en un clic
agentic-flow
agentic-flow contient 20 skills collectées depuis ruvnet, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
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