Skill UI

Bilig WorkPaper provides a code-first runtime environment for complex spreadsheet business logic. It allows agents to interact with structured data using formulas and JSON workbooks via deterministic API calls (MCP/TypeScript). This is ideal for replacing unreliable UI automation in use cases like quote calculators, budget modeling, and data validation, ensuring calculation integrity through explicit read-back proof.

This skill automates the critical step of enriching scaffolded research paper summaries within a dedicated wiki. It reads template pages and gathers content from multiple external academic sources (such as arXiv and DeepXiv). It then populates placeholder "TODO" sections (e.g., Method, Key Results, Open Questions) with coherent, 1-3 sentence prose, making it essential for maintaining a comprehensive, LLM-driven knowledge base.

This advanced AI skill converts raw study materials—such as syllabi, past exam papers, or personal notes—into a structured, prioritized, and difficulty-ranked High Score Roadmap. It covers all key question types (Theory, Numerical, MCQ, Coding, Lab) and optimizes study flow by ordering topics from Easy to Hard. Use it for targeted revision, gap analysis, and predicting high-yield exam questions.

A robust pipeline designed to build professional academic conference posters as single, measurement-gated HTML/CSS files, rendered to print-ready PDF. It enforces structural discipline, utilizes real paper figures with provenance, and employs a two-hue design system, preventing common pitfalls of aesthetic-only design.

This skill automates the complex compilation process required for LaTeX documents, specifically implementing the sequence: xelatex -> bibtex -> xelatex -> xelatex. It takes pre-assembled content (such as abstracts and body text) and compiles them into a cohesive, structured academic paper. Ideal for the final steps of scientific and technical writing.

This meta-skill automates the process of compiling a daily briefing on the top submissions from the arXiv repository. It fetches the latest papers in a specified category, generates a structured, per-paper digest summarizing the core claims, methods, and key findings. It then renders this information into a professional PowerPoint deck (one slide per paper) and persists the entire digest into long-term memory. Ideal for daily research briefings or knowledge management.

This tool is designed to generate a concise and academically rigorous abstract for completed research papers. By taking the revised body, the function synthesizes the core problem, methodology, key results, and overall significance. It adheres strictly to defined style preferences, audience needs, and outputting a pure LaTeX fragment, ensuring the summary is accurate and professional.

This tool assists researchers in structuring academic papers by mapping specific claims across different sections (Introduction, Method, Results, Discussion) to required external citations (BibTeX keys). It enforces citation density rules and ensures the correct role alignment (e.g., background, comparison, limitation) before the actual drafting process, guaranteeing that every core claim is properly supported and distributed.

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This advanced tool generates a comprehensive, five-section academic paper outline (Abstract, Introduction, Method, Results, Discussion). Given a research topic, preferred style, and curated reference sources, it crafts a detailed narrative backbone aiming for 6,500-8,000 words. It ensures that each section contains sufficient concrete substance, sub-topics, and specific findings, providing a robust foundation for authors to expand into a full, high-quality research manuscript.

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 reads a CSV file containing experimental results (x, y_baseline, y_ours). It utilizes matplotlib to generate a two-line comparative plot of these data points and outputs the visualization directly as a PDF file. It is highly useful for academic purposes, allowing users to easily compare baseline model performance against proposed methods for inclusion in research papers.