A high-performance extraction tool that converts PowerPoint presentations into clean Markdown or structured JSON data.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pptx-to-text
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~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
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Help me install pptx-to-text using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
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PPTX to Text is a developer-centric tool designed to bridge the gap between visual presentation files and machine-readable text. By leveraging the MinerU document intelligence engine from OpenDataLab, this skill allows users to programmatically ingest content from .pptx files. It is an essential addition to the Openclaw Skills ecosystem for anyone building automated document processing pipelines.
The skill provides two primary modes of operation: a quick, token-free flash extraction for small files and a comprehensive API-driven extraction for complex data structures. Whether you are building a knowledge base or preparing data for an AI agent, this tool ensures that presentation content is easily accessible and ready for analysis.
To get started with this skill, install the MinerU CLI via npm or Go:
npm install -g mineru-open-api
# Alternatively, for macOS/Linux users
go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest
For basic use, no token is required. For full extraction features, set up your API token:
mineru-open-api auth
# Or set the environment variable
export MINERU_TOKEN="your-token-here"
The skill organizes extracted data based on the requested format to ensure compatibility with various Openclaw Skills:
| Output Format | Description | Best Use Case |
|---|---|---|
| Markdown | Near-plain text representation of slides | Human reading / LLM context |
| JSON | Structured objects containing per-slide text fields | Data parsing / Database ingestion |
| Stdout | Direct text stream | CLI piping and automation |
Users can also provide a --language hint (e.g., en for English) to improve extraction accuracy.
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