nutrient-document-processing
Process, convert, OCR, extract, redact, sign, and fill documents using the Nutrient DWS API. Works with PDFs, DOCX, XLSX, PPTX, HTML, and images.
Best use case
nutrient-document-processing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Process, convert, OCR, extract, redact, sign, and fill documents using the Nutrient DWS API. Works with PDFs, DOCX, XLSX, PPTX, HTML, and images.
Teams using nutrient-document-processing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nutrient-document-processing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nutrient-document-processing Compares
| Feature / Agent | nutrient-document-processing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Process, convert, OCR, extract, redact, sign, and fill documents using the Nutrient DWS API. Works with PDFs, DOCX, XLSX, PPTX, HTML, and images.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Nutrient Document Processing
Process documents with the [Nutrient DWS Processor API](https://www.nutrient.io/api/). Convert formats, extract text and tables, OCR scanned documents, redact PII, add watermarks, digitally sign, and fill PDF forms.
## Setup
Get a free API key at **[nutrient.io](https://dashboard.nutrient.io/sign_up/?product=processor)**
```bash
export NUTRIENT_API_KEY="pdf_live_..."
```
All requests go to `https://api.nutrient.io/build` as multipart POST with an `instructions` JSON field.
## Operations
### Convert Documents
```bash
# DOCX to PDF
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.docx=@document.docx" \
-F 'instructions={"parts":[{"file":"document.docx"}]}' \
-o output.pdf
# PDF to DOCX
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"output":{"type":"docx"}}' \
-o output.docx
# HTML to PDF
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "index.html=@index.html" \
-F 'instructions={"parts":[{"html":"index.html"}]}' \
-o output.pdf
```
Supported inputs: PDF, DOCX, XLSX, PPTX, DOC, XLS, PPT, PPS, PPSX, ODT, RTF, HTML, JPG, PNG, TIFF, HEIC, GIF, WebP, SVG, TGA, EPS.
### Extract Text and Data
```bash
# Extract plain text
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"output":{"type":"text"}}' \
-o output.txt
# Extract tables as Excel
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"output":{"type":"xlsx"}}' \
-o tables.xlsx
```
### OCR Scanned Documents
```bash
# OCR to searchable PDF (supports 100+ languages)
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "scanned.pdf=@scanned.pdf" \
-F 'instructions={"parts":[{"file":"scanned.pdf"}],"actions":[{"type":"ocr","language":"english"}]}' \
-o searchable.pdf
```
Languages: Supports 100+ languages via ISO 639-2 codes (e.g., `eng`, `deu`, `fra`, `spa`, `jpn`, `kor`, `chi_sim`, `chi_tra`, `ara`, `hin`, `rus`). Full language names like `english` or `german` also work. See the [complete OCR language table](https://www.nutrient.io/guides/document-engine/ocr/language-support/) for all supported codes.
### Redact Sensitive Information
```bash
# Pattern-based (SSN, email)
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"actions":[{"type":"redaction","strategy":"preset","strategyOptions":{"preset":"social-security-number"}},{"type":"redaction","strategy":"preset","strategyOptions":{"preset":"email-address"}}]}' \
-o redacted.pdf
# Regex-based
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"actions":[{"type":"redaction","strategy":"regex","strategyOptions":{"regex":"\\b[A-Z]{2}\\d{6}\\b"}}]}' \
-o redacted.pdf
```
Presets: `social-security-number`, `email-address`, `credit-card-number`, `international-phone-number`, `north-american-phone-number`, `date`, `time`, `url`, `ipv4`, `ipv6`, `mac-address`, `us-zip-code`, `vin`.
### Add Watermarks
```bash
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"actions":[{"type":"watermark","text":"CONFIDENTIAL","fontSize":72,"opacity":0.3,"rotation":-45}]}' \
-o watermarked.pdf
```
### Digital Signatures
```bash
# Self-signed CMS signature
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "document.pdf=@document.pdf" \
-F 'instructions={"parts":[{"file":"document.pdf"}],"actions":[{"type":"sign","signatureType":"cms"}]}' \
-o signed.pdf
```
### Fill PDF Forms
```bash
curl -X POST https://api.nutrient.io/build \
-H "Authorization: Bearer $NUTRIENT_API_KEY" \
-F "form.pdf=@form.pdf" \
-F 'instructions={"parts":[{"file":"form.pdf"}],"actions":[{"type":"fillForm","formFields":{"name":"Jane Smith","email":"jane@example.com","date":"2026-02-06"}}]}' \
-o filled.pdf
```
## MCP Server (Alternative)
For native tool integration, use the MCP server instead of curl:
```json
{
"mcpServers": {
"nutrient-dws": {
"command": "npx",
"args": ["-y", "@nutrient-sdk/dws-mcp-server"],
"env": {
"NUTRIENT_DWS_API_KEY": "YOUR_API_KEY",
"SANDBOX_PATH": "/path/to/working/directory"
}
}
}
}
```
## When to Use
- Converting documents between formats (PDF, DOCX, XLSX, PPTX, HTML, images)
- Extracting text, tables, or key-value pairs from PDFs
- OCR on scanned documents or images
- Redacting PII before sharing documents
- Adding watermarks to drafts or confidential documents
- Digitally signing contracts or agreements
- Filling PDF forms programmatically
## Links
- [API Playground](https://dashboard.nutrient.io/processor-api/playground/)
- [Full API Docs](https://www.nutrient.io/guides/dws-processor/)
- [npm MCP Server](https://www.npmjs.com/package/@nutrient-sdk/dws-mcp-server)Related Skills
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Academic Writing
## Overview
scientific-visualization
## Overview
venue-templates
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
vaex
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
uspto-database
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
uniprot-database
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
umap-learn
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
treatment-plans
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
transformers
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
torchdrug
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.