hive-mind-advanced
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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...
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...
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
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...
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
Guide for authoring agent skills. Use when creating new skills, writing SKILL.md files, or improving existing skills.
Techniques for writing effective LLM prompts. Use when crafting system prompts, optimizing agent instructions, or improving model outputs.
Create new Claude Code skills with proper structure, YAML frontmatter, and best practices
Generate execution plans from user queries through intake, research, specification, and validation. Use when user provides any query (vague to specific) and needs an executable plan for /execute skill.
Auto-detects next pending Smart Orchestrator component, spawns 2-3 research agents, generates evidence-backed specs, validates, and offers optional implementation with automatic progress tracking.
Use when creating a new LangGraph graph. Start with a minimal graph and iterate.
Advanced LangGraph patterns for production applications. Use when implementing streaming (tokens, updates, custom data), parallel node execution, subgraphs for modular workflows, error handling and retries, or...
Core concepts for building LangGraph applications. Use when creating StateGraph workflows, defining nodes and edges, managing state with TypedDict/Pydantic, or understanding basic graph structure. Covers state...
Multi-agent architectures with LangGraph. Use when building systems with multiple collaborating agents, implementing supervisor or swarm patterns, creating hierarchical agent workflows, or designing agent hando...
Persistence and human-in-the-loop patterns for LangGraph. Use when implementing checkpointing, saving/restoring graph state, adding human approval workflows, managing conversation memory across sessions, enabli...
Use when integrating multiple LLM providers. Keep provider-specific code isolated behind a small interface.
Use for LangGraph agent design and refactors. Prefer explicit state, small nodes, and clear transitions.
Use to review OpenAI API integrations for correctness (endpoints, params, auth, response handling) and robustness.
This skill should be used when the user initiates autonomous development with /auto, /auto-lite, or similar commands to classify the type of work. Semantic work type classification with multi-layer detection. A...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。