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coderabbit-performance-tuningCoderabbit 性能调优

Agent Skill

coderabbit-performance-tuning 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

总安装

509

周安装

21

GitHub Stars

2,078

下载量

166
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:coderabbit-performance-tuning(Coderabbit 性能调优)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/coderabbit-performance-tuning
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill coderabbit-performance-tuning
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/jeremylongshore/claude-code-plugins-plus-skills --skill coderabbit-performance-tuning

简介

Coderabbit 性能调优专注于提升审查速度和结果相关性,优化开发者工作流整合。

  • 典型审查耗时 2-10 分钟,大文件 PR 最长可达 15 分钟。
  • 通过拆分 PR 大小、配置 review_time 参数等方式加速处理过程。
  • 需已安装 Coderabbit 并配置 .coderabbit.yaml 文件以启用相关规则。
  • coderabbit-performance-tuning 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

CodeRabbit Performance Tuning

Overview

Optimize CodeRabbit review speed, relevance, and developer workflow integration. CodeRabbit reviews typically take 2-10 minutes depending on PR size, with large PRs (1000+ lines) taking up to 15 minutes.

Prerequisites

  • CodeRabbit installed on GitHub/GitLab organization
  • .coderabbit.yaml configuration file in repositories
  • Understanding of review patterns and team feedback

Instructions

Step 1: Keep PRs Small for Faster Reviews

# PR size directly impacts review speed and quality
size_guidelines:
  small:    # <200 lines changed
    review_time: "2-3 minutes"
    quality: "High - focused, actionable comments"
  medium:   # 200-500 lines
    review_time: "3-7 minutes"
    quality: "Good - may miss nuanced issues"
  large:    # 500-1000 lines
    review_time: "7-12 minutes"
    quality: "Moderate - broad strokes only"
  huge:     # 1000+ lines
    review_time: "12-15+ minutes"
    quality: "Low - too much context to process well"

# Best practice: enforce PR size limits with CI checks
# max_lines_changed: 500

Step 2: Use Path-Specific Instructions for Relevance

# .coderabbit.yaml - Give context so reviews are actionable
reviews:
  path_instructions:
    - path: "src/api/**"
      instructions: |
        Check for: proper error handling, input validation, auth middleware.
        Ignore: logging format, import order.
    - path: "src/components/**"
      instructions: |
        Check for: accessibility (aria labels), performance (no inline styles).
        Ignore: CSS naming conventions (handled by linter).
    - path: "tests/**"
      instructions: |
        Check for: assertion completeness, edge cases.
        Ignore: test structure (handled by testing framework conventions).

Step 3: Configure Incremental Reviews

# .coderabbit.yaml - Only re-review changed files on push
reviews:
  auto_review:
    enabled: true
    incremental: true    # Re-review only changed files on new pushes
    drafts: false        # Skip draft PRs (work in progress)
    base_branches: [main, develop]  # Only PRs targeting these branches

Step 4: Reduce Noise with Smart Exclusions

# .coderabbit.yaml - Skip files that don't benefit from AI review
reviews:
  auto_review:
    ignore_paths:
      - "**/*.lock"             # Package lock files
      - "**/*.snap"             # Test snapshots
      - "**/*.generated.*"      # Generated code
      - "**/*.min.js"           # Minified files
      - "**/vendor/**"          # Third-party code
      - "**/__mocks__/**"       # Test mocks
      - "**/fixtures/**"        # Test fixtures
    ignore_title_keywords:
      - "WIP"
      - "DO NOT MERGE"
      - "chore: bump"

Step 5: Tune Review Profile for Your Team

# Match review aggressiveness to team preferences
profiles:
  chill:       # Few comments, only major issues
    best_for: "Senior teams, high-trust environments"
    comment_count: "1-3 per PR"

  assertive:   # Balanced signal-to-noise
    best_for: "Most teams (recommended default)"
    comment_count: "3-8 per PR"

  nitpicky:    # Detailed comments on style and best practices
    best_for: "Junior teams, onboarding, compliance-critical"
    comment_count: "8-15 per PR"
    warning: "May cause review fatigue if team isn't expecting it"

Error Handling

IssueCauseSolution
Review takes 15+ minutesPR too large (1000+ lines)Split into smaller PRs
Too many irrelevant commentsNo path_instructions configuredAdd context-specific instructions
Reviews on generated filesNo ignore_paths configuredAdd generated file patterns to exclusions
Team ignoring reviewsProfile too nitpickySwitch to assertive or chill profile

Examples

Basic usage: Apply coderabbit performance tuning to a standard project setup with default configuration options.

Advanced scenario: Customize coderabbit performance tuning for production environments with multiple constraints and team-specific requirements.

Output

  • Configuration files or code changes applied to the project
  • Validation report confirming correct implementation
  • Summary of changes made and their rationale

Resources

  • Official ORM documentation
  • Community best practices and patterns
  • Related skills in this plugin pack

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.92%
按下载量换算60

Claude

30.15%
按下载量换算50

Cursor

19.91%
按下载量换算33

Gemini CLI

10%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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