Skill UI

A comprehensive guide to ensuring robust state management for LangGraph agents. This skill addresses common checkpointing pitfalls (like missing thread_id or non-JSON serializable state) and details best practices for implementing reliable state persistence using MemorySaver or PostgresSaver. It covers thread discipline, schema migration, and time-travel debugging for production-ready applications.

A tool designed for persistent context management within AI coding sessions. Memory Kit solves the problem of context loss that occurs during compaction or session restarts by saving, loading, updating, and sharing critical session state to a dedicated `MEMORY.md` file. Key functions include creating snapshots of goals and decisions, syncing context across team members via Git, and auditing/pruning stale entries, ensuring continuous workflow and knowledge retention.

This guide provides detailed strategies for optimizing Qdrant's operational performance. It covers critical areas such as search speed (latency and throughput), efficient vector indexing strategies, and advanced memory management techniques. Use this when you need to improve the speed, scalability, and resource efficiency of your vector database deployment.

This skill guides the design and optimization of production-grade multi-agent systems. It specializes in using LangGraph, LangChain, and DeepAgents to create structured, complex AI workflows. It covers state management, agent routing, memory integration, and tool calling, making it ideal for building robust, scalable agents like supervisors, planners, and coders.

A comprehensive technical reference detailing idiomatic and efficient coding practices for the C language. It covers critical topics such as safe memory management, pointer handling, robust error checking, and modern string manipulation techniques, helping developers write reliable and performant C code.

This skill provides a hybrid, persistent, and searchable knowledge memory system specifically designed for enhancing AI agents' capabilities. It allows developers to record, retrieve, and search key knowledge, architectural decisions, and operational memories automatically. Use it to build agents with long-term context and reliable knowledge management, ensuring findings are synced back into project documentation.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

This comprehensive tool guides developers through the entire plugin ecosystem, helping them find and recommend the optimal plugins for any given workflow or task. It covers core coordination, multi-agent collaboration, advanced memory management (Vector DB, RAG), architectural design (ADR/DDD), security auditing, and modern development methodologies. Use it when starting a new project or optimizing existing development pipelines.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This operator skill provides comprehensive control over the OpenViking context engine, enabling agents to manage long-term memory, session archives, and external resources within OpenClaw. It facilitates semantic recall, context querying, resource import, and recall trace debugging by communicating with a remote OpenViking server. Use this plugin when your agent requires advanced, persistent context management.

This MCP server provides a persistent and searchable memory bank for AI agents. It enables reading, writing, and searching long-term knowledge regarding architecture, patterns, and decisions. It automatically syncs with project documentation and includes a standalone dashboard for visualization. Requires Node.js setup for local deployment.

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Ensures graceful degradation and clear failure modes. Capable of agent architecture design, tool calling, memory management, and complex task decomposition. Includes knowledge of ReAct loops, Plan-and-Execute patterns, and safety guardrails.