Skill UI

This meta-skill orchestrates the entire academic writing workflow, from initial requirement extraction (topic, length, audience) to drafting, citation management, and final LaTeX/PDF compilation. It supports various modes, including compact skeletons for quick drafts and full manuscripts for publication-ready work. Ideal for users needing structured help with research papers or academic manuscripts.

This tool acts as a pre-drafting planning mechanism for academic or technical writing. It analyzes the user's request to extract and structure detailed preferences, such as target audience, citation style, depth, and desired emphasis. It determines the optimal generation mode (direct or preference-driven) before any writing or research takes place, ensuring all required criteria are met systematically.

This utility reads structured JSON data from multiple search engines (multi-search-engine JSON) provided via standard input. It processes this academic data and converts it into a minimal, standardized BibTeX format, primarily generating @misc{} entries. This feature is ideal for automating the creation of a bibliography or reference list for scholarly papers, enhancing research workflow efficiency.

Drafts a complete, publication-ready research paper section (e.g., Introduction, Method, Results) as a robust LaTeX fragment. This tool ensures the section adheres strictly to a predefined writing plan, maintaining narrative coherence, managing citation budgets, and slotting seamlessly into a larger academic manuscript.

This specialized tool is designed to curate and structure academic source material for long-form research papers. It takes paper topics, user preferences, search results, and bibliography entries as input. The output is a highly structured source pack, clearly identifying primary, supporting, and excluded sources, along with coverage notes to ensure research depth and methodological rigor.

This guide provides comprehensive instructions for searching Tabular Editor (TE) documentation and understanding complex configuration files. It details the usage of the `pbi-search` CLI tool, allowing users to query multiple sources (DAX, Power BI, SQLBI) simultaneously. It also covers the structure and best practices for model-specific files like `.tmuo` and application preferences, ensuring efficient data modeling and development.

This skill implements Autonomous Optimization (AO) using Hypothesis Tree Refinement (HTR). It iteratively improves concrete artifacts (like code, models, or data pipelines) by running structured, long-term experiments against a measurable objective. It maintains a persistent hypothesis tree, coordinating subagents to test hypotheses in isolation while using a held-out test evaluator to prevent overfitting and ensure reliable, cumulative research progress. Ideal for complex benchmark tuning, agent refinement, or optimizing entire workflows.

A protocol for token-efficient, accurate, and structured web research. It guides the model to fetch minimum data when accessing URLs, GitHub repositories, or performing web searches, preventing token waste.

This skill facilitates structured, bidirectional reading and knowledge synthesis for academic texts, theories, and articles. It forces users to first capture the source material's original intent, before critically mapping its relationships, conflicts, and absorptions against the CrossFrame framework. It ensures structured output with clear source boundaries, making it ideal for deep learning and scholarly work.

This skill optimizes articles for Answer Engine Optimization (AEO), ensuring content is structured for maximum extractability by AI models like ChatGPT and Perplexity. It rewrites vague headings into direct questions, inserts self-contained 50-80 word answer capsules, audits and trims long paragraphs, and highlights trust signals, making your content highly readable and citable by AI search engines.

This skill transforms vague, unclear, or half-formed business opportunities and brief descriptions into highly structured, actionable problem statements. It guides the user through a three-stage process: reframing the core questions, rigorously defining in-scope and out-of-scope boundaries, and designing a minimum viable research plan. It ensures that ambiguous inputs are converted into clear, executable steps, allowing the user to respond with confidence rather than asking for more clarification meetings.

This skill generates a comprehensive discovery call brief designed for sales professionals. Users input prospect details and product info, and the AI outputs a structured guide including market research summaries, hypotheses about pain points, deep-dive questioning frameworks, and clear success criteria. It ensures every sales conversation is strategic, context-driven, and leads to a defined next step.