PPT Master is a Python-based application designed to generate natively editable PowerPoint (PPTX) presentations from diverse source formats including PDF, DOCX, Markdown, and URLs. The system operates as a CLI tool and AI agent skill, orchestrating a workflow that converts source content into SVG intermediates before rendering them into native DrawingML shapes within the PPTX file. This approach prioritizes editability over flattened image exports, allowing users to modify text and shapes directly in PowerPoint. The architecture relies heavily on external AI APIs for content generation and image creation, with a local Flask server providing live preview and confirmation capabilities. The codebase functions as a presentation engineering toolkit, featuring bidirectional conversion between PPTX and SVG, template management, and asset handling. It enforces strict design constraints via machine-readable 'spec_lock' files to maintain visual consistency across generated decks. The application is structured as a modular skill within AI IDEs (Claude Code, Cursor), allowing developers to invoke presentation generation via chat commands. While the core conversion logic is robust, the system's stability is intrinsically linked to the availability and schema of external AI providers and the specific behavior of the python-pptx library. This repository represents a hybrid of application engineering and data pipeline logic. While the primary output is a software artifact (the PPTX file), the internal processing involves significant data transformation (document parsing, image generation, XML manipulation). The extensive example suite serves as both documentation and regression testing for the conversion engine, demonstrating the tool's capability across technical, business, and artistic domains.
PPTist is a full-featured web-based slide editor and presentation application, built with Vue 3, TypeScript, and Pinia. It replicates core PowerPoint functionality entirely in the browser, supporting text, image, shape, line, chart, table, video, audio, and LaTeX elements. Users can create, edit, animate, and present slides, with import/export support for PPTX, PDF, images, and a custom .pptist format. An AI-assisted slide generation feature allows users to input a topic and receive a complete slide deck via an external API, with template-based layout and image selection. The architecture is a single-page application with a custom canvas rendering system, rich text editing via ProseMirror, and state management through Pinia stores. The editor is divided into three main modes: Editor (full editing with thumbnails, toolbar, and canvas), Screen (presentation with speaker notes, audience view, writing board, and laser pointer), and Mobile (basic editing and preview for touch devices). Data persistence uses IndexedDB via Dexie for undo/redo snapshots, and slide data is stored as JSON in Pinia stores. External dependencies include ECharts for charts, pptxgenjs for PPTX export, and a backend server for AI generation and image search. Key entry points include src/App.vue (top-level routing), src/views/Editor/index.vue (main editor), src/views/Screen/index.vue (presentation mode), and src/views/Mobile/index.vue (mobile mode). Data flows center on the slide store: user actions trigger mutations captured as snapshots for undo/redo, export processes convert internal JSON to target formats, and import parses external files into the internal schema. The AI PPT flow sends a user prompt to the backend, receives an outline and slide data, then fills template slides with content and images. The application is actively maintained with a large community (9386 stars, 1770 forks) and is licensed under AGPL-3.0. It relies on an external backend for AI features and image search, meaning those features are not self-contained. The codebase is well-structured but lacks automated tests, and some components (e.g., HTML parser, shape library) are self-contained and potentially reusable in other projects.
This repository implements an AI-agent skill that converts images, PDFs, and image-based PPT/PPTX files into fully editable PowerPoint (.pptx) documents. The system uses a deterministic CLI state machine (`editppt`) with a four-phase workflow: normalize input into per-page jobs, dispatch to subagents for page reconstruction, record page manifests, and finalize by rebuilding the PPTX from manifests. It integrates OCR (PaddleOCR or offline detector) for text bounding boxes, image generation/editing backends (built-in, OpenAI-compatible, or Codex OAuth), and LaTeX rendering for formulas. The output preserves native text boxes, shapes (including custom paths and polygons), tables, images, and speaker notes, with strict provenance tracking for all assets. The codebase is well-structured with clear separation of concerns, a manifest-driven assembly pattern, and comprehensive tests. However, the last commit date is anomalously in the future (2026-09-16), suggesting the repository may be abandoned or the timestamp is erroneous; combined with only 1 open issue and no recent activity, it is classified as stale. The technical drift is low as the code uses modern Python and standard libraries, but external dependencies (OCR, image APIs, LaTeX) may become incompatible over time.
This repository is a Python library and Claude Code plugin for generating McKinsey-style PowerPoint (PPTX) presentations. It provides ~40 slide templates (executive summaries, charts, matrices, org charts, roadmaps, etc.) with a consistent deep-navy/bright-blue design system. The core architecture is a builder pattern: `PresentationBuilder` accepts slide specs (as dicts or keyword arguments) and delegates to slide-specific drawing functions in the `slides/` subpackage. A CLI (`mckinsey_pptx.cli`) reads JSON specs and produces `.pptx` files. The Claude Code integration includes an agent definition (`mckinsey-slide-agent.md`) that reads a template catalog, plans a deck story arc, generates a Python build script, executes it, and optionally renders PNG previews via LibreOffice/Poppler. The code is well-structured with a clean separation of concerns: theme dataclasses, base primitives, slide templates, and a builder. The adaptive slide type inference (`infer_slide_type`) is a clever design pattern that allows the same API to accept loosely-typed payloads. The library is production-ready, with provenance from a real consulting tool (AX Labs).
unioffice is a commercial Go library for creating, reading, and editing Office Open XML (OOXML) documents: Word (.docx), Excel (.xlsx), and PowerPoint (.pptx). It provides a high-level API for common document operations such as text formatting, tables, images, headers/footers, charts, and slide management, while also exposing low-level access to the underlying XML via an `X()` method on wrapper types. The library includes experimental Word-to-PDF conversion and spreadsheet formula evaluation. Its architecture is modular, with top-level packages for each document type (`document`, `spreadsheet`, `presentation`), internal packages for schema types, license management, temporary storage, and utilities, and a large set of generated schema packages under `schema/` that mirror the OOXML specification. The codebase is heavily generated from OOXML schemas and obfuscated using the `unitwist` tool, which hinders direct debugging but ensures fidelity to the standard. External dependencies include other UniDoc modules (`unipdf`, `unichart`, `unitype`, `emf`) and third-party libraries for testing, font rendering, and image processing. The library requires a commercial license key, with a free tier available via cloud.unidoc.io. It is actively maintained, with recent commits and a responsive issue tracker.