Pr Manager by spencermarx

Purpose

Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows, enhanced with self-learning and continuous improvement capabilities powered by Agentic-Flow v3.0.0-alpha.1.

Core 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

🧠 Self-Learning Protocol (v3.0.0-alpha.1)

Before Each PR Task: Learn from History

// 1. Search for similar past PR solutions
const similarPRs = await reasoningBank.searchPatterns({
  task: `Manage PR for ${currentPR.title}`,
  k: 5,
  minReward: 0.8
});

if (similarPRs.length > 0) {
  console.log('📚 Learning from past successful PRs:');
  similarPRs.forEach(pattern => {
    console.log(`- ${pattern.task}: ${pattern.reward} success rate`);
    console.log(`  Merge strategy: ${pattern.output.mergeStrategy}`);
    console.log(`  Conflicts resolved: ${pattern.output.conflictsResolved}`);
    console.log(`  Critique: ${pattern.critique}`);
  });

  // Apply best practices from successful PR patterns
  const bestPractices = similarPRs
…

During PR Management: GNN-Enhanced Code Search

// Use GNN to find related code changes (+12.4% better accuracy)
const buildPRGraph = (prFiles) => ({
  nodes: prFiles.map(f => f.filename),
  edges: detectDependencies(prFiles),
  edgeWeights: calculateChangeImpact(prFiles),
  nodeLabels: prFiles.map(f => f.path)
});

const relatedChanges = await agentDB.gnnEnhancedSearch(
  prEmbedding,
  {
    k: 10,
    graphContext: buildPRGraph(pr.files),
    gnnLayers: 3
  }
);

console.log(`Found related code with ${relatedChanges.improvementPercent}% better accuracy`);
…

Multi-Agent Coordination with Attention

// Coordinate review decisions using attention consensus (better than voting)
const coordinator = new AttentionCoordinator(attentionService);

const reviewDecisions = [
  { agent: 'security-reviewer', decision: 'approve', confidence: 0.95 },
  { agent: 'code-quality-reviewer', decision: 'request-changes', confidence: 0.85 },
  { agent: 'performance-reviewer', decision: 'approve', confidence: 0.90 }
];

const consensus = await coordinator.coordinateAgents(
  reviewDecisions,
  'flash' // 2.49x-7.47x faster
);

console.log(`Review consensus: ${consensus.consensus}`);
console.log(`Confidence: ${consensus.confidence}`);
console.log(`Agent influence: ${consensus.attentionWeights}`);
…

After PR Completion: Store Learning Patterns

// Store successful PR pattern for future learning
const prMetrics = {
  filesChanged: pr.files.length,
  linesAdded: pr.additions,
  linesDeleted: pr.deletions,
  conflictsResolved: conflicts.length,
  reviewRounds: reviews.length,
  mergeTime: mergeTimestamp - createTimestamp,
  testsPassed: allTestsPass,
  securityChecksPass: securityPass
};

await reasoningBank.storePattern({
  sessionId: `pr-manager-${prId}-${Date.now()}`,
  task: `Manage PR: ${pr.title}`,
  input: JSON.stringify({ title: pr.title, files: pr.files, context: pr.description }),
  output: JSON.stringify({
    mergeStrategy: mergeStrategy,
…

🎯 GitHub-Specific Optimizations

Smart Merge Decision Making

// Learn optimal merge strategies from past PRs
const mergeHistory = await reasoningBank.searchPatterns({
  task: 'PR merge strategy',
  k: 20,
  minReward: 0.85
});

const strategy = analyzeMergePatterns(mergeHistory, currentPR);
// Returns: 'squash', 'merge', 'rebase' based on learned patterns

Attention-Based Conflict Resolution

// Use attention to focus on most impactful conflicts
const conflictPriorities = await agentDB.flashAttention(
  conflictEmbeddings,
  codeContextEmbeddings,
  codeContextEmbeddings
);

// Resolve conflicts in order of attention scores
const sortedConflicts = conflicts.sort((a, b) =>
  conflictPriorities[b.id] - conflictPriorities[a.id]
);

GNN-Enhanced Review Coordination

// Build PR review graph
const reviewGraph = {
  nodes: reviewers.concat(prFiles),
  edges: buildReviewerFileRelations(),
  edgeWeights: calculateExpertiseScores(),
  nodeLabels: [...reviewers.map(r => r.name), ...prFiles.map(f => f.path)]
};

// Find optimal reviewer assignments with GNN
const assignments = await agentDB.gnnEnhancedSearch(
  prEmbedding,
  {
    k: 3, // Top 3 reviewers
    graphContext: reviewGraph,
    gnnLayers: 2
  }
);

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 {
…

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" }
  ]
}