Skill UI

This memory bridge ingests Claude Code's local auto-memory markdown files and structures them into AgentDB. It generates high-fidelity ONNX embeddings, handles deduplication using cosine similarity, and indexes the memories in a scalable namespace. This enables seamless, unified semantic search across all historical sessions and projects, significantly enhancing RAG capabilities.

A state-of-the-art memory search engine providing multi-faceted retrieval strategies. It supports default dense semantic matching, advanced hybrid search (sparse + dense with RRF), deep multi-hop Graph RAG for complex reasoning, and 'Smart' search utilizing query expansion, recency boosting, and MMR diversity reranking. Ideal for retrieving nuanced, diverse, and time-sensitive information from large, complex knowledge bases across multiple namespaces.

Uses the Monitor tool to stream real-time events from a swarm system instead of inefficient polling. It provides live observability for key actions like agent spawns, task completions, memory writes, and health status. This is ideal for continuous monitoring and debugging of complex, distributed processes.

This tool synthesizes multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

This utility diagnoses and automatically repairs common issues within the Ruflo installation environment. It performs comprehensive health checks on core dependencies like Node.js, npm, and git, while also validating configurations, daemon status, memory databases, and API keys. It provides targeted commands to resolve issues related to memory initialization, daemon startup, and configuration resets, ensuring a stable operational environment.

This tool manages RVF (Ruflo Vector Format) files, providing comprehensive functions for handling, transferring, and backing up agent memories. It supports listing, exporting, importing, migrating, and generating statistics for complex vector embeddings and associated metadata, ensuring knowledge portability across different projects and platforms.

This toolset allows users to save and restore complete agent sessions, ensuring context continuity across multiple conversations. It captures the agent's entire state, including memory, configuration, task progress, and learning metrics. Use it when pausing work and needing to resume later with full context, or for checkpointing long-running tasks.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This plugin provides advanced vector search capabilities using two distinct paths. The large-scale path supports millions of vectors via HNSW, ideal for general corpus search. The hot-path router handles small, critical pattern routing (≤11 patterns) with sub-ms latency. Key features include RaBitQ 1-bit quantization for memory efficiency (32× reduction), hyperbolic space search for hierarchical data, and unified cross-namespace search.

This tool performs a comprehensive audit of the project's stored auto-memory. It analyzes learned knowledge to identify actionable patterns for promotion to core rules, flag stale or outdated entries, and suggest opportunities for consolidating redundant knowledge. Use it after major development phases or when the memory knowledge base becomes crowded to maintain system knowledge quality.

This tool provides a comprehensive overview of your project's memory usage across all associated memory systems. It reports metrics such as line counts, topic file counts, capacity limits, stale entries, and actionable recommendations to help maintain memory integrity and optimize performance.