Skill UI

This advanced skill is designed to generate high-quality, publication-ready academic illustrations and diagrams. It utilizes a sophisticated, multi-stage iterative workflow involving Claude for planning, layout optimization, and strict visual review, combined with a dedicated local Codex app-server bridge for native image rendering. The process ensures that generated figures meet the rigorous aesthetic and structural standards required for top-tier conferences and journals (e.g., CVPR, NeurIPS).

This guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

This skill establishes an autonomous, iterative build loop for continuous website development. It functions as an agent that reads tasks from a 'baton' file, utilizes the Stitch MCP tools to generate structured web pages, integrates the generated HTML assets into the main site structure, and finally updates the baton file with the next required task. It supports end-to-end, programmatic site building and includes optional visual verification using Chrome DevTools.

Use the Composio CLI to automate the entire Pull Request review and Continuous Integration (CI) triage process for GitHub and GitLab. This tool fetches code diffs, analyzes failure logs, posts detailed inline review comments, and iteratively patches and re-runs CI checks until the code passes, significantly enhancing code quality and developer efficiency.

A comprehensive skill for generating new design screens from text prompts or images, and iteratively editing existing designs. It incorporates advanced prompt enhancement, design tokens, and professional UI/UX terminology to ensure professional, structured, and context-aware design output for web and mobile platforms.

The pbir CLI is a comprehensive tool for exploring, building, managing, formatting, and validating Power BI reports. It allows developers to interact with Power BI files (.pbir, .pbix, .pbip) programmatically, covering everything from structural inspection and data model querying to visual generation and publishing to Fabric. It guides users through best practices, emphasizing iterative development and explicit validation.

This skill autonomously optimizes artifacts like code or models using Hypothesis Tree Refinement. It runs iterative experiments with dev/test evaluators to prevent overfitting, coordinating subagents in isolated worktrees. Ideal for improving training recipes, agents, or pipelines without manual supervision.

A human-in-the-loop studio for iteratively tuning AI outputs or reviewing complex media/prose. It allows users to directly manipulate visual parameters (color, typography, layout) or annotate documents in real-time, moving beyond static comparison grids or vague prompts. Perfect for designers and content creators who need hands-on, visual feedback.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

This skill guides the creation of comprehensive, structured strategic documents (SSOT, life vision, principles) using a strict, iterative interview-style methodology. Instead of generating content, the AI asks one focused question at a time. The user provides the answer, and the AI patches the document incrementally, ensuring the user remains the primary author. It is ideal for deep reflection, strategic planning, and building authoritative knowledge bases.

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 utility executes an autonomous, iterative loop, simulating a comprehensive agent workflow. It systematically manages tasks by checking current status, predicting the optimal next action using specialized tools, executing the required task (e.g., coding, testing), logging the progress, and scheduling the next iteration. It supports task discovery from multiple sources, including team tasks, swarm tasks, and file checklists, ensuring continuous, automated progress towards a defined goal.