issue-tracker

name: issue-tracker
description: Intelligent issue management and project coordination with automated tracking, progress monitoring, and team coordination
type: development
color: green
capabilities:
  - self_learning         # ReasoningBank pattern storage
  - context_enhancement   # GNN-enhanced search
  - fast_processing       # Flash Attention
  - smart_coordination    # Attention-based consensus
  - automated_issue_creation_with_smart_templates
  - progress_tracking_with_swarm_coordination
  - multi_agent_collaboration_on_complex_issues
  - project_milestone_coordination
  - cross_repository_issue_synchronization
  - intelligent_labeling_and_organization
tools:
  - mcp__claude-flow__swarm_init
  - mcp__claude-flow__agent_spawn
  - mcp__claude-flow__task_orchestrate
  - mcp__claude-flow__memory_usage
  - mcp__agentic-flow__agentdb_pattern_store
  - mcp__agentic-flow__agentdb_pattern_search
  - mcp__agentic-flow__agentdb_pattern_stats
  - Bash
  - TodoWrite
  - Read
  - Write
priority: high
hooks:
  pre: |
    echo "🚀 [Issue Tracker] starting: $TASK"

    # 1. Learn from past similar issue patterns (ReasoningBank)
    SIMILAR_ISSUES=$(npx agentdb-cli pattern search "Issue triage for $ISSUE_CONTEXT" --k=5 --min-reward=0.8)
    if [ -n "$SIMILAR_ISSUES" ]; then
      echo "📚 Found ${SIMILAR_ISSUES} similar successful issue patterns"
      npx agentdb-cli pattern stats "issue management" --k=5
    fi

    # 2. GitHub authentication
    echo "Initializing issue management swarm"
    gh auth status || (echo "GitHub CLI not authenticated" && exit 1)
    echo "Setting up issue coordination environment"

    # 3. Store task start
    npx agentdb-cli pattern store \
      --session-id "issue-tracker-$AGENT_ID-$(date +%s)" \
      --task "$TASK" \
      --input "$ISSUE_CONTEXT" \
      --status "started"

  post: |
    echo "✨ [Issue Tracker] completed: $TASK"

    # 1. Calculate issue management metrics
    REWARD=$(calculate_issue_quality "$ISSUE_OUTPUT")
    SUCCESS=$(validate_issue_resolution "$ISSUE_OUTPUT")
    TOKENS=$(count_tokens "$ISSUE_OUTPUT")
    LATENCY=$(measure_latency)

    # 2. Store learning pattern for future issue management
    npx agentdb-cli pattern store \
      --session-id "issue-tracker-$AGENT_ID-$(date +%s)" \
      --task "$TASK" \
      --input "$ISSUE_CONTEXT" \
      --output "$ISSUE_OUTPUT" \
      --reward "$REWARD" \
      --success "$SUCCESS" \
      --critique "$ISSUE_CRITIQUE" \
      --tokens-used "$TOKENS" \
      --latency-ms "$LATENCY"

    # 3. Standard post-checks
    echo "Issues created and coordinated"
    echo "Progress tracking initialized"
    echo "Swarm memory updated with issue state"

    # 4. Train neural patterns for successful issue management
    if [ "$SUCCESS" = "true" ] && [ "$REWARD" -gt "0.9" ]; then
      echo "🧠 Training neural pattern from successful issue management"
      npx claude-flow neural train \
        --pattern-type "coordination" \
        --training-data "$ISSUE_OUTPUT" \
        --epochs 50
    fi

GitHub Issue Tracker

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}`);
  });
}

// 2. Learn from misclassified issues
const triageFailures = await reasoningBank.searchPatterns({
  task: 'issue triage',
  onlyFailures: true,
  k: 3
});

if (triageFailures.length > 0) {
  console.log('⚠️  Avoiding past triage mistakes:');
  triageFailures.forEach(pattern => {
    console.log(`- ${pattern.critique}`);
    console.log(`  Misclassification: ${pattern.output.misclassification}`);
  });
}

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
  }
);

console.log(`Found ${relatedIssues.length} related issues with ${relatedIssues.improvementPercent}% better accuracy`);

// Detect duplicates with GNN
const potentialDuplicates = await agentDB.gnnEnhancedSearch(
  currentIssueEmbedding,
  {
    k: 5,
    graphContext: buildIssueGraph(openIssues),
    gnnLayers: 2,
    filter: 'open_issues'
  }
);

Multi-Agent Priority Ranking with Attention

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

const priorityAssessments = [
  { agent: 'security-analyst', priority: 'criti