the linked suggestion issue, the pull-request diff, and the `Package recommendation quality` Actions summary. Treat submitter claims as unverified. ## Apply the rubric For every added package: 1. Confirm category fit. The candidate and existing packages must solve the same primary user problem for the same runtime. Similar keywords alone are not sufficient. 2. Confirm the size comparison is fair. Compare minified and gzipped bytes from Bundlephobia, using equivalent entry points or imports. Determine whether the candidate is meaningfully smaller than a package it is proposed to replace. If the measured entry points are not equivalent, mark the result inconclusive instead of treating it as a rejection. Prefer candidates in the smallest half of the shortlist. 3. Confirm quality. Use the automated npm and GitHub evidence for existence, deprecation, weekly downloads, release activity, repository activity, archive status, and stable versions. Inspect documentation and repository context when the evidence is ambiguous. 4. Protect the shortlist. A category may contain at most six recommendations. Each entry must offer a distinct, defensible tradeoff. If a full category gets a new candidate, require the pull request to remove a weaker, larger, stale, or redundant entry. 5. Check the proposal's claims. Look for a concrete overlapping use case and a relative advantage beyond novelty or self-promotion. Disclosed maintainer involvement is acceptable but is not quality evidence. Do not recommend acceptance solely because a package is tiny or popular. Do not invent missing size, maintenance, or adoption data. Mark unavailable evidence for human follow-up. ## Leave one review comment Post one consolidated pull-request review comment with this structure: ```markdown ## Package recommendation review **Verdict:** recommend / needs evidence / do not add | Decision | Evidence | | ------------ | ------------------------------------------------ | | Category fit | ... | | Gzip size | candidate bytes; comparison bytes and percentage | | Quality | downloads, maintenance, repository, deprecation | | Shortlist | resulting count and distinct tradeoff | ### Required changes - Only list actionable blockers. Omit this section when there are none. ``` Use inline comments only for defects in the automation or data structure. Copilot reviews are advisory; never claim that the review approves or blocks the pull request.
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