thehardwarestore.ai

This repository provides the 'novathesis' LaTeX class and template for formatting MSc dissertations, PhD theses, and other academic documents. It is primarily targeted at Portuguese universities (25+ supported schools) but can be adapted for other institutions. The architecture is modular: a core engine (nt-core.sty) built on LaTeX3 (Expl3) manages metadata, file registration, hooks, and download utilities; separate style files handle covers (nt-covers.sty), spines (nt-spine.sty), rendering of front/main/back matter (nt-render-matter.sty), and options (nt-options.sty). Users configure the document via files in the 0-Config/ directory, and the main entry point is template.tex which calls \ntprint commands. The build system uses latexmk with a Makefile, supports multiple LaTeX engines (pdfLaTeX, LuaLaTeX, XeLaTeX), and includes CI via GitHub Actions. External dependencies include a full LaTeX distribution, biber for bibliography, bib2gls (requires Java) for glossaries, and Ghostscript for PDF post-processing. The template is actively maintained with a comprehensive changelog and version 8.3.0.

This repository provides a two-skill workflow system for academic paper creation designed to work with Claude Code. The 'academic-paper-strategist' skill handles research planning through platform analysis, literature search, gap identification, and outline optimization with quality gates. The 'academic-paper-composer' skill executes systematic writing from outlines with chapter-level quality checks and final evaluation. The system emphasizes quality control through structured rubrics and iterative validation, transforming research ideas into submission-ready manuscripts with comprehensive quality reports. While primarily documentation-heavy, it contains several self-contained, reusable evaluation systems that demonstrate sophisticated assessment frameworks.

This repository is a Claude Code/Pi skills framework for academic research and writing. It packages markdown skills, agent definitions, and Python tooling that together support the full research pipeline: topic discovery, deep research, paper writing, review, citation verification, and meta-analysis. The dominant character is application/tooling: it has CLI entry points, a Pi wrapper, dozens of lint/check scripts, and a large test suite. It is not a data pipeline, though it includes network-backed citation lookup scripts and research-oriented data-integrity checks.

This repository is a curated knowledge base and skill package for academic paper writing, specifically targeting ML/CV/NLP-style research papers. It is not a software application or data pipeline; rather, it provides structured writing guides, templates, and examples organized as a reusable 'skill' for AI coding agents (Codex, Claude Code, Gemini). The core content includes section-specific writing guides (Abstract, Introduction, Method, Experiments, Conclusion), paragraph clarity checks, adversarial self-review checklists, and example banks with reusable sentence skeletons. The architecture is a static file collection with a main entry point (SKILL.md) that defines the workflow and references to sub-guides. There are no executable scripts, API endpoints, or data processing components. The repository is well-maintained with high community engagement (6549 stars) and demonstrates strong craftsmanship in organizing and presenting writing methodology, though it is not a software engineering artifact.

SurveyX is an academic survey automation system that leverages Large Language Models (LLMs) to generate domain-specific academic papers from user-provided references. The system implements a modular pipeline that processes markdown-formatted reference documents through stages of preprocessing, outline generation, content drafting, post-refinement (citation correction, figure/table insertion), and LaTeX/PDF output generation. Designed for offline use in its open-source version, it accepts survey titles, keywords, and local reference paths to produce structured surveys with watermarked PDF outputs. The architecture emphasizes separation of concerns with distinct modules for LLM interaction, content refinement, and document generation, supported by monitoring utilities for tracking token usage and pipeline timing.

This repository provides a LaTeX template for generating structured response letters to reviewer comments on academic papers. It consists of a main LaTeX document (review_response.tex), a custom style file (reviewresponse.sty) that defines specialized environments for comments and responses, bibliography management, and modular content files. The template automates formatting, numbering, and visual distinction between reviewer comments and author responses, producing PDF outputs that follow academic conventions for manuscript revisions.

RebuttalStudio is a desktop application built with Electron that assists academic researchers in writing peer-review rebuttals through a structured five-stage workflow: Breakdown, Reply, First Round, Multiple Rounds, and Final Remarks. The application operates locally, storing project data in the file system and leveraging large language models (LLMs) via various providers (OpenAI, Gemini, etc.) to aid in generating and refining responses at each stage. It features a document memory system for importing and summarizing background knowledge from PDFs, Markdown, and other formats, which can be used as context during the rebuttal process. The application follows a classic Electron architecture with a main process handling window management, IPC, and core services (project persistence, file operations, LLM API calls), and a renderer process managing the user interface and state. Communication between processes occurs through a contextBridge-exposed API (`window.studioApi`). Key innovations include a zero-intrusion internationalization system that efficiently updates the UI based on CSS selector mappings, a robust JSON repair pipeline to handle common LLM output formatting issues, and a provider abstraction layer that simplifies integration with multiple LLM backends. The software also implements thoughtful usability features such as debounced autosave, project snapshots for undo/redo, and a modular stage-based interface. While the application demonstrates strong architectural separation between processes and contains several reusable components (e.g., i18n system, JSON repair utilities), the renderer process contains a large, monolithic file that combines UI rendering, state management, and business logic, which could present challenges for maintainability as the project grows. Despite this, the codebase exhibits careful attention to detail in areas like incremental saving, internationalization, and LLM integration, reflecting a solid implementation that meets the needs of its target audience.

This repository is a full-stack web application, 'BypassAIGC', functioning as an AI-powered academic writing assistant. Its core operational logic, confirmed by code analysis of its two-stage 'polish' and 'enhance' pipeline and configuration flags, is to rewrite text to evade detection by AIGC classifiers like GPTZero. It is a monolithic application built with a FastAPI backend and a React frontend, packaged for standalone deployment via PyInstaller. The codebase features a highly sophisticated, deterministic Word document formatting engine (`word_formatter`) that parses markdown into a Document AST, renders it to OOXML, validates the structure against a StyleSpec, and applies patch-based auto-corrections. This module demonstrates exceptional craftsmanship in document engineering. While functionally complete, the application suffers from significant technical debt. This includes critical security vulnerabilities (default secrets, permissive CORS, plaintext API key storage), ad-hoc database migrations, duplicated application entry points, and incomplete features (e.g., PDF export is a stub). The project is maintained but carries a high risk of technical drift, making it unsuitable for production deployment without substantial hardening.

This repository is a curated collection of prompt templates and agent skill configurations for AI-assisted academic writing. It contains no executable code, only a single README.md file that documents prompts for translation, polishing, logic checking, and figure/table caption generation, as well as instructions for configuring agent skills in tools like Cursor. The prompts are designed for use with large language models to help researchers write and refine papers. While the content is well-organized and practical, the repository is purely documentation with no software implementation, making it a reference resource rather than a functional application.