Skill UI

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 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.

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.

Use PaperJSX to automate the creation of professional documents, including PPTX presentations, DOCX reports, XLSX spreadsheets, and PDF files. By providing a structured JSON specification, this tool generates ready-to-use files, eliminating manual formatting and ensuring consistency across large-scale reporting and content generation pipelines.

A utility for managing and installing specialized AI skills into the Codex environment. It supports fetching curated skills from predefined sources (like openai/skills) or installing custom capabilities directly from any GitHub repository, including private ones. It handles listing available skills, installing specific modules, and provides robust fallback methods (download/git sparse checkout).

This skill provides an offline self-optimization cycle for local AI agents. When triggered, it harvests past interaction sessions, mines recurring tasks and patterns, and re-runs these tasks offline. It then proposes bounded edits to the agent's memory (CLAUDE.md) and skills (SKILL.md). Crucially, all changes pass through a held-out validation gate, ensuring that only improvements are adopted, thereby continuously enhancing the agent's performance for the user's specific workflow without requiring model retraining.

An internal, language-agnostic probe designed for investigating complex stack traces when the source language is unknown. It analyzes the provided trace to output structured, language-neutral elements, including failure contract checks, minimal reproduction guidance, and specific patch targets. This tool is vital for robust failure analysis and debugging across diverse codebases.

An internal diagnostic tool designed for analyzing Go language panics and complex stack traces. It provides structured guidance, including Go-specific nil/error checks, minimal reproducible `go test` commands, and precise patch targets, enabling highly efficient root cause analysis and debugging for Go projects.

An internal utility designed for deep investigation of JavaScript or TypeScript stack traces. It analyzes complex runtime errors, providing specific checks for asynchronous boundaries, module resolution issues, JSON parsing failures, and type contract violations. It aids developers by generating minimal reproduction commands and identifying potential patch targets for robust error handling.

A specialized internal helper for deep meta-stack trace investigation in Python environments. This tool guides users through generating Python-specific root-cause checks, creating minimal reproducible test cases using pytest, and identifying precise targets for defensive patches (e.g., validation or guard clauses). It ensures robust debugging and testing practices.

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.