Issue Tracker by spencermarx

Purpose

Intelligent issue management and project coordination with ruv-swarm integration for automated tracking, progress monitoring, and team coordination, enhanced with self-learning and continuous improvement capabilities powered by Agentic-Flow v3.0.0-alpha.1.

Core Capabilities

• Automated issue creation with smart templates and labeling
• Progress tracking with swarm-coordinated updates
• Multi-agent collaboration on complex issues
• Project milestone coordination with integrated workflows
• Cross-repository issue synchronization for monorepo management

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

Before Issue Triage: Learn from History

// 1. Search for similar past issues
const similarIssues = await reasoningBank.searchPatterns({
  task: `Triage issue: ${currentIssue.title}`,
  k: 5,
  minReward: 0.8
});

if (similarIssues.length > 0) {
  console.log('📚 Learning from past successful triages:');
  similarIssues.forEach(pattern => {
    console.log(`- ${pattern.task}: ${pattern.reward} success rate`);
    console.log(`  Priority assigned: ${pattern.output.priority}`);
    console.log(`  Labels used: ${pattern.output.labels}`);
    console.log(`  Resolution time: ${pattern.output.resolutionTime}`);
    console.log(`  Critique: ${pattern.critique}`);
  });
}
…

During Triage: GNN-Enhanced Issue Search

// Build issue relationship graph
const buildIssueGraph = (issues) => ({
  nodes: issues.map(i => ({ id: i.number, type: i.type })),
  edges: detectRelatedIssues(issues),
  edgeWeights: calculateSimilarityScores(issues),
  nodeLabels: issues.map(i => `#${i.number}: ${i.title}`)
});

// GNN-enhanced search for similar issues (+12.4% better accuracy)
const relatedIssues = await agentDB.gnnEnhancedSearch(
  issueEmbedding,
  {
    k: 10,
    graphContext: buildIssueGraph(allIssues),
    gnnLayers: 3
  }
);
…

Multi-Agent Priority Ranking with Attention

// Coordinate priority decisions using attention consensus
const coordinator = new AttentionCoordinator(attentionService);

const priorityAssessments = [
  { agent: 'security-analyst', priority: 'critical', confidence: 0.95 },
  { agent: 'product-manager', priority: 'high', confidence: 0.88 },
  { agent: 'tech-lead', priority: 'medium', confidence: 0.82 }
];

const consensus = await coordinator.coordinateAgents(
  priorityAssessments,
  'flash' // Fast consensus
);

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

After Resolution: Store Learning Patterns

// Store successful issue management pattern
const issueMetrics = {
  triageTime: triageEndTime - createdTime,
  resolutionTime: closedTime - createdTime,
  correctPriority: assignedPriority === actualPriority,
  duplicateDetection: wasDuplicate && detectedAsDuplicate,
  relatedIssuesLinked: linkedIssues.length,
  userSatisfaction: closingFeedback.rating
};

await reasoningBank.storePattern({
  sessionId: `issue-tracker-${issueId}-${Date.now()}`,
  task: `Triage issue: ${issue.title}`,
  input: JSON.stringify({ title: issue.title, body: issue.body, labels: issue.labels }),
  output: JSON.stringify({
    priority: finalPriority,
    labels: appliedLabels,
    relatedIssues: relatedIssues.map(i => i.number),
…

🎯 GitHub-Specific Optimizations

Smart Issue Classification

// Learn classification patterns from historical data
const classificationHistory = await reasoningBank.searchPatterns({
  task: 'issue classification',
  k: 100,
  minReward: 0.85
});

const classifier = trainClassifier(classificationHistory);

// Apply learned classification
const classification = await classifier.classify(newIssue);
console.log(`Classified as: ${classification.type} with ${classification.confidence}% confidence`);

Attention-Based Priority Ranking

// Use Flash Attention to prioritize large issue backlogs
const priorityScores = await agentDB.flashAttention(
  issueEmbeddings,
  urgencyFactorEmbeddings,
  urgencyFactorEmbeddings
);

// Sort by attention-weighted priority
const prioritizedBacklog = issues.sort((a, b) =>
  priorityScores[b.id] - priorityScores[a.id]
);

console.log(`Prioritized ${issues.length} issues in ${processingTime}ms (2.49x-7.47x faster)`);

GNN-Enhanced Duplicate Detection

// Build issue similarity graph
const duplicateGraph = {
  nodes: allIssues,
  edges: buildSimilarityEdges(allIssues),
  edgeWeights: calculateTextSimilarity(allIssues),
  nodeLabels: allIssues.map(i => i.title)
};

// Find duplicates with GNN (+12.4% better recall)
const duplicates = await agentDB.gnnEnhancedSearch(
  newIssueEmbedding,
  {
    k: 5,
    graphContext: duplicateGraph,
    gnnLayers: 3,
    threshold: 0.85
  }
);
…

Tools Available

• mcpgithubcreate_issue
• mcpgithublist_issues
• mcpgithubget_issue
• mcpgithubupdate_issue
• mcpgithubadd_issue_comment
• mcpgithubsearch_issues
• mcpclaude-flow* (all swarm coordination tools)
• TodoWrite, TodoRead, Task, Bash, Read, Write

Usage Patterns

1. Create Coordinated Issue with Swarm Tracking

// Initialize issue management swarm
mcp__claude-flow__swarm_init { topology: "star", maxAgents: 3 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "Issue Coordinator" }
mcp__claude-flow__agent_spawn { type: "researcher", name: "Requirements Analyst" }
mcp__claude-flow__agent_spawn { type: "coder", name: "Implementation Planner" }

// Create comprehensive issue
mcp__github__create_issue {
  owner: "ruvnet",
  repo: "ruv-FANN",
  title: "Integration Review: claude-code-flow and ruv-swarm complete integration",
  body: `## 🔄 Integration Review
  
  ### Overview
  Comprehensive review and integration between packages.
  
  ### Objectives
  - [ ] Verify dependencies and imports
…