Skill UI

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

This skill guides users through the installation and configuration of the OpenViking long-term memory plugin for OpenClaw. It enables the system to automatically capture crucial facts from conversations and recall relevant context across sessions (auto-capture + auto-recall). It is designed for users who do not know CLI commands, making advanced, knowledge-based chat capabilities accessible via natural conversation.

This tool functions as a Command Line Interface (CLI) alias for implementing the specific 'pua:mama' protocol within advanced coding models like Codex or Claude Code. It loads and enforces a structured 'mama skill/protocol,' maintaining rigorous verification standards while adjusting the visible narrative style to a specific 'Chinese-mom nagging mode.' It manages configuration by interacting with `~/.pua/config.json`, ensuring existing user fields are preserved.

This skill searches GitHub and the web to discover the latest Japanese Natural Language Processing (NLP) resources, tools, and libraries (e.g., NER, LLM, TTS) that are not yet included in the awesome-japanese-nlp-resources list. It is designed for developers and researchers who want to track the cutting edge of Japanese language technology or prepare a contribution.

This skill generates a comprehensive issue report on the current challenges, limitations, and open problems within Japanese Natural Language Processing (NLP) for a specified topic. By combining knowledge from a curated local dataset with real-time web search, it provides a deep dive into ongoing research gaps, evaluating the state-of-the-art and recommending areas for future work.

This skill automates the critical step of enriching scaffolded research paper summaries within a dedicated wiki. It reads template pages and gathers content from multiple external academic sources (such as arXiv and DeepXiv). It then populates placeholder "TODO" sections (e.g., Method, Key Results, Open Questions) with coherent, 1-3 sentence prose, making it essential for maintaining a comprehensive, LLM-driven knowledge base.