wifi-densepose/npm/packages/ruvllm/scripts/training/claude-flow-capabilities.json

{
  "version": "3.0.0",
  "generated": "2025-01-20",
  "description": "Comprehensive Claude Flow CLI capabilities for AI routing and training",

  "cli_commands": [
    {
      "name": "init",
      "description": "Project initialization with wizard, presets, skills, and hooks configuration",
      "category": "core",
      "subcommands": ["wizard", "presets", "skills", "hooks"],
      "keywords": ["initialize", "setup", "configure", "project", "wizard", "presets"],
      "example_prompts": [
        "initialize claude flow project",
        "setup new project with wizard",
        "configure claude-flow with presets"
      ]
    },
    {
      "name": "agent",
      "description": "Agent lifecycle management including spawn, list, status, stop, metrics, pool, health, and logs",
      "category": "core",
      "subcommands": ["spawn", "list", "status", "stop", "metrics", "pool", "health", "logs"],
      "keywords": ["agent", "spawn", "worker", "lifecycle", "status", "health", "metrics"],
      "example_prompts": [
        "spawn a coder agent",
        "list all active agents",
        "check agent health status",
        "get agent metrics"
      ]
    },
    {
      "name": "swarm",
      "description": "Multi-agent swarm coordination and orchestration with topology management",
      "category": "core",
      "subcommands": ["init", "status", "scale", "shutdown", "health", "monitor"],
      "keywords": ["swarm", "orchestration", "coordination", "multi-agent", "parallel", "topology"],
      "example_prompts": [
        "initialize swarm with hierarchical topology",
        "check swarm status",
        "scale swarm to 10 agents",
        "monitor swarm progress"
      ]
    },
    {
      "name": "memory",
      "description": "AgentDB memory operations with vector search (150x-12,500x faster) including store, retrieve, search, list, delete",
      "category": "core",
      "subcommands": ["store", "retrieve", "search", "list", "delete", "init", "stats", "export", "import", "clear", "namespace"],
      "keywords": ["memory", "store", "retrieve", "search", "vector", "agentdb", "HNSW"],
      "example_prompts": [
        "store pattern in memory",
        "search memory for authentication patterns",
        "list all memory entries",
        "retrieve value by key"
      ]
    },
    {
      "name": "mcp",
      "description": "MCP server management and tool execution for Model Context Protocol integration",
      "category": "core",
      "subcommands": ["start", "stop", "status", "list", "add", "remove", "exec", "config", "health"],
      "keywords": ["mcp", "server", "model", "context", "protocol", "tools"],
      "example_prompts": [
        "start MCP server",
        "list available MCP tools",
        "add MCP server configuration"
      ]
    },
    {
      "name": "task",
      "description": "Task creation, assignment, tracking, and lifecycle management",
      "category": "core",
      "subcommands": ["create", "status", "list", "complete", "update", "cancel"],
      "keywords": ["task", "create", "assign", "track", "complete", "workflow"],
      "example_prompts": [
        "create new development task",
        "check task status",
        "list all pending tasks",
        "mark task as complete"
      ]
    },
    {
      "name": "session",
      "description": "Session state management, persistence, and recovery across conversations",
      "category": "core",
      "subcommands": ["save", "restore", "list", "delete", "info", "export", "import"],
      "keywords": ["session", "state", "save", "restore", "persist", "recovery"],
      "example_prompts": [
        "save current session",
        "restore previous session",
        "list all saved sessions"
      ]
    },
    {
      "name": "config",
      "description": "Configuration management and provider setup with scoped settings",
      "category": "core",
      "subcommands": ["get", "set", "list", "reset", "export", "import", "validate"],
      "keywords": ["config", "configuration", "settings", "provider", "setup"],
      "example_prompts": [
        "get configuration value",
        "set default topology",
        "export configuration"
      ]
    },
    {
      "name": "status",
      "description": "System status monitoring with watch mode for real-time updates",
      "category": "core",
      "subcommands": ["system", "watch", "verbose"],
      "keywords": ["status", "monitor", "health", "watch", "system"],
      "example_prompts": [
        "check system status",
        "watch status in real-time",
        "get verbose status report"
      ]
    },
    {
      "name": "workflow",
      "description": "Workflow execution, template management, and automation",
      "category": "core",
      "subcommands": ["create", "execute", "status", "list", "pause", "resume", "cancel", "delete", "template"],
      "keywords": ["workflow", "automation", "template", "execute", "pipeline"],
      "example_prompts": [
        "create development workflow",
        "execute workflow template",
        "pause running workflow"
      ]
    },
    {
      "name": "hooks",
      "description": "Self-learning hooks system with 27 hooks and 12 background workers for automation",
      "category": "core",
      "subcommands": ["pre-edit", "post-edit", "pre-command", "post-command", "pre-task", "post-task", "session-start", "session-end", "session-restore", "route", "explain", "pretrain", "build-agents", "metrics", "transfer", "list", "intelligence", "worker"],
      "keywords": ["hooks", "learning", "automation", "pre-edit", "post-task", "workers", "intelligence"],
      "example_prompts": [
        "run pre-task hook",
        "get routing recommendation",
        "dispatch background worker",
        "view learning metrics"
      ]
    },
    {
      "name": "hive-mind",
      "description": "Queen-led Byzantine fault-tolerant consensus for distributed coordination",
      "category": "core",
      "subcommands": ["init", "status", "join", "leave", "consensus", "broadcast", "memory", "spawn"],
      "keywords": ["hive-mind", "consensus", "byzantine", "distributed", "queen", "coordination"],
      "example_prompts": [
        "initialize hive-mind collective",
        "join agent to hive-mind",
        "broadcast message to all workers"
      ]
    },
    {
      "name": "daemon",
      "description": "Background worker daemon for continuous processing and automation",
      "category": "advanced",
      "subcommands": ["start", "stop", "status", "trigger", "enable"],
      "keywords": ["daemon", "background", "worker", "continuous", "process"],
