com um clique
agentic-flow
agentic-flow contém 20 skills coletadas de ruvnet, com cobertura ocupacional por repositório e páginas de detalhe dentro do site.
Skills neste repositório
CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.
Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
Complete security architecture overhaul for claude-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation.
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
Quick start guide for agentic-flow - initialize, configure, and run
Configure Claude Code hooks for automated coordination and learning
Persistent memory patterns for cross-session learning and context retention
SPARC development methodology for systematic test-driven development
Orchestrate multi-agent swarms for complex parallel task execution
Run comprehensive worker system benchmarks and performance analysis
Worker-Agent integration for intelligent task dispatch and performance tracking
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms