Skill UI

This skill guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

This file serves as the localization resource for the Liquid Glass Design skill, specifically targeting Japanese translation. Please note that this file is currently under translation development. For the complete functionality and core documentation, refer to the original skill file.

This skill guides the design and rigorous review of goal-oriented feedback loops for autonomous agents. It helps users define machine-decidable exit criteria, select the appropriate loop type (e.g., servo, regulator), and test the workflow against common failure modes like spinning, goal-gaming, or running past the correct answer. Use it when building complex, iterative, or self-correcting agent workflows.

This skill is designed to enhance the overall feel and usability of user interfaces. Currently, this file is undergoing Japanese translation. Please refer to the original English skill file for detailed functionality and usage scenarios.

A comprehensive skill designed to fetch raw market data (Open, High, Low, Close, Volume) for a given symbol. It normalizes the time series data into relative OHLCV vectors, converts these vectors into 64-dimension embeddings, and stores them in a memory store. Crucially, it indexes the resulting vectors using the HNSW algorithm, enabling fast and precise nearest-neighbor search for pattern detection and similarity analysis in financial time series.

This skill is designed to streamline the process of market research. It helps users gather comprehensive data on target markets, analyze industry trends, and identify key consumer insights. Use it to inform product development, refine business strategies, and make data-driven decisions.

This file is designated as the Japanese localization resource for the Motion UI component. It is currently under active translation and implementation. Users should refer to the original skill file for the English version or the final functional skill.

This skill provides robust automation capabilities for interacting with network devices via SSH using the industry-standard Netmiko library. It is designed to simplify complex network operations, such as configuration backups, status checks, and bulk management across heterogeneous devices, significantly improving operational efficiency.

This comprehensive skill guides the user through the entire process of creating a deep, multi-layered AI persona (Soul.md). It helps define the identity, core rules, name, and visual prompts for agents designed for the OpenClaw platform. Users can initiate character creation via guided prompts or a random 'gacha' draw, ensuring a cohesive and compelling character profile.

This skill orchestrates complex, behavior-preserving code refactoring. It is designed to improve code structure—such as extracting modules, eliminating duplication, or reducing nesting—without changing the core functionality. The process follows a rigorous, multi-phase gate system, ensuring existing tests remain green throughout the entire development lifecycle, culminating in a behavior-neutral commit.

This skill is designed for orchestrating complex, multi-step processes. It helps manage workflows, coordinate different components, and ensure systematic execution of tasks from planning to completion. Ideal for complex automation scenarios and structured project management.

This skill runs a single, simulated tick of a defined business pod (Sales). It is designed for dry-run testing, allowing users to validate inputs, resolve agents, and reserve budgeted resources without invoking real LLM workers. It handles template loading, schema validation, budget management, and emits structured status reports for subsequent workflow consumption.