github-code-review
Comprehensive GitHub code review with AI-powered swarm coordination
Explorez les skills IA publiés de bjpl et consultez leurs instructions source.
Skills indexés
28Étoiles des dépôts des skills indexés
26Forks du dépôt
0Moyenne des étoiles par skill indexé
1Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Comprehensive GitHub code review with AI-powered swarm coordination
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Fe...
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledg...
Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building d...
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems,...
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cogn...
Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges
Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill...
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intell...
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, impleme...
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination,...
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling t...
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.