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Agent Skill · 研究检索

pr-comments(公关评论)

pr-comments 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-comments 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,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 55,410。

总安装 480周安装 20GitHub Stars 33下载 160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-comments(公关评论)
来源仓库:https://github.com/mblode/agent-skills
仓库路径:skills/pr-comments
安装命令:
npx skills add https://github.com/mblode/agent-skills --skill pr-comments
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/mblode/agent-skills --skill pr-comments

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-comments 用于查找、检索和筛选相关信息,支持关键词定位。适合在 Codex、Claude、Cursor、Gemini CLI 中需要快速定位候选结果时使用。通过 GitHub 安装,使用 npx skills add 命令添加指定仓库的 skill。

安装前需确认权限范围、维护状态,以及是否会触发联网或文件读写。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/pr-comments

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/mblode/agent-skills --skill pr-comments;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

pr-comments 怎么安装?

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

pr-comments 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-comments 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

pr-comments 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-comments 用的人多吗?

公开数据是累计安装 480 次,近一周安装 20 次,下载 160 次,GitHub 33 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-comments 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。

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-comments

公关评论 · 本页

48033需要联网未明确有提醒

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

PR Comment Triage

Triage and resolve PR review comments from humans and bots in a structured four-phase workflow.

Scope

  • Use for: unresolved review threads on open PRs, both human and bot comments
  • Skip: closed PRs, PRs with no unresolved threads, draft PRs unless explicitly requested

Reference Files

FileRead When
references/github-api.mdDefault: GraphQL queries for fetching, replying, and resolving threads
references/bot-patterns.mdPhase 2: bot detection, severity parsing, deduplication, false positive rules
references/fix-plan-template.mdPhase 3: generating the fix plan document

Workflow

Copy this checklist to track progress:

PR comment triage progress:
- [ ] Phase 1: Fetch unresolved review threads, reviews, and comments
- [ ] Phase 2: Triage and classify all items
- [ ] Phase 3: Write fix plan and get approval
- [ ] Phase 4: Execute fixes and resolve threads

Phase 1: Fetch

Load references/github-api.md for query templates.

  1. Identify the PR — auto-detect from current branch (gh pr view --json number) or accept an explicit PR number argument
  2. Extract owner/repo — gh repo view --json owner,name
  3. Fetch all review threads — use the GraphQL reviewThreads query with pagination. Collect every thread, then filter to isResolved == false
  4. Fetch PR reviews — use the REST reviews endpoint. Collect all reviews with their state, body, and author. Pay attention to CHANGES_REQUESTED reviews with body text — these contain the reviewer's summary of what needs to change
  5. Fetch issue-level comments — use the REST endpoint for all PR conversation comments. Do not filter by author — bot comments from github-actions[bot] may contain actionable findings (DangerJS warnings, schema compatibility checks)
  6. Early exit — if zero unresolved threads, zero actionable reviews, and zero actionable issue comments, report "No unresolved review items found" and stop

Output: three lists — unresolved threads, reviews with content, and issue-level comments.

Phase 2: Triage

Load references/bot-patterns.md for detection and parsing rules.

For each item across all three sources:

  1. Identify author type — human or bot. For bots, classify by content first, then username. See references/bot-patterns.md for detection rules, severity parsing, and false positive patterns
  2. Skip noise — auto-classify noise items per bot-patterns reference (linear linkbacks, vercel deployments, Devin "no issues" reviews, changeset releases, event-lib triggers)
  3. Parse severity — extract from bot-specific format (emoji, SVG, HTML tables). Human comments: default to Major for CHANGES_REQUESTED, Minor for APPROVED + question
  4. Deduplicate — group inline comments on the same file within a 3-line range. Keep the highest-severity, most-actionable comment. Mark others as ignore-duplicate
  5. Classify each remaining item:

- Category: bug, security, performance, style, correctness, docs, test-coverage - Severity: critical, major, minor, nitpick - Confidence: high (human or multi-bot), medium (single bot, clear issue), low (single bot, likely false positive) - Disposition: fix or ignore (with reason) - Source type: thread (resolvable), conversation (reply-only)

  1. Flag ignore candidates with specific reasoning:

- Pre-existing code not changed in this PR - Bot flagged.md, config, lock, or migration files (no security implications) - Contradicts project CLAUDE.md/AGENTS.md conventions - Outdated thread where code has changed substantially - Noise bot boilerplate or badge injection

Human comments are never auto-ignored. Always classify as fix unless clearly already resolved or the reviewer explicitly marked it as optional ("up to you", "but not blocking").

