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Agent Skill · 研究检索

agent-swarm-pr(Agent 群公关)

agent-swarm-pr 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。

agent-swarm-pr 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,权限为执行命令;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 46,384。

总安装 783周安装 32GitHub Stars 33,993下载 251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:agent-swarm-pr(Agent 群公关)
来源仓库:https://github.com/ruvnet/claude-flow
仓库路径:skills/agent-swarm-pr
安装命令:
npx skills add https://github.com/ruvnet/claude-flow --skill agent-swarm-pr
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/ruvnet/claude-flow --skill agent-swarm-pr

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。

用于查找、检索和筛选相关信息,适用于多宿主环境中的信息定位需求。支持根据关键词、任务场景或来源线索快速获取候选结果。通过 npx skills add 命令从 GitHub 仓库安装。使用前请确认权限边界及是否涉及敏感操作。agent-swarm-pr 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/agent-swarm-pr

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/ruvnet/claude-flow --skill agent-swarm-pr;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

    它需要执行命令,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

agent-swarm-pr 怎么安装?

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

agent-swarm-pr 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。

agent-swarm-pr 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/ruvnet/claude-flow --skill agent-swarm-pr 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

agent-swarm-pr 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。

agent-swarm-pr 用的人多吗?

公开数据是累计安装 783 次,近一周安装 32 次,下载 251 次,GitHub 33,993 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。

agent-swarm-pr 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。
  • 来源仓库有 33,993 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
agent-swarm-pr

Agent 群公关 · 本页

78333,993执行命令未明确有提醒

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

name
swarm-pr description: Pull request swarm management agent that coordinates multi-agent code review, validation, and integration workflows with automated PR lifecycle management type: development color: "#4ECDC4" tools:

Swarm PR - Managing Swarms through Pull Requests

Overview

Create and manage AI swarms directly from GitHub Pull Requests, enabling seamless integration with your development workflow through intelligent multi-agent coordination.

Core Features

1. PR-Based Swarm Creation

# Create swarm from PR description using gh CLI
gh pr view 123 --json body,title,labels,files | npx ruv-swarm swarm create-from-pr

# Auto-spawn agents based on PR labels
gh pr view 123 --json labels | npx ruv-swarm swarm auto-spawn

# Create swarm with PR context
gh pr view 123 --json body,labels,author,assignees | \
  npx ruv-swarm swarm init --from-pr-data

2. PR Comment Commands

Execute swarm commands via PR comments:

<!-- In PR comment -->
$swarm init mesh 6
$swarm spawn coder "Implement authentication"
$swarm spawn tester "Write unit tests"
$swarm status

3. Automated PR Workflows

# .github$workflows$swarm-pr.yml
name: Swarm PR Handler
on:
  pull_request:
    types: [opened, labeled]
  issue_comment:
    types: [created]

jobs:
  swarm-handler:
    runs-on: ubuntu-latest
    steps:
      - uses: actions$checkout@v3
      - name: Handle Swarm Command
        run: |
          if [[ "${{ github.event.comment.body }}" == $swarm* ]]; then
            npx ruv-swarm github handle-comment \
              --pr ${{ github.event.pull_request.number }} \
              --comment "${{ github.event.comment.body }}"
          fi

PR Label Integration

Automatic Agent Assignment

Map PR labels to agent types:

{
  "label-mapping": {
    "bug": ["debugger", "tester"],
    "feature": ["architect", "coder", "tester"],
    "refactor": ["analyst", "coder"],
    "docs": ["researcher", "writer"],
    "performance": ["analyst", "optimizer"]
  }
}

Label-Based Topology

# Small PR (< 100 lines): ring topology
# Medium PR (100-500 lines): mesh topology
# Large PR (> 500 lines): hierarchical topology
npx ruv-swarm github pr-topology --pr 123

PR Swarm Commands

Initialize from PR

# Create swarm with PR context using gh CLI
PR_DIFF=$(gh pr diff 123)
PR_INFO=$(gh pr view 123 --json title,body,labels,files,reviews)

npx ruv-swarm github pr-init 123 \
  --auto-agents \
  --pr-data "$PR_INFO" \
  --diff "$PR_DIFF" \
  --analyze-impact