      "example_prompts": [
        "start daemon process",
        "check daemon status",
        "trigger daemon worker"
      ]
    },
    {
      "name": "neural",
      "description": "Neural pattern training with MoE, SONA, and HNSW for intelligent routing",
      "category": "advanced",
      "subcommands": ["train", "status", "patterns", "predict", "optimize"],
      "keywords": ["neural", "train", "patterns", "MoE", "SONA", "HNSW", "learning"],
      "example_prompts": [
        "train neural patterns",
        "predict optimal approach",
        "view learned patterns"
      ]
    },
    {
      "name": "security",
      "description": "Security scanning, CVE detection, vulnerability analysis, and audit reports",
      "category": "advanced",
      "subcommands": ["scan", "audit", "cve", "threats", "validate", "report"],
      "keywords": ["security", "scan", "CVE", "vulnerability", "audit", "threats"],
      "example_prompts": [
        "run security scan",
        "audit codebase for vulnerabilities",
        "check for known CVEs"
      ]
    },
    {
      "name": "performance",
      "description": "Performance profiling, benchmarking, bottleneck detection, and optimization",
      "category": "advanced",
      "subcommands": ["benchmark", "profile", "metrics", "optimize", "report"],
      "keywords": ["performance", "benchmark", "profile", "optimize", "bottleneck", "metrics"],
      "example_prompts": [
        "run performance benchmark",
        "profile application performance",
        "detect bottlenecks"
      ]
    },
    {
      "name": "providers",
      "description": "AI provider management for multi-model support",
      "category": "advanced",
      "subcommands": ["list", "add", "remove", "test", "configure"],
      "keywords": ["providers", "AI", "models", "anthropic", "openai", "configure"],
      "example_prompts": [
        "list available providers",
        "add new AI provider",
        "test provider connection"
      ]
    },
    {
      "name": "plugins",
      "description": "Plugin management for extending functionality",
      "category": "advanced",
      "subcommands": ["list", "install", "uninstall", "enable", "disable"],
      "keywords": ["plugins", "extensions", "install", "manage"],
      "example_prompts": [
        "list installed plugins",
        "install new plugin",
        "enable plugin"
      ]
    },
    {
      "name": "deployment",
      "description": "Deployment management with rollback support and environment configuration",
      "category": "advanced",
      "subcommands": ["deploy", "rollback", "status", "environments", "release"],
      "keywords": ["deployment", "deploy", "rollback", "release", "environment"],
      "example_prompts": [
        "deploy to production",
        "rollback deployment",
        "check deployment status"
      ]
    },
    {
      "name": "embeddings",
      "description": "Vector embeddings with ONNX support - 75x faster with hyperbolic space",
      "category": "advanced",
      "subcommands": ["init", "generate", "compare", "search", "neural", "hyperbolic", "status"],
      "keywords": ["embeddings", "vectors", "ONNX", "hyperbolic", "semantic", "similarity"],
      "example_prompts": [
        "generate text embeddings",
        "compare text similarity",
        "semantic search with embeddings"
      ]
    },
    {
      "name": "claims",
      "description": "Claims-based work coordination for human-agent collaboration",
      "category": "advanced",
      "subcommands": ["claim", "release", "handoff", "accept-handoff", "status", "list", "mark-stealable", "steal", "stealable", "load", "board", "rebalance"],
      "keywords": ["claims", "coordination", "handoff", "collaboration", "work-distribution"],
      "example_prompts": [
        "claim issue for work",
        "handoff task to another agent",
        "view claims board"
      ]
    },
    {
      "name": "migrate",
      "description": "V2 to V3 migration with backup and rollback support",
      "category": "advanced",
      "subcommands": ["status", "run", "rollback", "validate", "backup"],
      "keywords": ["migrate", "upgrade", "v2", "v3", "backup", "rollback"],
      "example_prompts": [
        "check migration status",
        "run v3 migration",
        "rollback migration"
      ]
    },
    {
      "name": "doctor",
      "description": "System diagnostics with comprehensive health checks and auto-fix",
      "category": "advanced",
      "subcommands": ["check", "fix"],
      "keywords": ["doctor", "diagnostics", "health", "fix", "troubleshoot"],
      "example_prompts": [
        "run system diagnostics",
        "fix common issues",
        "check environment health"
      ]
    },
    {
      "name": "completions",
      "description": "Shell completions for bash, zsh, fish, and powershell",
      "category": "advanced",
      "subcommands": ["bash", "zsh", "fish", "powershell"],
      "keywords": ["completions", "shell", "bash", "zsh", "autocomplete"],
      "example_prompts": [
        "generate bash completions",
        "setup zsh autocomplete"
      ]
    },
    {
      "name": "aidefence",
      "description": "AI manipulation threat detection for prompt injection and PII scanning",
      "category": "advanced",
      "subcommands": ["scan", "analyze", "stats", "learn", "is_safe", "has_pii"],
      "keywords": ["aidefence", "security", "prompt-injection", "PII", "threats"],
      "example_prompts": [
        "scan input for threats",
        "check for PII in content",
        "analyze security threats"
      ]
    },
    {
      "name": "transfer",
      "description": "Pattern transfer and plugin store for sharing learned patterns",
      "category": "advanced",
      "subcommands": ["detect-pii", "ipfs-resolve", "store-search", "store-info", "store-download", "store-featured", "store-trending", "plugin-search", "plugin-info", "plugin-featured", "plugin-official"],
      "keywords": ["transfer", "patterns", "plugins", "store", "share"],
      "example_prompts": [
        "search pattern store",
        "download pattern template",
        "find trending plugins"
      ]
    }
  ],

  "agent_types": [
    {
      "name": "coder",
      "description": "Code implementation specialist for writing clean, efficient code",
      "category": "core",
      "keywords": ["code", "implement", "develop", "write", "programming"],
      "example_prompts": ["implement feature", "write code", "develop function"]
    },
    {
      "name": "reviewer",
      "description": "Code review specialist for quality, security, and best practices",
      "category": "core",
