home / skills / ruvnet / ruflo / agent-pr-manager

agent-pr-manager skill

/.agents/skills/agent-pr-manager

This skill orchestrates multi agent PR workflows, automating reviews, tests, and merges to accelerate code delivery.

npx playbooks add skill ruvnet/ruflo --skill agent-pr-manager

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
6.1 KB
---
name: agent-pr-manager
description: Agent skill for pr-manager - invoke with $agent-pr-manager
---

---
name: pr-manager
description: Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows
type: development
color: "#4ECDC4"
tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - LS
  - TodoWrite
  - mcp__claude-flow__swarm_init
  - mcp__claude-flow__agent_spawn
  - mcp__claude-flow__task_orchestrate
  - mcp__claude-flow__swarm_status
  - mcp__claude-flow__memory_usage
  - mcp__claude-flow__github_pr_manage
  - mcp__claude-flow__github_code_review
  - mcp__claude-flow__github_metrics
hooks:
  pre:
    - "gh auth status || (echo 'GitHub CLI not authenticated' && exit 1)"
    - "git status --porcelain"
    - "gh pr list --state open --limit 1 >$dev$null || echo 'No open PRs'"
    - "npm test --silent || echo 'Tests may need attention'"
  post:
    - "gh pr status || echo 'No active PR in current branch'"
    - "git branch --show-current"
    - "gh pr checks || echo 'No PR checks available'"
    - "git log --oneline -3"
---

# GitHub PR Manager

## Purpose
Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows.

## Capabilities
- **Multi-reviewer coordination** with swarm agents
- **Automated conflict resolution** and merge strategies
- **Comprehensive testing** integration and validation
- **Real-time progress tracking** with GitHub issue coordination
- **Intelligent branch management** and synchronization

## Usage Patterns

### 1. Create and Manage PR with Swarm Coordination
```javascript
// Initialize review swarm
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 4 }
mcp__claude-flow__agent_spawn { type: "reviewer", name: "Code Quality Reviewer" }
mcp__claude-flow__agent_spawn { type: "tester", name: "Testing Agent" }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "PR Coordinator" }

// Create PR and orchestrate review
mcp__github__create_pull_request {
  owner: "ruvnet",
  repo: "ruv-FANN",
  title: "Integration: claude-code-flow and ruv-swarm",
  head: "integration$claude-code-flow-ruv-swarm",
  base: "main",
  body: "Comprehensive integration between packages..."
}

// Orchestrate review process
mcp__claude-flow__task_orchestrate {
  task: "Complete PR review with testing and validation",
  strategy: "parallel",
  priority: "high"
}
```

### 2. Automated Multi-File Review
```javascript
// Get PR files and create parallel review tasks
mcp__github__get_pull_request_files { owner: "ruvnet", repo: "ruv-FANN", pull_number: 54 }

// Create coordinated reviews
mcp__github__create_pull_request_review {
  owner: "ruvnet",
  repo: "ruv-FANN", 
  pull_number: 54,
  body: "Automated swarm review with comprehensive analysis",
  event: "APPROVE",
  comments: [
    { path: "package.json", line: 78, body: "Dependency integration verified" },
    { path: "src$index.js", line: 45, body: "Import structure optimized" }
  ]
}
```

### 3. Merge Coordination with Testing
```javascript
// Validate PR status and merge when ready
mcp__github__get_pull_request_status { owner: "ruvnet", repo: "ruv-FANN", pull_number: 54 }

// Merge with coordination
mcp__github__merge_pull_request {
  owner: "ruvnet",
  repo: "ruv-FANN",
  pull_number: 54,
  merge_method: "squash",
  commit_title: "feat: Complete claude-code-flow and ruv-swarm integration",
  commit_message: "Comprehensive integration with swarm coordination"
}

// Post-merge coordination
mcp__claude-flow__memory_usage {
  action: "store",
  key: "pr/54$merged",
  value: { timestamp: Date.now(), status: "success" }
}
```

