Skill UI

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

The Atlas Contract is a goal-integrity governance protocol designed for complex, multi-stage tasks such as backend development, API integration, or data persistence. It prevents 'goal drift' by enforcing critical checkpoints (Goal Contracts, Phase Checks, Final Audits). It is essential for high-risk, multi-part requests where maintaining strict alignment with the user's original objective is paramount.

This skill applies a rigorous, evidence-first discipline to coding tasks, inspired by Fable-like best practices. It guides agents through a structured workflow: inspecting the workspace, tracking goals and findings, grounding all conclusions in verifiable evidence, and executing thorough verification gates before declaring completion. Ideal for complex development, debugging, or adapting cross-model prompt guidance into robust, verifiable project rules.

This skill provides a systematic framework for diagnosing and proposing structural repairs for organizational failures. It moves beyond personal blame by analyzing complex dynamics such as team structures, project bottlenecks, authority chains, and governance gaps. It generates actionable memos and low-risk pilot plans, ensuring solutions address systemic root causes rather than individual shortcomings. Best used when CrossFrame Suite routes explicit organizational tasks.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

Implements a robust, four-stage coding discipline (Plan, Isolate, Test First, Double Review) to prevent premature code delivery. This skill forces the AI to structure complex tasks by planning, implementing in isolation, writing tests first, and performing deep self-review, significantly improving code correctness and quality.

This skill guides the planning and structuring of Figma prototypes specifically for usability testing and user research. It helps define the precise scope (in/out scope), specifies every interaction detail (trigger, elements, animations), builds comprehensive flow diagrams, and generates detailed test task scripts. It ensures the prototype is the minimum viable version needed to answer the research questions, preventing scope creep and over-engineering.

This skill conducts a 15-question interview to deeply capture your professional context, including your role, core topics, trusted sources, and preferred format. It then generates a comprehensive, reusable 'master prompt' that can be pasted into automated routines or scheduled tasks, ensuring you receive personalized, structured intelligence briefings without manual setup.

This skill utilizes the Gemini Nano Banana Pro model to generate and edit high-quality images. It is ideal for users needing to create visuals, generate product mockups, design logos, or perform complex photo editing tasks based on text prompts. Supports file-based API calls for robust operation.

A comprehensive, adaptive framework for developing and implementing features, adhering to a strict, multi-phase workflow: Specify, Design, Tasks, and Execute. This skill ensures feature quality through granular task breakdown, atomic Git commits, requirement traceability, and mandatory, independent verification. It automatically adjusts its depth based on feature complexity (Small to Complex), providing a robust mechanism for managing technical debt and capturing project lessons.

Workthreads analyzes a team's coding history across multiple repositories to generate a structured weekly status report. It clusters raw logs into 'work threads' and automatically tags their lifecycle status (new, open loops, closed, reverted). This is essential for leads needing a high-level, actionable summary of project progress, outstanding tasks, and completed features without reading thousands of lines of raw activity.

This advanced protocol forces the AI to conduct a thorough context audit before attempting any complex task. It mandates reading every relevant file, identifying architectural gaps, and providing a detailed orientation statement. It is essential for multi-file modifications, system design, and debugging to prevent acting on assumptions and maintain high code accuracy. The AI cannot proceed until all unknown variables are resolved.