      "keywords": ["review", "quality", "check", "audit", "standards"],
      "example_prompts": ["review code", "check code quality", "audit implementation"]
    },
    {
      "name": "tester",
      "description": "Testing specialist for unit, integration, and e2e tests",
      "category": "core",
      "keywords": ["test", "testing", "unit", "integration", "e2e", "coverage"],
      "example_prompts": ["write tests", "create test suite", "improve coverage"]
    },
    {
      "name": "planner",
      "description": "Planning specialist for task breakdown and project planning",
      "category": "core",
      "keywords": ["plan", "breakdown", "organize", "schedule", "roadmap"],
      "example_prompts": ["plan project", "break down task", "create roadmap"]
    },
    {
      "name": "researcher",
      "description": "Research specialist for investigation and analysis",
      "category": "core",
      "keywords": ["research", "investigate", "analyze", "explore", "discover"],
      "example_prompts": ["research topic", "investigate issue", "analyze patterns"]
    },
    {
      "name": "security-architect",
      "description": "Security architecture specialist for secure system design",
      "category": "v3-specialized",
      "keywords": ["security", "architecture", "design", "secure", "threat-modeling"],
      "example_prompts": ["design secure system", "review security architecture"]
    },
    {
      "name": "security-auditor",
      "description": "Security audit specialist for vulnerability assessment",
      "category": "v3-specialized",
      "keywords": ["audit", "vulnerability", "assessment", "penetration", "CVE"],
      "example_prompts": ["audit security", "find vulnerabilities", "CVE assessment"]
    },
    {
      "name": "memory-specialist",
      "description": "Memory optimization specialist for efficient data management",
      "category": "v3-specialized",
      "keywords": ["memory", "optimization", "cache", "storage", "efficiency"],
      "example_prompts": ["optimize memory", "improve caching", "reduce memory usage"]
    },
    {
      "name": "performance-engineer",
      "description": "Performance optimization specialist for speed and efficiency",
      "category": "v3-specialized",
      "keywords": ["performance", "optimization", "speed", "efficiency", "bottleneck"],
      "example_prompts": ["optimize performance", "find bottlenecks", "improve speed"]
    },
    {
      "name": "hierarchical-coordinator",
      "description": "Swarm coordinator for hierarchical topology management",
      "category": "swarm-coordination",
      "keywords": ["coordinator", "hierarchical", "swarm", "orchestrate", "manage"],
      "example_prompts": ["coordinate swarm", "manage agent hierarchy"]
    },
    {
      "name": "mesh-coordinator",
      "description": "Swarm coordinator for mesh topology peer-to-peer coordination",
      "category": "swarm-coordination",
      "keywords": ["mesh", "coordinator", "peer-to-peer", "distributed"],
      "example_prompts": ["coordinate mesh swarm", "manage peer communication"]
    },
    {
      "name": "adaptive-coordinator",
      "description": "Dynamic coordinator that adapts to changing workloads",
      "category": "swarm-coordination",
      "keywords": ["adaptive", "dynamic", "coordinator", "scaling"],
      "example_prompts": ["adapt coordination strategy", "dynamic workload management"]
    },
    {
      "name": "collective-intelligence-coordinator",
      "description": "Coordinator for collective decision-making and consensus",
      "category": "swarm-coordination",
      "keywords": ["collective", "intelligence", "consensus", "decision"],
      "example_prompts": ["coordinate collective decision", "achieve consensus"]
    },
    {
      "name": "swarm-memory-manager",
      "description": "Shared memory manager for swarm state coordination",
      "category": "swarm-coordination",
      "keywords": ["memory", "shared", "state", "coordination", "sync"],
      "example_prompts": ["manage swarm memory", "sync shared state"]
    },
    {
      "name": "byzantine-coordinator",
      "description": "Fault-tolerant coordinator for Byzantine consensus",
      "category": "consensus",
      "keywords": ["byzantine", "fault-tolerant", "consensus", "distributed"],
      "example_prompts": ["coordinate byzantine consensus", "handle faulty nodes"]
    },
    {
      "name": "raft-manager",
      "description": "Raft consensus manager for leader election and state replication",
      "category": "consensus",
      "keywords": ["raft", "consensus", "leader", "election", "replication"],
      "example_prompts": ["manage raft consensus", "leader election"]
    },
    {
      "name": "gossip-coordinator",
      "description": "Gossip protocol coordinator for eventual consistency",
      "category": "consensus",
      "keywords": ["gossip", "protocol", "eventual", "consistency", "epidemic"],
      "example_prompts": ["coordinate gossip protocol", "achieve eventual consistency"]
    },
    {
      "name": "consensus-builder",
      "description": "General consensus builder for distributed agreement",
      "category": "consensus",
      "keywords": ["consensus", "agreement", "distributed", "voting"],
      "example_prompts": ["build consensus", "achieve agreement"]
    },
    {
      "name": "crdt-synchronizer",
      "description": "CRDT synchronizer for conflict-free replicated data",
      "category": "consensus",
      "keywords": ["CRDT", "conflict-free", "replicated", "sync", "merge"],
      "example_prompts": ["sync CRDT data", "merge replicated state"]
    },
    {
      "name": "quorum-manager",
      "description": "Quorum manager for vote-based consensus",
      "category": "consensus",
      "keywords": ["quorum", "voting", "majority", "consensus"],
      "example_prompts": ["manage quorum voting", "achieve majority consensus"]
    },
    {
      "name": "security-manager",
      "description": "Security manager for distributed system security",
      "category": "consensus",
      "keywords": ["security", "distributed", "authentication", "authorization"],
      "example_prompts": ["manage distributed security", "secure communication"]
    },
    {
      "name": "perf-analyzer",
      "description": "Performance analyzer for system profiling and optimization",
      "category": "performance",
      "keywords": ["performance", "analyzer", "profiling", "metrics"],
      "example_prompts": ["analyze performance", "profile system"]
    },
    {
      "name": "performance-benchmarker",
      "description": "Benchmarking specialist for performance measurement",
      "category": "performance",
      "keywords": ["benchmark", "measure", "performance", "comparison"],
