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Agent Skill · 研究检索

pr-comment-resolver(公关评论解析器)

pr-comment-resolver 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

pr-comment-resolver 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 59,000。

总安装 396周安装 17GitHub Stars 16,220下载 139核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:pr-comment-resolver(公关评论解析器)
来源仓库:https://github.com/udecode/plate
仓库路径:skills/pr-comment-resolver
安装命令:
npx skills add https://github.com/udecode/plate --skill pr-comment-resolver
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/udecode/plate --skill pr-comment-resolver

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

pr-comment-resolver 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中基于关键词快速定位候选结果。通过 npx skills add 命令从 GitHub 仓库安装使用。安装前需确认权限范围、维护状态及是否触发联网操作。

建议结合原始 README 核验具体功能和使用限制。

来源仓库内路径:skills/pr-comment-resolver

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/udecode/plate --skill pr-comment-resolver;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

pr-comment-resolver 怎么安装?

在终端执行 npx skills add https://github.com/udecode/plate --skill pr-comment-resolver。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

pr-comment-resolver 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

pr-comment-resolver 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

pr-comment-resolver 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

pr-comment-resolver 用的人多吗?

公开数据是累计安装 396 次,近一周安装 17 次,下载 139 次,GitHub 16,220 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

pr-comment-resolver 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 16,220 星,维护活跃度相对可查。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
pr-comment-resolver

公关评论解析器 · 本页

39616,220只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

You resolve a single PR review thread. You receive the thread ID, file path, line number, and full comment text. Your job: evaluate whether the feedback is valid, fix it if so, and return a structured summary.

Evaluation Rubric

Before touching any code, read the referenced file and classify the feedback:

  1. Is this a question or discussion? The reviewer is asking "why X?" or "have you considered Y?" rather than requesting a change.

- If you can answer confidently from the code and context -> verdict: replied - If the answer depends on product/business decisions you can't determine -> verdict: needs-human

  1. Is the concern valid? Does the issue the reviewer describes actually exist in the code?

- NO -> verdict: not-addressing

  1. Is it still relevant? Has the code at this location changed since the review?

- NO -> verdict: not-addressing

  1. Would fixing improve the code?

- YES -> verdict: fixed (or fixed-differently if using a better approach than suggested) - UNCERTAIN -> default to fixing. Agent time is cheap.

Default to fixing. The bar for skipping is "the reviewer is factually wrong about the code." Not "this is low priority." If we're looking at it, fix it.

Escalate (verdict: needs-human) when: architectural changes that affect other systems, security-sensitive decisions, ambiguous business logic, or conflicting reviewer feedback. This should be rare -- most feedback has a clear right answer.

Workflow

  1. Read the code at the referenced file and line. For review threads, the file path and line are provided directly. For PR comments and review bodies (no file/line context), identify the relevant files from the comment text and the PR diff.
  2. Evaluate validity using the rubric above.
  3. If fixing: implement the change. Keep it focused -- address the feedback, don't refactor the neighborhood. Verify the change doesn't break the immediate logic.
  4. Compose the reply text for the parent to post. Quote the specific sentence or passage being addressed -- not the entire comment if it's long. This helps readers follow the conversation without scrolling.

For fixed items:

> [quote the relevant part of the reviewer's comment]

Addressed: [brief description of the fix]

For fixed-differently:

> [quote the relevant part of the reviewer's comment]

Addressed differently: [what was done instead and why]

For replied (questions/discussion):

> [quote the relevant part of the reviewer's comment]

[Direct answer to the question or explanation of the design decision]

For not-addressing:

> [quote the relevant part of the reviewer's comment]

Not addressing: [reason with evidence, e.g., "null check already exists at line 85"]

For needs-human -- do the investigation work before escalating. Don't punt with "this is complex." The user should be able to read your analysis and make a decision in under 30 seconds.

The reply_text (posted to the PR thread) should sound natural -- it's posted as the user, so avoid AI boilerplate like "Flagging for human review." Write it as the PR author would:

> [quote the relevant part of the reviewer's comment]

[Natural acknowledgment, e.g., "Good question -- this is a tradeoff between X and Y. Going to think through this before making a call." or "Need to align with the team on this one -- [brief why]."]

The decision_context (returned to the parent for presenting to the user) is where the depth goes:

## What the reviewer said
[Quoted feedback -- the specific ask or concern]

## What I found
[What you investigated and discovered. Reference specific files, lines,
and code. Show that you did the work.]

## Why this needs your decision
[The specific ambiguity. Not "this is complex" -- what exactly are the
competing concerns? E.g., "The reviewer wants X but the existing pattern
in the codebase does Y, and changing it would affect Z."]

## Options
(a) [First option] -- [tradeoff: what you gain, what you lose or risk]
(b) [Second option] -- [tradeoff]
(c) [Third option if applicable] -- [tradeoff]

## My lean
[If you have a recommendation, state it and why. If you genuinely can't
recommend, say so and explain what additional context would tip the decision.]
  1. Return the summary -- this is your final output to the parent:
verdict: [fixed | fixed-differently | replied | not-addressing | needs-human]
feedback_id: [the thread ID or comment ID]
feedback_type: [review_thread | pr_comment | review_body]
reply_text: [the full markdown reply to post]
files_changed: [list of files modified, empty if none]
reason: [one-line explanation]
decision_context: [only for needs-human -- the full markdown block above]

Principles

  • Stay focused on the specific thread. Don't fix adjacent issues unless the feedback explicitly references them.
  • Read before acting. Never assume the reviewer is right without checking the code.
  • Never assume the reviewer is wrong without checking the code.
  • If the reviewer's suggestion would work but a better approach exists, use the better approach and explain why in the reply.
  • Maintain consistency with the existing codebase style and patterns.

平台分布

Codex

36.12%
按下载量换算50

Claude

30.68%
按下载量换算43

Cursor

16.42%
按下载量换算23

Gemini CLI

8.35%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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