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.

Analyze product pages, Google Shopping visibility, Amazon marketplace data, schema markup, and competitor pricing. Useful for optimizing ecommerce listings and finding marketplace keyword gaps.

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.

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.

Generates a highly structured, machine-parseable shooting script for short videos based on a specified topic, style, and duration. It outputs a detailed shot list including image prompts, video prompts, voiceovers, and on-screen text, making it ideal for automated AI video generation workflows.

This meta-skill automates the entire process of creating a professional, high-quality AI short drama video from a simple topic or prompt. It intelligently drafts a shot-by-shot shooting script, maintains character and scene consistency across all shots, generates comprehensive visual assets (identity reference, per-shot compositions), and compiles the final MP4 with burned subtitles and title cards. Suitable for content creators, marketers, and storytellers needing rapid, consistent visual narratives.

This skill allows users to generate complete songs, including vocals or pure instrumental tracks, using advanced AI APIs. By providing a structured JSON specification, you can define the desired style, mood, scene, and optional lyrics. Ideal for creating background music, theme songs, jingles, or full compositions for creative projects.

A powerful skill for generating high-quality single images from text prompts (Text-to-Image) or modifying existing images (Image-to-Image). It utilizes OpenRouter, supporting various aspect ratios and resolutions, making it ideal for concept art, product rendering, or any single visual asset creation. The tool handles sophisticated parameters like model choice and failure fallbacks.

Generates high-quality, short video clips (3-15s) using the Seedance 2.0 model. This tool accepts structured English prompts, an optional first-frame image, and an optional identity/style reference image for continuity. It supports dual backends (OpenRouter, Volcengine, BytePlus) to maximize accessibility and reliability for cinematic shot generation.

A generic entry point for delegating self-contained, multi-step tasks to specialized LLM agents (Codex, Claude Code, Pi). Utilizes background processing tools to manage the full lifecycle of AI-driven sub-tasks, such as policy review, complex report synthesis, document generation, or large-scale code refactoring. Ideal for meta-skill workflows requiring full agent agency.

This tool converts local source audio recordings into a specified, authorized target voice. It manages the complex workflow, including checking source and target voice rights, handling linguistic specifics (locale, accent), and performing previews before generating final assets. It is essential for professional narration, podcasting, and content localization while adhering to strict copyright and identity policies.

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.