      "example_prompts": ["run benchmarks", "measure performance"]
    },
    {
      "name": "task-orchestrator",
      "description": "Task orchestration specialist for workflow management",
      "category": "performance",
      "keywords": ["orchestrate", "workflow", "tasks", "pipeline"],
      "example_prompts": ["orchestrate tasks", "manage workflow pipeline"]
    },
    {
      "name": "memory-coordinator",
      "description": "Memory coordination specialist for distributed state",
      "category": "performance",
      "keywords": ["memory", "coordination", "distributed", "state"],
      "example_prompts": ["coordinate memory", "manage distributed state"]
    },
    {
      "name": "smart-agent",
      "description": "Self-learning agent with adaptive capabilities",
      "category": "performance",
      "keywords": ["smart", "adaptive", "learning", "self-improving"],
      "example_prompts": ["create smart agent", "deploy adaptive agent"]
    },
    {
      "name": "github-modes",
      "description": "GitHub integration specialist for repository management",
      "category": "github",
      "keywords": ["github", "repository", "integration", "git"],
      "example_prompts": ["manage github repo", "integrate with github"]
    },
    {
      "name": "pr-manager",
      "description": "Pull request management specialist",
      "category": "github",
      "keywords": ["PR", "pull-request", "merge", "review"],
      "example_prompts": ["manage pull requests", "review PRs"]
    },
    {
      "name": "code-review-swarm",
      "description": "Multi-agent code review swarm coordinator",
      "category": "github",
      "keywords": ["code-review", "swarm", "multi-agent", "review"],
      "example_prompts": ["coordinate code review swarm", "multi-agent review"]
    },
    {
      "name": "issue-tracker",
      "description": "Issue tracking and management specialist",
      "category": "github",
      "keywords": ["issues", "tracking", "bugs", "features"],
      "example_prompts": ["track issues", "manage bug reports"]
    },
    {
      "name": "release-manager",
      "description": "Release management and versioning specialist",
      "category": "github",
      "keywords": ["release", "version", "deploy", "changelog"],
      "example_prompts": ["manage releases", "create new version"]
    },
    {
      "name": "workflow-automation",
      "description": "GitHub Actions workflow automation specialist",
      "category": "github",
      "keywords": ["automation", "actions", "CI", "CD", "pipeline"],
      "example_prompts": ["automate workflow", "setup CI/CD"]
    },
    {
      "name": "project-board-sync",
      "description": "Project board synchronization specialist",
      "category": "github",
      "keywords": ["project", "board", "sync", "kanban"],
      "example_prompts": ["sync project board", "manage kanban"]
    },
    {
      "name": "repo-architect",
      "description": "Repository architecture and structure specialist",
      "category": "github",
      "keywords": ["architecture", "structure", "repository", "organization"],
      "example_prompts": ["design repo structure", "organize repository"]
    },
    {
      "name": "multi-repo-swarm",
      "description": "Multi-repository coordination swarm",
      "category": "github",
      "keywords": ["multi-repo", "coordination", "monorepo", "polyrepo"],
      "example_prompts": ["coordinate multiple repos", "manage monorepo"]
    },
    {
      "name": "sparc-coord",
      "description": "SPARC methodology coordinator",
      "category": "sparc",
      "keywords": ["SPARC", "methodology", "coordinator", "orchestrate"],
      "example_prompts": ["coordinate SPARC workflow", "orchestrate development"]
    },
    {
      "name": "sparc-coder",
      "description": "SPARC methodology coder for implementation phase",
      "category": "sparc",
      "keywords": ["SPARC", "coder", "implementation", "code"],
      "example_prompts": ["implement SPARC code", "SPARC development"]
    },
    {
      "name": "specification",
      "description": "SPARC specification writer",
      "category": "sparc",
      "keywords": ["specification", "requirements", "SPARC", "design"],
      "example_prompts": ["write specification", "define requirements"]
    },
    {
      "name": "pseudocode",
      "description": "SPARC pseudocode designer",
      "category": "sparc",
      "keywords": ["pseudocode", "algorithm", "design", "SPARC"],
      "example_prompts": ["write pseudocode", "design algorithm"]
    },
    {
      "name": "architecture",
      "description": "SPARC architecture designer",
      "category": "sparc",
      "keywords": ["architecture", "design", "system", "SPARC"],
      "example_prompts": ["design architecture", "system design"]
    },
    {
      "name": "refinement",
      "description": "SPARC refinement and optimization specialist",
      "category": "sparc",
      "keywords": ["refinement", "optimization", "improve", "SPARC"],
      "example_prompts": ["refine implementation", "optimize code"]
    },
    {
      "name": "backend-dev",
      "description": "Backend development specialist",
      "category": "specialized",
      "keywords": ["backend", "server", "API", "database"],
      "example_prompts": ["develop backend", "build API"]
    },
    {
      "name": "mobile-dev",
      "description": "Mobile development specialist",
      "category": "specialized",
      "keywords": ["mobile", "iOS", "Android", "React Native"],
      "example_prompts": ["develop mobile app", "build iOS feature"]
    },
    {
      "name": "ml-developer",
      "description": "Machine learning development specialist",
      "category": "specialized",
      "keywords": ["ML", "machine-learning", "AI", "model"],
      "example_prompts": ["develop ML model", "train AI"]
    },
    {
      "name": "cicd-engineer",
      "description": "CI/CD pipeline engineering specialist",
      "category": "specialized",
      "keywords": ["CI", "CD", "pipeline", "automation", "DevOps"],
      "example_prompts": ["setup CI/CD", "build pipeline"]
    },
    {
      "name": "api-docs",
      "description": "API documentation specialist",
      "category": "specialized",
      "keywords": ["API", "documentation", "OpenAPI", "swagger"],
      "example_prompts": ["write API docs", "generate OpenAPI spec"]
    },
    {
      "name": "system-architect",
      "description": "System architecture design specialist",
      "category": "specialized",
      "keywords": ["system", "architecture", "design", "scalability"],
      "example_prompts": ["design system architecture", "plan scalability"]
    },
    {
      "name": "code-analyzer",