Progress Updates

# Post swarm progress to PR using gh CLI
PROGRESS=$(npx ruv-swarm github pr-progress 123 --format markdown)

gh pr comment 123 --body "$PROGRESS"

# Update PR labels based on progress
if [[ $(echo "$PROGRESS" | grep -o '[0-9]\+%' | sed 's/%//') -gt 90 ]]; then
  gh pr edit 123 --add-label "ready-for-review"
fi

Code Review Integration

# Create review agents with gh CLI integration
PR_FILES=$(gh pr view 123 --json files --jq '.files[].path')

# Run swarm review
REVIEW_RESULTS=$(npx ruv-swarm github pr-review 123 \
  --agents "security,performance,style" \
  --files "$PR_FILES")

# Post review comments using gh CLI
echo "$REVIEW_RESULTS" | jq -r '.comments[]' | while read -r comment; do
  FILE=$(echo "$comment" | jq -r '.file')
  LINE=$(echo "$comment" | jq -r '.line')
  BODY=$(echo "$comment" | jq -r '.body')

  gh pr review 123 --comment --body "$BODY"
done

Advanced Features

1. Multi-PR Swarm Coordination

# Coordinate swarms across related PRs
npx ruv-swarm github multi-pr \
  --prs "123,124,125" \
  --strategy "parallel" \
  --share-memory

2. PR Dependency Analysis

# Analyze PR dependencies
npx ruv-swarm github pr-deps 123 \
  --spawn-agents \
  --resolve-conflicts

3. Automated PR Fixes

# Auto-fix PR issues
npx ruv-swarm github pr-fix 123 \
  --issues "lint,test-failures" \
  --commit-fixes

Best Practices

1. PR Templates

<!-- .github$pull_request_template.md -->
## Swarm Configuration
- Topology: [mesh$hierarchical$ring$star]
- Max Agents: [number]
- Auto-spawn: [yes$no]
- Priority: [high$medium$low]

## Tasks for Swarm
- [ ] Task 1 description
- [ ] Task 2 description

2. Status Checks

# Require swarm completion before merge
required_status_checks:
  contexts:
    - "swarm$tasks-complete"
    - "swarm$tests-pass"
    - "swarm$review-approved"

3. PR Merge Automation

# Auto-merge when swarm completes using gh CLI
# Check swarm completion status
SWARM_STATUS=$(npx ruv-swarm github pr-status 123)

if [[ "$SWARM_STATUS" == "complete" ]]; then
  # Check review requirements
  REVIEWS=$(gh pr view 123 --json reviews --jq '.reviews | length')

  if [[ $REVIEWS -ge 2 ]]; then
    # Enable auto-merge
    gh pr merge 123 --auto --squash
  fi
fi

Webhook Integration

Setup Webhook Handler

// webhook-handler.js
const { createServer } = require('http');
const { execSync } = require('child_process');

createServer((req, res) => {
  if (req.url === '$github-webhook') {
    const event = JSON.parse(body);

    if (event.action === 'opened' && event.pull_request) {
      execSync(`npx ruv-swarm github pr-init ${event.pull_request.number}`);
    }

    res.writeHead(200);
    res.end('OK');
  }
}).listen(3000);

Examples

Feature Development PR

# PR #456: Add user authentication
npx ruv-swarm github pr-init 456 \
  --topology hierarchical \
  --agents "architect,coder,tester,security" \
  --auto-assign-tasks

Bug Fix PR

# PR #789: Fix memory leak
npx ruv-swarm github pr-init 789 \
  --topology mesh \
  --agents "debugger,analyst,tester" \
  --priority high

Documentation PR

# PR #321: Update API docs
npx ruv-swarm github pr-init 321 \
  --topology ring \
  --agents "researcher,writer,reviewer" \
  --validate-links

Metrics & Reporting

PR Swarm Analytics

# Generate PR swarm report
npx ruv-swarm github pr-report 123 \
  --metrics "completion-time,agent-efficiency,token-usage" \
  --format markdown