## Batch Operations Example

### Complete PR Lifecycle in Parallel:
```javascript
[Single Message - Complete PR Management]:
  // Initialize coordination
  mcp__claude-flow__swarm_init { topology: "hierarchical", maxAgents: 5 }
  mcp__claude-flow__agent_spawn { type: "reviewer", name: "Senior Reviewer" }
  mcp__claude-flow__agent_spawn { type: "tester", name: "QA Engineer" }
  mcp__claude-flow__agent_spawn { type: "coordinator", name: "Merge Coordinator" }
  
  // Create and manage PR using gh CLI
  Bash("gh pr create --repo :owner/:repo --title '...' --head '...' --base 'main'")
  Bash("gh pr view 54 --repo :owner/:repo --json files")
  Bash("gh pr review 54 --repo :owner/:repo --approve --body '...'")
  
  
  // Execute tests and validation
  Bash("npm test")
  Bash("npm run lint")
  Bash("npm run build")
  
  // Track progress
  TodoWrite { todos: [
    { id: "review", content: "Complete code review", status: "completed" },
    { id: "test", content: "Run test suite", status: "completed" },
    { id: "merge", content: "Merge when ready", status: "pending" }
  ]}
```

## Best Practices

### 1. **Always Use Swarm Coordination**
- Initialize swarm before complex PR operations
- Assign specialized agents for different review aspects
- Use memory for cross-agent coordination

### 2. **Batch PR Operations**
- Combine multiple GitHub API calls in single messages
- Parallel file operations for large PRs
- Coordinate testing and validation simultaneously

### 3. **Intelligent Review Strategy**
- Automated conflict detection and resolution
- Multi-agent review for comprehensive coverage
- Performance and security validation integration

### 4. **Progress Tracking**
- Use TodoWrite for PR milestone tracking
- GitHub issue integration for project coordination
- Real-time status updates through swarm memory

## Integration with Other Modes

### Works seamlessly with:
- `$github issue-tracker` - For project coordination
- `$github branch-manager` - For branch strategy
- `$github ci-orchestrator` - For CI/CD integration
- `$sparc reviewer` - For detailed code analysis
- `$sparc tester` - For comprehensive testing

## Error Handling

### Automatic retry logic for:
- Network failures during GitHub API calls
- Merge conflicts with intelligent resolution
- Test failures with automatic re-runs
- Review bottlenecks with load balancing

### Swarm coordination ensures:
- No single point of failure
- Automatic agent failover
- Progress preservation across interruptions
- Comprehensive error reporting and recovery

Overview

This skill provides end-to-end pull request management with swarm-based coordination for automated reviews, testing, and merges. It combines multi-agent orchestration, GitHub integration, and CI validation to accelerate PR lifecycle and reduce manual overhead. Use it to standardize review workflows and enforce consistent merge policies across repositories.

How this skill works

The skill initializes a swarm of specialized agents (reviewers, testers, coordinators) and orchestrates tasks across them to perform parallel reviews, run tests, and resolve conflicts. It calls GitHub PR APIs to list, inspect, comment, approve, and merge pull requests while tracking progress and state in swarm memory. Hooks run pre- and post-checks (CLI auth, git status, tests) to surface issues before actions. Retry logic and agent failover handle transient errors and conflicting changes.

When to use it

  • When you need parallel, multi-reviewer code reviews across large PRs or many files
  • When you want automated conflict detection, resolution, and coordinated merging
  • When CI, linting, and test validation must be orchestrated together with reviews
  • When tracking PR progress and cross-agent state is required (milestones, todos)
  • When you need reliable retries and failover for flaky network or CI failures

Best practices

  • Initialize a swarm topology before complex operations and assign clear agent roles
  • Batch GitHub API calls and create parallel review tasks for large PRs
  • Persist key events in swarm memory for cross-agent coordination and auditability
  • Run pre-hooks (auth, git status, tests) to catch issues early
  • Use hierarchical or mesh topologies based on repo size and review complexity

Example use cases

  • Spin up a 3–5 agent swarm to review and test a feature branch, run CI, then merge on success
  • Create parallel file-specific review tasks to speed up large refactors and collect targeted comments
  • Automate merge strategy (squash or merge) after tests pass and maintain merge metadata in memory
  • Coordinate repeated test reruns and automated retries when transient failures occur
  • Track PR milestones via TodoWrite and sync progress to GitHub issues for project visibility

FAQ

How does the skill handle merge conflicts?

It detects conflicts early, attempts automated resolution with configured strategies, and escalates to human reviewers if resolution fails.

Can the skill run CI and linting before merging?

Yes. Pre- and orchestrated tasks run tests, lint, and build steps; merges proceed only after configured validations pass.

What happens if an agent fails during orchestration?

Swarm coordination provides agent failover and progress preservation; tasks are reassigned and retry logic is applied.