      "description": "Static code analysis specialist",
      "category": "specialized",
      "keywords": ["analysis", "static", "code-quality", "lint"],
      "example_prompts": ["analyze code", "run static analysis"]
    },
    {
      "name": "base-template-generator",
      "description": "Project template generation specialist",
      "category": "specialized",
      "keywords": ["template", "generator", "scaffold", "boilerplate"],
      "example_prompts": ["generate template", "create scaffold"]
    },
    {
      "name": "tdd-london-swarm",
      "description": "TDD London-style testing swarm",
      "category": "testing",
      "keywords": ["TDD", "London", "testing", "mocks"],
      "example_prompts": ["TDD development", "London-style testing"]
    },
    {
      "name": "production-validator",
      "description": "Production readiness validation specialist",
      "category": "testing",
      "keywords": ["production", "validation", "readiness", "deployment"],
      "example_prompts": ["validate for production", "check deployment readiness"]
    },
    {
      "name": "debugger",
      "description": "Debugging and troubleshooting specialist",
      "category": "core",
      "keywords": ["debug", "troubleshoot", "fix", "issue", "bug"],
      "example_prompts": ["debug issue", "fix bug", "troubleshoot error"]
    },
    {
      "name": "documenter",
      "description": "Documentation writing specialist",
      "category": "core",
      "keywords": ["documentation", "docs", "write", "explain"],
      "example_prompts": ["write documentation", "document API"]
    },
    {
      "name": "analyst",
      "description": "Data and system analysis specialist",
      "category": "core",
      "keywords": ["analyze", "data", "metrics", "insights"],
      "example_prompts": ["analyze data", "generate insights"]
    },
    {
      "name": "optimizer",
      "description": "Code and system optimization specialist",
      "category": "core",
      "keywords": ["optimize", "improve", "refactor", "efficiency"],
      "example_prompts": ["optimize code", "improve efficiency"]
    },
    {
      "name": "architect",
      "description": "Software architecture design specialist",
      "category": "core",
      "keywords": ["architecture", "design", "structure", "patterns"],
      "example_prompts": ["design architecture", "plan structure"]
    },
    {
      "name": "devops",
      "description": "DevOps and infrastructure specialist",
      "category": "specialized",
      "keywords": ["devops", "infrastructure", "deploy", "kubernetes"],
      "example_prompts": ["setup infrastructure", "deploy application"]
    },
    {
      "name": "qa-engineer",
      "description": "Quality assurance engineering specialist",
      "category": "testing",
      "keywords": ["QA", "quality", "testing", "validation"],
      "example_prompts": ["QA testing", "quality assurance"]
    }
  ],

  "hooks": [
    {
      "name": "pre-edit",
      "description": "Get context and agent suggestions before editing files",
      "category": "file-operations",
      "keywords": ["edit", "file", "context", "suggestions", "before"],
      "example_prompts": ["get edit context", "prepare for file edit"]
    },
    {
      "name": "post-edit",
      "description": "Record editing outcome for learning and neural training",
      "category": "file-operations",
      "keywords": ["edit", "record", "learn", "neural", "outcome"],
      "example_prompts": ["record edit success", "train on edit"]
    },
    {
      "name": "pre-command",
      "description": "Assess risk before executing commands",
      "category": "command-execution",
      "keywords": ["command", "risk", "assess", "before", "safety"],
      "example_prompts": ["assess command risk", "validate command safety"]
    },
    {
      "name": "post-command",
      "description": "Record command execution outcome for learning",
      "category": "command-execution",
      "keywords": ["command", "record", "outcome", "metrics", "after"],
      "example_prompts": ["record command result", "track command metrics"]
    },
    {
      "name": "pre-task",
      "description": "Record task start and get agent suggestions with model routing",
      "category": "task-management",
      "keywords": ["task", "start", "suggestions", "routing", "model"],
      "example_prompts": ["start task tracking", "get task routing"]
    },
    {
      "name": "post-task",
      "description": "Record task completion for learning and improvement",
      "category": "task-management",
      "keywords": ["task", "complete", "record", "learn", "metrics"],
      "example_prompts": ["record task completion", "finish task tracking"]
    },
    {
      "name": "session-start",
      "description": "Initialize session with auto-start daemon and state restoration",
      "category": "session-management",
      "keywords": ["session", "start", "initialize", "daemon", "restore"],
      "example_prompts": ["start new session", "initialize session"]
    },
    {
      "name": "session-end",
      "description": "End session, stop daemon, and persist state with metrics export",
      "category": "session-management",
      "keywords": ["session", "end", "persist", "export", "metrics"],
      "example_prompts": ["end session", "save session state"]
    },
    {
      "name": "session-restore",
      "description": "Restore a previous session with agents and tasks",
      "category": "session-management",
      "keywords": ["session", "restore", "recover", "previous"],
      "example_prompts": ["restore session", "recover previous session"]
    },
    {
      "name": "route",
      "description": "Route task to optimal agent using learned patterns",
      "category": "routing",
      "keywords": ["route", "optimal", "agent", "patterns", "recommend"],
      "example_prompts": ["route task to agent", "get routing recommendation"]
    },
    {
      "name": "explain",
      "description": "Explain routing decision with full transparency",
      "category": "routing",
      "keywords": ["explain", "routing", "decision", "transparency"],
      "example_prompts": ["explain routing", "why this agent"]
    },
    {
      "name": "pretrain",
      "description": "Analyze repository to bootstrap intelligence (4-step pipeline)",
      "category": "learning",
      "keywords": ["pretrain", "analyze", "bootstrap", "intelligence", "pipeline"],
      "example_prompts": ["pretrain on repo", "bootstrap intelligence"]
    },
    {
      "name": "build-agents",
      "description": "Generate optimized agent configurations from pretrain data",
      "category": "learning",
      "keywords": ["build", "agents", "configurations", "optimize"],