Dashboard Integration

# Export to GitHub Insights
npx ruv-swarm github export-metrics \
  --pr 123 \
  --to-insights

Security Considerations

  1. Token Permissions: Ensure GitHub tokens have appropriate scopes
  2. Command Validation: Validate all PR comments before execution
  3. Rate Limiting: Implement rate limits for PR operations
  4. Audit Trail: Log all swarm operations for compliance

Integration with Claude Code

When using with Claude Code:

  1. Claude Code reads PR diff and context
  2. Swarm coordinates approach based on PR type
  3. Agents work in parallel on different aspects
  4. Progress updates posted to PR automatically
  5. Final review performed before marking ready

Advanced Swarm PR Coordination

Multi-Agent PR Analysis

# Initialize PR-specific swarm with intelligent topology selection
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 8 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "PR Coordinator" }
mcp__claude-flow__agent_spawn { type: "reviewer", name: "Code Reviewer" }
mcp__claude-flow__agent_spawn { type: "tester", name: "Test Engineer" }
mcp__claude-flow__agent_spawn { type: "analyst", name: "Impact Analyzer" }
mcp__claude-flow__agent_spawn { type: "optimizer", name: "Performance Optimizer" }

# Store PR context for swarm coordination
mcp__claude-flow__memory_usage {
  action: "store",
  key: "pr/#{pr_number}$analysis",
  value: {
    diff: "pr_diff_content",
    files_changed: ["file1.js", "file2.py"],
    complexity_score: 8.5,
    risk_assessment: "medium"
  }
}

# Orchestrate comprehensive PR workflow
mcp__claude-flow__task_orchestrate {
  task: "Execute multi-agent PR review and validation workflow",
  strategy: "parallel",
  priority: "high",
  dependencies: ["diff_analysis", "test_validation", "security_review"]
}

Swarm-Coordinated PR Lifecycle

// Pre-hook: PR Initialization and Swarm Setup
const prPreHook = async (prData) => {
  // Analyze PR complexity for optimal swarm configuration
  const complexity = await analyzePRComplexity(prData);
  const topology = complexity > 7 ? "hierarchical" : "mesh";

  // Initialize swarm with PR-specific configuration
  await mcp__claude_flow__swarm_init({ topology, maxAgents: 8 });

  // Store comprehensive PR context
  await mcp__claude_flow__memory_usage({
    action: "store",
    key: `pr/${prData.number}$context`,
    value: {
      pr: prData,
      complexity,
      agents_assigned: await getOptimalAgents(prData),
      timeline: generateTimeline(prData)
    }
  });

  // Coordinate initial agent synchronization
  await mcp__claude_flow__coordination_sync({ swarmId: "current" });
};

// Post-hook: PR Completion and Metrics
const prPostHook = async (results) => {
  // Generate comprehensive PR completion report
  const report = await generatePRReport(results);

  // Update PR with final swarm analysis
  await updatePRWithResults(report);

  // Store completion metrics for future optimization
  await mcp__claude_flow__memory_usage({
    action: "store",
    key: `pr/${results.number}$completion`,
    value: {
      completion_time: results.duration,
      agent_efficiency: results.agentMetrics,
      quality_score: results.qualityAssessment,
      lessons_learned: results.insights
    }
  });
};

Intelligent PR Merge Coordination

# Coordinate merge decision with swarm consensus
mcp__claude-flow__coordination_sync { swarmId: "pr-review-swarm" }

# Analyze merge readiness with multiple agents
mcp__claude-flow__task_orchestrate {
  task: "Evaluate PR merge readiness with comprehensive validation",
  strategy: "sequential",
  priority: "critical"
}

# Store merge decision context
mcp__claude-flow__memory_usage {
  action: "store",
  key: "pr$merge_decisions/#{pr_number}",
  value: {
    ready_to_merge: true,
    validation_passed: true,
    agent_consensus: "approved",
    final_review_score: 9.2
  }
}

See also: swarm-issue.md, sync-coordinator.md, workflow-automation.md

平台分布

Codex

36.24%
按下载量换算91

Claude

29.65%
按下载量换算74

Cursor

20.37%
按下载量换算51

Gemini CLI

9.89%
按下载量换算25

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/ruvnet/claude-flow --skill agent-swarm-pr 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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