Skill UI

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 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.

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.

This specialized tool revises fragmented drafts of academic papers (including Introduction, Method, Results, and Discussion) into a single, cohesive, and professionally structured LaTeX body. It ensures strict consistency across terminology, metrics, and tone, while adhering to specific preferences like audience, venue style, and required citations. It is designed for academic authors preparing the main manuscript body before writing the abstract.

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.

An internal, non-user-invocable agent used by the meta-skill creator process. It executes positive (G3) and negative (G4) smoke tests against a candidate meta-skill definition (SKILL.md). It simulates meta-resolution using LLM-generated fixtures and reports the results in a structured JSON format.

An internal helper designed for analyzing Rust panics and backtraces. It guides users to perform Rust-specific checks for proper handling of Option and Result types, generates minimal reproducible cargo test commands, and pinpoints exact patch targets for safe code improvements.

A comprehensive voiceover studio that transforms written text into high-quality, professional spoken audio. It supports generating content for podcasts, IVR prompts, educational videos, and product demos. The system handles complex requirements including target locale, specific accents (e.g., British, Mandarin), desired emotion, and pacing, ensuring the resulting audio artifact is natural and contextually accurate. Ideal for content creators and developers needing reliable, multi-lingual narration.