      "example_prompts": ["build agent configs", "generate optimized agents"]
    },
    {
      "name": "metrics",
      "description": "View learning metrics dashboard with V3 performance data",
      "category": "monitoring",
      "keywords": ["metrics", "dashboard", "learning", "performance"],
      "example_prompts": ["view metrics", "learning dashboard"]
    },
    {
      "name": "transfer",
      "description": "Transfer learned patterns from another project",
      "category": "learning",
      "keywords": ["transfer", "patterns", "project", "knowledge"],
      "example_prompts": ["transfer patterns", "import knowledge"]
    },
    {
      "name": "list",
      "description": "List all registered hooks with their configurations",
      "category": "management",
      "keywords": ["list", "hooks", "registered", "configuration"],
      "example_prompts": ["list hooks", "show registered hooks"]
    },
    {
      "name": "intelligence",
      "description": "RuVector intelligence system status and trajectory management",
      "category": "intelligence",
      "keywords": ["intelligence", "RuVector", "trajectory", "pattern", "stats"],
      "example_prompts": ["intelligence status", "check RuVector"]
    },
    {
      "name": "worker",
      "description": "Background worker management and dispatch",
      "category": "workers",
      "keywords": ["worker", "background", "dispatch", "status"],
      "example_prompts": ["dispatch worker", "check worker status"]
    },
    {
      "name": "notify",
      "description": "Send cross-agent notification",
      "category": "communication",
      "keywords": ["notify", "message", "cross-agent", "broadcast"],
      "example_prompts": ["notify agents", "send notification"]
    },
    {
      "name": "init",
      "description": "Initialize hooks in project with settings configuration",
      "category": "management",
      "keywords": ["init", "initialize", "project", "settings"],
      "example_prompts": ["init hooks", "setup hooks in project"]
    },
    {
      "name": "model-route",
      "description": "Route task to optimal Claude model (haiku/sonnet/opus)",
      "category": "routing",
      "keywords": ["model", "route", "haiku", "sonnet", "opus", "cost"],
      "example_prompts": ["route to model", "choose optimal model"]
    },
    {
      "name": "model-outcome",
      "description": "Record model routing outcome for learning",
      "category": "routing",
      "keywords": ["model", "outcome", "record", "learning"],
      "example_prompts": ["record model outcome", "model success"]
    },
    {
      "name": "model-stats",
      "description": "Get model routing statistics",
      "category": "routing",
      "keywords": ["model", "stats", "statistics", "routing"],
      "example_prompts": ["model routing stats", "view model statistics"]
    },
    {
      "name": "coverage-route",
      "description": "Route based on test coverage gaps",
      "category": "testing",
      "keywords": ["coverage", "route", "gaps", "testing"],
      "example_prompts": ["route by coverage", "coverage-aware routing"]
    },
    {
      "name": "coverage-suggest",
      "description": "Suggest coverage improvements",
      "category": "testing",
      "keywords": ["coverage", "suggest", "improvements", "testing"],
      "example_prompts": ["suggest coverage", "improve test coverage"]
    },
    {
      "name": "coverage-gaps",
      "description": "List coverage gaps with priorities",
      "category": "testing",
      "keywords": ["coverage", "gaps", "priorities", "testing"],
      "example_prompts": ["list coverage gaps", "find untested code"]
    },
    {
      "name": "statusline",
      "description": "Generate dynamic statusline for Claude Code integration",
      "category": "monitoring",
      "keywords": ["statusline", "status", "dynamic", "display"],
      "example_prompts": ["get statusline", "show status"]
    }
  ],

  "workers": [
    {
      "name": "ultralearn",
      "description": "Deep knowledge acquisition and learning",
      "priority": "normal",
      "keywords": ["learn", "knowledge", "deep", "acquisition"],
      "example_prompts": ["deep learning analysis", "acquire knowledge"]
    },
    {
      "name": "optimize",
      "description": "Performance optimization and improvement",
      "priority": "high",
      "keywords": ["optimize", "performance", "improve", "speed"],
      "example_prompts": ["optimize performance", "improve speed"]
    },
    {
      "name": "consolidate",
      "description": "Memory consolidation and cleanup",
      "priority": "low",
      "keywords": ["consolidate", "memory", "cleanup", "organize"],
      "example_prompts": ["consolidate memory", "cleanup patterns"]
    },
    {
      "name": "predict",
      "description": "Predictive preloading and anticipation",
      "priority": "normal",
      "keywords": ["predict", "preload", "anticipate", "forecast"],
      "example_prompts": ["predict needs", "preload resources"]
    },
    {
      "name": "audit",
      "description": "Security analysis and vulnerability scanning",
      "priority": "critical",
      "keywords": ["audit", "security", "vulnerability", "scan"],
      "example_prompts": ["security audit", "scan vulnerabilities"]
    },
    {
      "name": "map",
      "description": "Codebase mapping and structure analysis",
      "priority": "normal",
      "keywords": ["map", "codebase", "structure", "analyze"],
      "example_prompts": ["map codebase", "analyze structure"]
    },
    {
      "name": "preload",
      "description": "Resource preloading for performance",
      "priority": "low",
      "keywords": ["preload", "resources", "cache", "performance"],
      "example_prompts": ["preload resources", "cache data"]
    },
    {
      "name": "deepdive",
      "description": "Deep code analysis and investigation",
      "priority": "normal",
      "keywords": ["deepdive", "analysis", "investigate", "deep"],
      "example_prompts": ["deep code analysis", "investigate thoroughly"]
    },
    {
      "name": "document",
      "description": "Auto-documentation generation",
      "priority": "normal",
      "keywords": ["document", "documentation", "generate", "auto"],
      "example_prompts": ["generate documentation", "auto-document"]
    },
    {
      "name": "refactor",
      "description": "Refactoring suggestions and improvements",
      "priority": "normal",
      "keywords": ["refactor", "improve", "clean", "suggestions"],
      "example_prompts": ["suggest refactoring", "improve code"]
    },
    {
      "name": "benchmark",
      "description": "Performance benchmarking and measurement",
