Agent skill · data analytics · nvidia
nemo-retriever
Use when searching, extracting, ingesting, or querying a document collection with the NeMo Retriever 26.8.1 CLI, including local LanceDB indexes and deployed Retriever services. Use for PDFs, images, Office files, HTML, text, audio, and video; not for editing documents or web search.
Why this skill is useful
Adds specific commands and workflows for indexing and querying various document formats that the AI wouldn't reliably generate on its own.
What it needs
Requires ffmpeg, libreoffice, nemo-retriever installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
NeMo Retriever Use the retriever CLI. Prefer it over hand-built retrieval code. Install only when missing Create a project-local Python environment: Install the package variant required by the workflow: Do not clone NeMo Retriever or install from a Git URL. If retriever is already on PATH, use that installation. Local workflow Build a local index: Query it: Use retriever ingest batch only for an explicitly requested Ray batch run. Service workflow Use these forms for an already deployed Retriever service: Set NEMORETRIEVERAPITOKEN when the service requires Bearer authentication. Do not pass local LanceDB flags to the service commands. Rules Use the existing index or service when one is provided; do not rebuild it. Use retriever ingest --help, retriever query --help, or the relevant batch / service help for options not shown here. Answer only from retrieved evidence; preserve source and page metadata when the task requests citations.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/nemo-retriever