Skill UI

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 guide maps user questions and topics (e.g., configuration, memory, skills, CLI) to specific, relevant documentation paths and source code entry points within the QwenPaw ecosystem. It is designed for built-in QA Agents to eliminate aimless searching, ensuring the agent reads the most accurate files to provide comprehensive and precise answers about the system's features.

Vaex is a high-performance Python library designed for processing and analyzing large tabular datasets that exceed available RAM. It supports out-of-core DataFrame operations, lazy evaluation, fast aggregations, and efficient visualization of big data. Ideal for working with massive CSV, HDF5, Arrow, or Parquet files, performing statistics on billions of rows, and building machine learning pipelines without memory constraints.

This skill converts complex technical descriptions, such as system architectures, data flow diagrams, flowcharts, and sequence diagrams, into professional, production-quality SVG diagrams. It supports various technical models, including Agent and Memory architectures, and exports the final output as high-resolution PNG files for seamless documentation and presentation use.

A comprehensive playbook detailing advanced techniques for post-exploitation security bypass on Linux systems. Covers escaping restricted shells (rbash), evading noexec/read-only filesystems using in-memory execution (DDexec, memfd_create), and circumventing mandatory access controls like AppArmor and SELinux. Essential for penetration testing and red team operations.

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

Guides users through ECC's agents, skills, commands, hooks, rules, install profiles, and project onboarding, answering from current repository files rather than memory.

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 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.

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 specialized skill is designed for Digital Forensics and Incident Response (DFIR) scenarios. It correlates evidence from multiple disparate sources—including EVTX, PCAP, registry, memory, and filesystem artifacts—to build a comprehensive, ordered, and replayable forensic timeline. Use it when the goal is to map the full sequence of attacker actions, establishing initial access, lateral movement, and persistence chains based on cross-source evidence correlation.