agent-pr-manager
- Repo stars 54,444
- Author updated Live
- Author repo ruflo
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ruvnet · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-pr-manager
description: Agent skill for pr-manager - invoke with $agent-pr-manager name: pr-manager description: Compreh…
category: ai
runtime: no special runtime
---
# agent-pr-manager output preview
## PART A: Task fit
- Use case: 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" Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows. runs….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Purpose / Capabilities / Usage Patterns” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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" Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows. runs…”.
- **02** When the source has headings, the agent prioritizes “Purpose / Capabilities / Usage Patterns” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Purpose / Capabilities / Usage Patterns”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-pr-manager
description: Agent skill for pr-manager - invoke with $agent-pr-manager name: pr-manager description: Compreh…
category: ai
source: ruvnet/ruflo
---
# agent-pr-manager
## When to use
- Agent skill for pr-manager - invoke with $agent-pr-manager name: pr-manager description: Comprehensive pull request ma…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Purpose / Capabilities / Usage Patterns” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-pr-manager" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Purpose / Capabilities / Usage Patterns
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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$null || echo 'No open PRs'"
- "npm test --silent || echo 'Tests may need attention'" post:
- "gh pr status || echo 'No active PR in current branch'"
- "git branch --show-current"
- "gh pr checks || echo 'No PR checks available'"
- "git log --oneline -3"
GitHub PR Manager
Purpose
Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows.
Capabilities
- Multi-reviewer coordination with swarm agents
- Automated conflict resolution and merge strategies
- Comprehensive testing integration and validation
- Real-time progress tracking with GitHub issue coordination
- Intelligent branch management and synchronization
Usage Patterns
1. Create and Manage PR with Swarm Coordination
// Initialize review swarm
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 4 }
mcp__claude-flow__agent_spawn { type: "reviewer", name: "Code Quality Reviewer" }
mcp__claude-flow__agent_spawn { type: "tester", name: "Testing Agent" }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "PR Coordinator" }
// Create PR and orchestrate review
mcp__github__create_pull_request {
owner: "ruvnet",
repo: "ruv-FANN",
title: "Integration: claude-code-flow and ruv-swarm",
head: "integration$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
// 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$index.js", line: 45, body: "Import structure optimized" }
]
}
3. Merge Coordination with Testing
// Validate PR status and merge when ready
mcp__github__get_pull_request_status { owner: "ruvnet", repo: "ruv-FANN", pull_number: 54 }
// Merge with coordination
mcp__github__merge_pull_request {
owner: "ruvnet",
repo: "ruv-FANN",
pull_number: 54,
merge_method: "squash",
commit_title: "feat: Complete claude-code-flow and ruv-swarm integration",
commit_message: "Comprehensive integration with swarm coordination"
}
// Post-merge coordination
mcp__claude-flow__memory_usage {
action: "store",
key: "pr/54$merged",
value: { timestamp: Date.now(), status: "success" }
}
Batch Operations Example
Complete PR Lifecycle in Parallel:
[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
Decide Fit First
Design Intent
How To Use It
Boundaries And Review