Skill UI

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

This tool simplifies the process of detecting structural drift by combining audit listing, fresh auditing, and trend diffing into a single command. It finds the most recent audit record, runs a current audit against the repository, and compares the two using structural similarity metrics. It alerts when the structural distance falls below a specified threshold, helping maintain code consistency over time.

This skill generates an enterprise-grade threat model report using the `harness threat-model` command. It categorizes threats across the MCP surface, providing a detailed report including the overall worst severity (clean, low, medium, high) and specific findings. Ideal for pre-launch security reviews and periodic audits when sharing findings with an InfoSec team.

Hookify rules enable developers to define custom, event-driven workflows using Markdown files with YAML frontmatter. These rules monitor specific operational events (such as bash commands, file edits, or user prompts) and can trigger warnings or block operations when predefined regex patterns are matched. They are essential for enforcing coding standards, preventing dangerous operations, and guiding development adherence in complex software environments.

This skill helps transform vague product or engineering ideas into structured, verifiable acceptance criteria (AC). It is crucial for high-impact changes involving security, data migration, or external APIs. By inspecting existing context and asking minimal, targeted questions, it produces clear, observable criteria, significantly de-risking the development process and ensuring handoff readiness.

A comprehensive guide detailing best practices for writing clean, maintainable, and type-safe Java code, specifically tailored for Spring Boot services. It covers naming conventions, prioritizing immutability, proper use of Optional, stream API best practices, and modern project structuring.

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.

Manim is a powerful Python library used for creating high-quality mathematical and scientific animations. This skill facilitates the generation of educational videos and complex visualizations for presentations. It is ideal for researchers, educators, or content creators who need to visualize complex concepts in an engaging animated format. Note: This specific file is related to localization/translation.

Collects distributed trace spans for a specific task ID, building a comprehensive, visual trace tree. Users can analyze the timing of individual spans, identify performance bottlenecks, understand agent coordination, and synthesize a narrative summary of the task's execution flow. Ideal for debugging complex multi-agent systems and improving system observability.

This advanced orchestration tool streamlines the process of building a Minimum Viable Product (MVP). By ingesting high-level design documents (SDD/PRD), it automatically plans thin, end-to-end vertical slices. The system then scaffolds, executes TDD implementation, and manages gated commits, transforming theoretical specifications into a functional, reviewable starting point, dramatically accelerating initial development cycles.

This tool is designed to manage the process of altering an existing, functional feature to meet a new desired specification. It ensures a structured workflow: first, update the existing test cases to reflect the new behavior; second, modify the underlying implementation to pass these updated tests; finally, complete through review and gated commit. Use this when the feature works but its behavior needs adjustment.

This engine serves as a shared, comprehensive orchestration layer for the entire development lifecycle (orch-* skills). It defines a rigorous, gated pipeline—including Research, Plan, TDD Implementation, Review, and Commit—ensuring consistency regardless of the specific operation (e.g., feature addition, defect fixing). It also includes a size classifier to accurately scope the required effort and gates for mandatory human review.