Skill UI

Sanitize user prompts through Google Model Armor templates via the gws CLI command, ensuring outbound safety and supporting stdin, text, or explicit JSON inputs for prompt filtering.

Sanitize Google Model Armor responses via CLI templates to ensure outbound safety before delivering model output to end users.

Creates high-fidelity visuals through structured JSON prompts and a Python generator script, handling subjects, styles, references, and technical specs for characters, scenes, or products.

Switches the AI persona into a Chinese-style mama nagging narrator that sticks to core behavior guardrails while delivering increasingly intense motivational scripts; useful when you need a relentless reminder that you must research, verify, and finish tasks before asking for help.

Provides OpenClaw agents with local persistent memory via MemOS, managing installation, configuration, upgrades, and a memory viewer for secure offline workflows.

Describes how to use the MemOS local memory toolkit to recall past conversations, search shared histories, and choose the right memory tool when automatic recall is insufficient.

This guide details the OpenViking long-term memory plugin, enabling advanced conversational context retention. The system automatically captures significant facts (via semantic or keyword modes) and proactively recalls relevant memories before responding. It provides tools for searching, manually storing, and deleting memories, significantly enhancing the AI's ability to maintain continuity and personalization across long interactions.

This skill allows agents to persistently add external knowledge and conversational context to the OpenViking context database. It supports ingesting various resources (local files, URLs, repositories, directories) using 'add-resource', and storing rich, multi-turn conversation history as long-term memory using 'add-memory'. Use this when building a knowledge base, documenting projects, or ensuring conversation insights persist across sessions.

Transforms user articles, reports, or documentation into natural two-host conversational podcasts by building structured JSON scripts and running the bundled TTS pipeline to emit MP3 audio plus transcripts.

This skill creates high-quality videos from structured JSON prompts and optional reference images, then runs the prescribed Python script to produce MP4 output tailored to the requested scene, style, and technical specs.

Programmatic access to Google NotebookLM via CLI/SDK: create notebooks, add diverse sources, chat about content, generate artifacts, download outputs, and embed into automated workflows.

This skill manages the schema for human feedback (annotation configs) within the Arize platform. It allows users to define label types (categorical, continuous, freeform) and programmatically apply these annotations to project spans using the Python SDK. Essential for establishing standardized human review processes and improving model evaluation.