      "priority": "normal",
      "keywords": ["benchmark", "performance", "measure", "compare"],
      "example_prompts": ["run benchmarks", "measure performance"]
    },
    {
      "name": "testgaps",
      "description": "Test coverage analysis and gap detection",
      "priority": "normal",
      "keywords": ["testgaps", "coverage", "testing", "gaps"],
      "example_prompts": ["find test gaps", "analyze coverage"]
    }
  ],

  "skills": [
    {
      "name": "swarm-orchestration",
      "description": "Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination",
      "category": "orchestration",
      "keywords": ["swarm", "orchestration", "multi-agent", "parallel", "coordination"],
      "example_prompts": ["orchestrate swarm", "coordinate agents", "parallel execution"]
    },
    {
      "name": "sparc-methodology",
      "description": "SPARC development methodology with multi-agent orchestration for Specification, Pseudocode, Architecture, Refinement, Completion",
      "category": "development",
      "keywords": ["SPARC", "methodology", "specification", "architecture", "development"],
      "example_prompts": ["use SPARC methodology", "SPARC development workflow"]
    },
    {
      "name": "hooks-automation",
      "description": "Automated coordination and learning from Claude Code operations using intelligent hooks",
      "category": "automation",
      "keywords": ["hooks", "automation", "learning", "coordination"],
      "example_prompts": ["automate with hooks", "setup automation"]
    },
    {
      "name": "agentdb-vector-search",
      "description": "Implement semantic vector search with AgentDB for intelligent document retrieval (150x-12,500x faster)",
      "category": "search",
      "keywords": ["vector", "search", "semantic", "AgentDB", "retrieval"],
      "example_prompts": ["semantic search", "vector search implementation"]
    },
    {
      "name": "agentdb-optimization",
      "description": "Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing",
      "category": "optimization",
      "keywords": ["optimization", "AgentDB", "quantization", "HNSW", "performance"],
      "example_prompts": ["optimize AgentDB", "reduce memory usage"]
    },
    {
      "name": "agentdb-memory-patterns",
      "description": "Implement persistent memory patterns for AI agents using AgentDB",
      "category": "memory",
      "keywords": ["memory", "patterns", "persistent", "AgentDB", "storage"],
      "example_prompts": ["implement memory patterns", "persistent agent memory"]
    },
    {
      "name": "agentdb-learning",
      "description": "Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms",
      "category": "learning",
      "keywords": ["learning", "training", "reinforcement", "AgentDB", "algorithms"],
      "example_prompts": ["train learning plugin", "reinforcement learning"]
    },
    {
      "name": "agentdb-advanced",
      "description": "Master advanced AgentDB features including QUIC sync, multi-database, custom metrics",
      "category": "advanced",
      "keywords": ["advanced", "AgentDB", "QUIC", "multi-database", "distributed"],
      "example_prompts": ["advanced AgentDB", "distributed systems"]
    },
    {
      "name": "github-code-review",
      "description": "Comprehensive GitHub code review with AI-powered swarm coordination",
      "category": "github",
      "keywords": ["github", "code-review", "AI", "swarm", "PR"],
      "example_prompts": ["code review", "review pull request"]
    },
    {
      "name": "github-multi-repo",
      "description": "Multi-repository coordination, synchronization, and architecture management",
      "category": "github",
      "keywords": ["multi-repo", "coordination", "sync", "architecture"],
      "example_prompts": ["coordinate repos", "multi-repo management"]
    },
    {
      "name": "github-project-management",
      "description": "Comprehensive GitHub project management with swarm-coordinated issue tracking",
      "category": "github",
      "keywords": ["project", "management", "issues", "tracking", "sprint"],
      "example_prompts": ["manage project", "track issues"]
    },
    {
      "name": "github-release-management",
      "description": "Comprehensive GitHub release orchestration with AI swarm coordination",
      "category": "github",
      "keywords": ["release", "management", "versioning", "deployment"],
      "example_prompts": ["manage release", "create version"]
    },
    {
      "name": "github-workflow-automation",
      "description": "Advanced GitHub Actions workflow automation with AI swarm coordination",
      "category": "github",
      "keywords": ["workflow", "automation", "actions", "CI/CD"],
      "example_prompts": ["automate workflow", "setup actions"]
    },
    {
      "name": "pair-programming",
      "description": "AI-assisted pair programming with multiple modes (driver/navigator/switch), TDD support",
      "category": "development",
      "keywords": ["pair", "programming", "TDD", "collaboration", "driver", "navigator"],
      "example_prompts": ["pair programming", "collaborative coding"]
    },
    {
      "name": "verification-quality",
      "description": "Comprehensive truth scoring, code quality verification with 0.95 accuracy threshold",
      "category": "quality",
      "keywords": ["verification", "quality", "truth-score", "accuracy"],
      "example_prompts": ["verify quality", "truth scoring"]
    },
    {
      "name": "reasoningbank-intelligence",
      "description": "Implement adaptive learning with ReasoningBank for pattern recognition and optimization",
      "category": "learning",
      "keywords": ["ReasoningBank", "learning", "patterns", "optimization"],
      "example_prompts": ["adaptive learning", "pattern recognition"]
    },
    {
      "name": "reasoningbank-agentdb",
      "description": "Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database",
      "category": "learning",
      "keywords": ["ReasoningBank", "AgentDB", "vector", "learning"],
      "example_prompts": ["ReasoningBank with AgentDB", "fast learning"]
    },
    {
      "name": "swarm-advanced",
      "description": "Advanced swarm orchestration patterns for research, development, testing workflows",
      "category": "orchestration",
      "keywords": ["swarm", "advanced", "patterns", "research", "testing"],
      "example_prompts": ["advanced swarm", "complex orchestration"]
    },
    {
      "name": "stream-chain",
      "description": "Stream-JSON chaining for multi-agent pipelines and data transformation",
      "category": "data",
      "keywords": ["stream", "chain", "pipeline", "transformation"],
