Skill UI

Hierarchical Agent Memory (HAM) scopes Claude Code agent context by generating per-directory CLAUDE.md files, tracking token savings, and surfacing analytics via a dashboard so multi-folder projects avoid repeated full scans.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

A comprehensive guide and workflow demonstrating how to use YARA rules for proactive threat hunting. This method helps detect unknown malware, suspicious patterns, and Indicators of Compromise (IOCs) across filesystems, memory dumps, and network artifacts, making it essential for incident response and security analysis.

This skill allows agents to ingest external resources (local files, URLs, repositories) using `ov add-resource`, and to create long-lasting searchable memories from conversations using `ov add-memory`. It is essential for building a comprehensive, persistent knowledge base for the OpenViking context database, ensuring that learned insights and external data remain available across sessions and interactions. Use when external knowledge injection or context persistence is required.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

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 skill establishes rules for AI agents to manage long-term memory effectively. It specifies when to recall past decisions before acting and when to save new decisions or corrections after tasks. It ensures consistency across sessions by preventing agents from forgetting preferences or repeating mistakes, working with any memory backend like files or MCP servers.

Reduces token costs by scoping context into directory-level CLAUDE.md files. Sets up hierarchical memory, tracks savings via dashboard, routes agents to sub-contexts, and provides audit and insights commands.