Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/langchain-ai/deepagents/gpu-document-processingnpx skills add langchain-ai/deepagents --skill gpu-document-processinggit clone --depth 1 https://github.com/langchain-ai/deepagentsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00041 | $0.00745 |
| Opus 5 | $0.00020 | $0.00373 |
| Sonnet 5 | $0.00008 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
gpu-document-processing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPU Document Processing Skill
Process large documents and document collections using GPU-accelerated tools. This skill uses the sandbox-as-tool pattern: the agent runs on CPU for reasoning, and sends document processing work to a GPU-equipped environment.
When to Use This Skill
Use this skill when:
- Processing large PDF files (50+ pages)
- Analyzing collections of documents (10+ files)
- Extracting structured data from unstructured documents
- Performing bulk text extraction and chunking
- Generating embeddings for large document sets
- The user uploads or references large documents for analysis
Architecture: Sandbox as Tool
This skill follows the sandbox-as-tool pattern for GPU execution:
- Agent reasons on CPU - planning, synthesis, report writing
- Processing sent to GPU sandbox - document parsing, embedding, extraction
- Results returned to agent - structured output for further analysis
This separation ensures:
- API keys stay outside the sandbox (security)
- Agent state persists independently of processing jobs
- Processing can be parallelized across documents
- Cost-efficient: GPU used only during processing, not during reasoning
Capabilities
PDF Text Extraction
Extract text content from PDF documents with layout preservation:
- Headers, paragraphs, lists, and tables detected separately
- Page numbers and section boundaries preserved
- Multi-column layout handling
Tabular Data Extraction
Extract tables from documents into structured formats:
- PDF tables to CSV/DataFrames using GPU-accelerated parsing
- Automatic column type detection
- Handles merged cells and multi-row headers
Document Chunking
Split large documents into meaningful chunks for analysis:
- Semantic chunking (by topic/section boundaries)
- Fixed-size chunking with overlap for embedding
- Configurable chunk sizes (default: 512 tokens)
Embedding Generation
Generate vector embeddings for document chunks:
- Uses NVIDIA NeMo Retriever NIM for GPU-accelerated embedding
- Supports batch processing for large document sets
- Compatible with standard vector stores (Milvus, ChromaDB)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 94 lines · 41 tokens per session scan A 80fad4867bc9
gpu-document-processing is a skill published in the GitHub repository langchain-ai/deepagents (28,721 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 745 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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