Phase 3: Plan

Load references/fix-plan-template.md for the output template.

  1. Create plan file — write to .claude/scratchpad/pr-{N}-review-plan.md (create directory if needed)
  2. Issues to Fix — ordered by severity (critical first), each with thread ID, file:line, author, category, finding, fix approach, and commit group label
  3. Conversation Items — issue-level comments and review bodies that need fixes but have no thread ID. These get a reply but no resolve mutation
  4. Ignored — each with thread ID or comment ID, author, reason, and the brief reply to post when resolving
  5. Summary table — disposition-by-severity matrix for quick scanning
  6. Present to user — print a summary showing counts (N to fix, K conversation items, M to ignore) and ask for approval

Stop here. Do not proceed to Phase 4 until the user reviews and approves the plan.

The user may edit the plan — move items between fix/conversation/ignore, change priorities, add notes. Re-read the plan file before executing.

Phase 4: Execute

After user approval, re-read .claude/scratchpad/pr-{N}-review-plan.md in case it was edited.

4a. Resolve ignored threads:

For each ignored item that has a thread ID:

  1. Post a brief reply explaining the decision
  2. Resolve the thread via GraphQL mutation

Use concise, specific reasons:

  • "Duplicate of thread addressing the same finding — resolving."
  • "Contradicts project convention (see CLAUDE.md) — resolving."
  • "Outdated thread — code has been refactored. Resolving."
  • "CI notification — not actionable. Resolving."

For ignored noise without a thread ID (issue-level bot comments), do nothing — no reply needed.

4b. Fix real issues:

Group fixes by commit group label from the plan. For each group:

  • If fixes touch independent files, launch parallel subagents (one per file group)
  • If fixes touch the same file or overlapping lines, execute sequentially
  • Each subagent reads the file, applies the fix, and verifies correctness

Subagent prompt template:

Fix the following PR review issue(s) on branch {BRANCH}:

Issue: {finding}
File: {path}:{line}
Fix approach: {fix_approach}

Read the file, understand the surrounding context, and make the fix.
After fixing, verify the change is correct.
Do NOT touch unrelated code.

4c. Commit and push:

  • Run project lint/test commands if available
  • One commit per logical fix group, message format: fix({scope}): {description}
  • Push the branch

4d. Resolve and reply:

  • Resolved threads: post reply ("Fixed in latest push.") then resolve via GraphQL
  • Conversation items: post reply acknowledging the fix
  • Issue-level comments have no resolve mechanism — the reply is the acknowledgment

4e. Verify:

  • Re-fetch threads from GitHub (fresh API call) to confirm zero unresolved remain
  • Check CI status with gh pr checks
  • Report results to the user

Anti-patterns

  • Resolving threads without posting a reply — reviewers need to see the reasoning
  • Auto-ignoring human comments — always classify for user review
  • Fixing items the plan marked as ignore without approval
  • One commit per individual comment — group related fixes by commit group label
  • Pushing before verifying lint/test pass locally
  • Skipping Phase 3 approval — the fix plan is the quality gate
  • Classifying github-actions[bot] as always noise — it is a shared identity used by DangerJS, schema checkers, and other active tools. Classify by content.
  • Filtering issue-level comments to humans only — bot comments from DangerJS and schema checkers are actionable

Related skills

  • review-pr for reviewing PRs and posting comments (outbound — complementary to this skill)

平台分布

Codex

34.01%
按下载量换算54

Claude

30.5%
按下载量换算49

Cursor

18.58%
按下载量换算30

Gemini CLI

8.11%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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