# Tools

> Every listed resource, editors’ picks first. The filters below can be combined.

- Search: append `?q=<keywords>` to the URL. Separate several keywords with spaces; all of them must match. Matches name, repository, tags, professions, description and README.
- Filter by type: append `?type=<cli|skill|plugin|mcp>` to the URL.
- Filter by profession: append `?profession=<profession id>` to the URL. Separate several ids with commas; a resource matches when it suits any of them. Ids are listed at https://hebbmind.com/api/v1/professions
- Filter by tag: append `?tag=<tag>` to the URL. Separate several tags with commas; a resource must carry all of them. Tags are listed at https://hebbmind.com/api/v1/tags

Resources for DevOps / SRE or Data analysis or Finance / Accounting, Tagged `ocr`, `python`: 5 items.

> - DevOps / SRE: Deployment, monitoring, cloud resources and reliability.
> - Data analysis: Querying, statistical analysis, reporting and visualization.
> - Finance / Accounting: Bookkeeping, reconciliation, financial statements, budgets and tax.

- [PDF](https://hebbmind.com/skill/pdf.md): [Skill] Extracts text and tables from PDFs; merges, splits and rotates pages, adds watermarks, fills forms and OCRs scanned documents. The official Anthropic skill behind Claude's document features; source-available, not open source.
- [OCRmyPDF](https://hebbmind.com/cli/ocrmypdf.md): [CLI] Adds a searchable, copyable text layer to scanned PDFs, with automatic deskew, rotation and size optimization, outputting PDF/A by default. Built on Tesseract, supports 100+ languages and runs fully locally.
- [RapidOCR](https://hebbmind.com/cli/rapidocr.md): [CLI] Lightweight OCR built on PaddleOCR models converted to ONNX, recognizing Chinese and English by default on CPU alone. The rapidocr CLI reads text from screenshots, photos and scanned page images. Needs Python; downloads models on first run, then works offline. Convert PDFs to images first.
- [invoice2data](https://hebbmind.com/cli/invoice2data.md): [CLI] Extracts issuer, amount, date, invoice number and other fields from PDF invoices using YAML/JSON templates, exporting CSV, JSON or XML. Ships several text-extraction backends, OCR for scanned invoices and an optional AI fallback.
- [Docling MCP](https://hebbmind.com/mcp/docling-mcp.md): [MCP] The Docling project's official MCP: converts PDF, Word, PowerPoint and more into structured documents with layout and table detection and OCR for scans, and lets agents build new documents paragraph by paragraph and export Markdown. Local mode needs Python and uv, or use your own Docling Serve.