agent-pr-manager
Skill1 starsChanged 7 months agoMITView on GitHub
Agent skill for pr-manager - invoke with $agent-pr-manager
- Commits and pushes
5 other projects use a version of this, 5 of them edited.
Adopted by
Most recently taken up by
- ruvnet/rufloedited, first seen Oct 21, 2026
- ruvnet/claude-flowedited, first seen Oct 8, 2026
- Soulcynics404/AgentForgeedited, first seen Aug 24, 2026
- jpoindexter/theft-kitedited, first seen May 20, 2026
- Zekiog/rufloedited, first seen Apr 8, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 5
- ruvnet/claude-flow★ 80k86% same
- ruvnet/ruflo★ 79k89% same
- Zekiog/ruflo★ 289% same
- jpoindexter/theft-kit★ 189% same
- Soulcynics404/AgentForge★ 189% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository175 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .claude/commands/monitoring/agents.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- v2/CLAUDE.mdCLAUDE.md
- v3/CLAUDE.mdCLAUDE.md
- agent-adaptive-coordinatorSkill
- agent-agentic-paymentsSkill
- agent-agentSkill
- agent-analyze-code-qualitySkill
- agent-app-storeSkill
- agent-architectureSkill
- agent-arch-system-designSkill
- agent-authenticationSkill
- agent-automation-smart-agentSkill
- agent-base-template-generatorSkill
- agent-benchmark-suiteSkill
- agent-byzantine-coordinatorSkill
- agent-challengesSkill
- agent-code-analyzerSkill
- agent-code-goal-plannerSkill
- agent-code-review-swarmSkill
- agent-coderSkill
- agent-collective-intelligence-coordinatorSkill
- agent-consensus-coordinatorSkill
- agent-coordinationSkill
- agent-coordinator-swarm-initSkill
- agent-crdt-synchronizerSkill
- agent-data-ml-modelSkill
- AgentDB Advanced FeaturesSkill
- AgentDB Learning PluginsSkill
- AgentDB Memory PatternsSkill
- AgentDB Performance OptimizationSkill
- AgentDB Vector SearchSkill
- agent-dev-backend-apiSkill
- agent-docs-api-openapiSkill
- agent-github-modesSkill
- agent-github-pr-managerSkill
- agent-goal-plannerSkill
- agent-gossip-coordinatorSkill
- agent-hierarchical-coordinatorSkill
- agentic-jujutsuSkill
- agent-implementer-sparc-coderSkill
- agent-issue-trackerSkill
- agent-load-balancerSkill
- agent-matrix-optimizerSkill
- agent-memory-coordinatorSkill
- agent-mesh-coordinatorSkill
- agent-migration-planSkill
- agent-multi-repo-swarmSkill
- agent-neural-networkSkill
- agent-ops-cicd-githubSkill
- agent-orchestrator-taskSkill
- agent-pagerank-analyzerSkill
- agent-paymentsSkill
- agent-performance-analyzerSkill
- agent-performance-benchmarkerSkill
- agent-performance-monitorSkill
- agent-performance-optimizerSkill
- agent-plannerSkill
- agent-production-validatorSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. 4 are common, 0 are distinctive, and 0 are very specific to this project.
4
Common
Most of the 5 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 158
Use memory for cross-agent coordination
- Common: line 171
Use TodoWrite for PR milestone tracking
- Common: line 189
Test failures with automatic re-runs
- Common: line 193
No single point of failure
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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__fidgetflo__swarm_init
- mcp__fidgetflo__agent_spawn
- mcp__fidgetflo__task_orchestrate
- mcp__fidgetflo__swarm_status
- mcp__fidgetflo__memory_usage
- mcp__fidgetflo__github_pr_manage
- mcp__fidgetflo__github_code_review
- mcp__fidgetflo__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$null || echo 'No open PRs'"
- "npm test --silent || echo 'Tests may need attention'" post:
- "gh pr status || echo 'No active PR in current branch'"
- "git branch --show-current"
- "gh pr checks || echo 'No PR checks available'"
- "git log --oneline -3"
GitHub PR Manager
Purpose
Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows.
Capabilities
- Multi-reviewer coordination with swarm agents
- Automated conflict resolution and merge strategies
- Comprehensive testing integration and validation
- Real-time progress tracking with GitHub issue coordination
- Intelligent branch management and synchronization
Usage Patterns
1. Create and Manage PR with Swarm Coordination
// Initialize review swarm
mcp__fidgetflo__swarm_init { topology: "mesh", maxAgents: 4 }
mcp__fidgetflo__agent_spawn { type: "reviewer", name: "Code Quality Reviewer" }
mcp__fidgetflo__agent_spawn { type: "tester", name: "Testing Agent" }
mcp__fidgetflo__agent_spawn { type: "coordinator", name: "PR Coordinator" }
// Create PR and orchestrate review
mcp__github__create_pull_request {
owner: "ruvnet",
repo: "ruv-FANN",
title: "Integration: claude-code-flow and ruv-swarm",
head: "integration$claude-code-flow-ruv-swarm",
base: "main",
body: "Comprehensive integration between packages..."
}
// Orchestrate review process
mcp__fidgetflo__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$index.js", line: 45, body: "Import structure optimized" }
]
}3. Merge Coordination with Testing
// Validate PR status and merge when ready
mcp__github__get_pull_request_status { owner: "ruvnet", repo: "ruv-FANN", pull_number: 54 }
// Merge with coordination
mcp__github__merge_pull_request {
owner: "ruvnet",
repo: "ruv-FANN",
pull_number: 54,
merge_method: "squash",
commit_title: "feat: Complete claude-code-flow and ruv-swarm integration",
commit_message: "Comprehensive integration with swarm coordination"
}
// Post-merge coordination
mcp__fidgetflo__memory_usage {
action: "store",
key: "pr/54$merged",
value: { timestamp: Date.now(), status: "success" }
}Batch Operations Example
Complete PR Lifecycle in Parallel:
[Single Message - Complete PR Management]:
// Initialize coordination
mcp__fidgetflo__swarm_init { topology: "hierarchical", maxAgents: 5 }
mcp__fidgetflo__agent_spawn { type: "reviewer", name: "Senior Reviewer" }
mcp__fidgetflo__agent_spawn { type: "tester", name: "QA Engineer" }
mcp__fidgetflo__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
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

