Skill UI

Provides deep semantic code intelligence for the Go language, utilizing the official `gopls` language server. Key features include precise go-to-definition, finding references, analyzing call graphs, performing safe refactoring (rename, extract, inline), generating diagnostics, and formatting code. It is essential for navigating, understanding dependencies, and maintaining large Go codebases.

This skill guides the safe, systematic, and at-scale refactoring of existing Go codebases. It ensures that structural changes maintain observable behavior by following a rigorous process: mapping blast radius, strengthening safety nets (tests), applying small tool-driven transforms, and committing changes atomically via staged PRs. Ideal for cleaning up large functions, resolving code smells, renaming packages, or tackling complex architectural improvements in Go.

A simulated board of legendary marketing advisors (Seth Godin, David Ogilvy, Alex Hormozi, etc.). This tool analyzes a marketing problem by providing multiple, often conflicting, expert perspectives. Its value lies in surfacing trade-offs, identifying blind spots, and forcing the user to confront real strategic dilemmas before choosing a path for execution.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

This skill converts ambiguous or general research needs into a single, highly precise, self-contained prompt. It guides the user to define the project context, the core question, and specific sub-questions, ensuring the resulting prompt is rigorous enough for deep, multi-source AI research, minimizing ambiguity and guesswork.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This function executes a full, autonomous iteration of the Claude Code workflow. It systematically checks the current status, predicts the optimal next action (e.g., editing code, running tests, spawning agents), executes the task, logs the results, and schedules the next wakeup. It discovers and processes tasks from team lists, swarm lists, and file checklists, ensuring continuous and automated progress toward a defined goal.

This methodology provides a rigorous framework for scoring competitors across nine weighted dimensions. By using explicit 1-5 rubrics and focusing intensely on the client's strategic tension, it moves beyond subjective gut-feel rankings to create defensible, comparable, and evidence-anchored scores. It is essential for developing a detailed, structured competitive report.

A rigorous, data-driven methodology for improving system performance. Use this skill when aiming to significantly increase speed, reduce latency, or lower operational cost. It establishes a measurable baseline, generates testable variants based on hypotheses, runs controlled benchmarks, and validates the best performing solution through iterative testing and code promotion gates.

This skill provides a rigorous, structured method for inspecting animated 3D characters, rigs, and motion retargeting in Blender. It moves beyond visual evidence (screenshots) by extracting and comparing structured data points such as bone transformations, bounding box changes, ground contacts, and axis conventions. Ideal for diagnosing subtle animation flaws like foot sliding, ground penetration, or incorrect orientation that are invisible in still images.