      "example_prompts": ["stream pipeline", "data transformation"]
    },
    {
      "name": "skill-builder",
      "description": "Create new Claude Code Skills with proper YAML frontmatter and structure",
      "category": "development",
      "keywords": ["skill", "builder", "create", "YAML", "template"],
      "example_prompts": ["create skill", "build new skill"]
    },
    {
      "name": "v3-core-implementation",
      "description": "Core module implementation for claude-flow v3 with DDD and clean architecture",
      "category": "v3",
      "keywords": ["v3", "core", "DDD", "architecture"],
      "example_prompts": ["v3 implementation", "core modules"]
    },
    {
      "name": "v3-ddd-architecture",
      "description": "Domain-Driven Design architecture for claude-flow v3",
      "category": "v3",
      "keywords": ["DDD", "architecture", "v3", "domain"],
      "example_prompts": ["DDD architecture", "domain design"]
    },
    {
      "name": "v3-memory-unification",
      "description": "Unify 6+ memory systems into AgentDB with HNSW for 150x-12,500x improvements",
      "category": "v3",
      "keywords": ["memory", "unification", "AgentDB", "HNSW"],
      "example_prompts": ["unify memory", "memory optimization"]
    },
    {
      "name": "v3-security-overhaul",
      "description": "Complete security architecture overhaul for claude-flow v3",
      "category": "v3",
      "keywords": ["security", "overhaul", "v3", "CVE"],
      "example_prompts": ["security overhaul", "fix vulnerabilities"]
    },
    {
      "name": "v3-performance-optimization",
      "description": "Achieve V3 performance targets: 2.49x-7.47x Flash Attention, 150x-12,500x search",
      "category": "v3",
      "keywords": ["performance", "optimization", "v3", "benchmark"],
      "example_prompts": ["optimize v3", "performance targets"]
    },
    {
      "name": "v3-swarm-coordination",
      "description": "15-agent hierarchical mesh coordination for v3 implementation",
      "category": "v3",
      "keywords": ["swarm", "coordination", "v3", "hierarchical"],
      "example_prompts": ["v3 swarm", "coordinate v3 agents"]
    },
    {
      "name": "v3-mcp-optimization",
      "description": "MCP server optimization for sub-100ms response times",
      "category": "v3",
      "keywords": ["MCP", "optimization", "v3", "performance"],
      "example_prompts": ["optimize MCP", "fast MCP responses"]
    },
    {
      "name": "v3-integration-deep",
      "description": "Deep agentic-flow integration eliminating 10,000+ duplicate lines",
      "category": "v3",
      "keywords": ["integration", "agentic-flow", "v3", "refactor"],
      "example_prompts": ["deep integration", "eliminate duplicates"]
    },
    {
      "name": "v3-cli-modernization",
      "description": "CLI modernization and hooks system enhancement for claude-flow v3",
      "category": "v3",
      "keywords": ["CLI", "modernization", "v3", "hooks"],
      "example_prompts": ["modernize CLI", "enhance hooks"]
    }
  ],

  "topologies": [
    {
      "name": "hierarchical",
      "description": "Queen controls workers directly - anti-drift for small teams (6-8 agents)",
      "keywords": ["hierarchical", "queen", "coordinator", "anti-drift"],
      "use_cases": ["small teams", "tight control", "sequential workflows"]
    },
    {
      "name": "hierarchical-mesh",
      "description": "V3 queen + peer communication - recommended for 10+ agents",
      "keywords": ["hierarchical-mesh", "hybrid", "peer", "scalable"],
      "use_cases": ["large teams", "flexible coordination", "v3 implementation"]
    },
    {
      "name": "mesh",
      "description": "Fully connected peer network for distributed collaboration",
      "keywords": ["mesh", "peer-to-peer", "distributed", "flexible"],
      "use_cases": ["research", "brainstorming", "collaborative analysis"]
    },
    {
      "name": "ring",
      "description": "Circular communication pattern for pipeline processing",
      "keywords": ["ring", "circular", "pipeline", "sequential"],
      "use_cases": ["data pipelines", "staged processing", "workflows"]
    },
    {
      "name": "star",
      "description": "Central coordinator with spokes for testing and validation",
      "keywords": ["star", "central", "coordinator", "testing"],
      "use_cases": ["testing", "validation", "quality assurance"]
    },
    {
      "name": "hybrid",
      "description": "Dynamic topology switching based on workload",
      "keywords": ["hybrid", "dynamic", "adaptive", "switching"],
      "use_cases": ["varying workloads", "dynamic requirements"]
    }
  ],

  "consensus_algorithms": [
    {
      "name": "byzantine",
      "description": "BFT consensus tolerating f < n/3 faulty nodes",
      "keywords": ["byzantine", "BFT", "fault-tolerant", "distributed"]
    },
    {
      "name": "raft",
      "description": "Leader-based consensus tolerating f < n/2 failures",
      "keywords": ["raft", "leader", "election", "replication"]
    },
    {
      "name": "gossip",
      "description": "Epidemic protocol for eventual consistency",
      "keywords": ["gossip", "epidemic", "eventual", "consistency"]
    },
    {
      "name": "crdt",
      "description": "Conflict-free replicated data types for automatic merge",
      "keywords": ["CRDT", "conflict-free", "replicated", "merge"]
    },
    {
      "name": "quorum",
      "description": "Configurable quorum-based consensus",
      "keywords": ["quorum", "voting", "majority", "configurable"]
    }
  ],

  "intelligence_features": [
    {
      "name": "SONA",
      "description": "Self-Optimizing Neural Architecture with <0.05ms adaptation",
      "keywords": ["SONA", "neural", "self-optimizing", "adaptation"]
    },
    {
      "name": "MoE",
      "description": "Mixture of Experts for specialized routing",
      "keywords": ["MoE", "mixture", "experts", "routing"]
    },
    {
      "name": "HNSW",
      "description": "Hierarchical Navigable Small World for 150x-12,500x faster search",
      "keywords": ["HNSW", "vector", "search", "indexing"]
    },
    {
      "name": "EWC++",
      "description": "Elastic Weight Consolidation to prevent catastrophic forgetting",
      "keywords": ["EWC", "elastic", "consolidation", "forgetting"]
    },
    {
      "name": "Flash Attention",
      "description": "2.49x-7.47x attention speedup",
      "keywords": ["flash", "attention", "speedup", "performance"]
    }
  ],

  "performance_targets": {
    "flash_attention_speedup": "2.49x-7.47x",
    "hnsw_search_improvement": "150x-12,500x",
    "memory_reduction": "50-75%",
    "mcp_response_time": "<100ms",
    "cli_startup_time": "<500ms",
    "sona_adaptation_time": "<0.05ms"
  }
}