Skill UI

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 CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

The Ejentum Reasoning Harness is a comprehensive library of cognitive operations designed to guide complex AI agents through critical thinking tasks. It exposes four specialized harnesses (reasoning, code, anti-deception, memory) that mitigate common failure modes like hallucination and reasoning decay. Instead of a fixed answer, the agent ingests a detailed, executable scaffold—including failure patterns, procedures, and falsification tests—to ensure robust, self-verified output.

This command runs a high-stakes business memo through multiple advanced AI models (Claude, Codex, Gemini) to achieve a robust consensus review. It identifies areas of agreement, divergence, and critical concerns, providing an independent sanity check before irreversible corporate decisions like M&A, fundraises, or major pivots. It includes graceful degradation for API availability.

A specialist tool designed to pressure-test AI Management Systems (AIMS) against the ISO 42001 standard. It executes six critical compliance checks, covering scope definition, risk mapping, policy adherence, and framework integration. Essential for pre-certification readiness, annual internal audits, and new system onboarding.

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.

Combines a curated Japanese NLP resource dataset with live web research to produce an up-to-date trend report on models, tooling, benchmarks, embeddings, speech, and related subfields.

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 acts as a comprehensive hybrid router for any research request. It first deterministically classifies the user's intent, delegating the query to specialized modules like Pulse (trends/sentiment), Grants (NIH funding), or LitReview (academic papers). If no specialist matches, it falls back to a robust general search and synthesis workflow. The system ensures routing transparency, provides comprehensive reports, and includes full citations and an audit log.

This skill transforms a course syllabus (PDF, DOCX, etc.) into a highly curated, supplementary academic reading list. It parses topics and learning outcomes, calibrates content difficulty based on the target audience (undergraduate, graduate, etc.), and searches academic databases (Consensus) for recent, peer-reviewed papers. The output is a professional .docx file containing summaries, links, and advanced discussion questions, making it invaluable for educators and students needing structured, up-to-date course resources.

A specialized skill for optimizing content to be cited as authoritative sources by major AI language models (ChatGPT, Perplexity, Claude, Gemini). Unlike traditional SEO, AEO focuses on E-E-A-T signals, factual density, and structured data to ensure LLM integration. Features include content auditing, E-E-A-T scoring, structural rewriting, and citation tracking.

This tool generates an extensive, long-form cheat sheet for specialized Machine Learning or Large Language Model (LLM) topics. It provides comprehensive technical deep dives, including theoretical formulas with derivations, from-scratch PyTorch code implementations, detailed comparison tables, and 25 high-frequency interview questions spanning L1 (Must-Know) to L3 (Expert Lab) difficulty.