{ "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" } }