AutoGineer
Generated banner for Nemo Retriever
Generated logo for Nemo Retriever

Nemo Retriever

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk
Download ZIP

From the author

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.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The NeMo Retriever skill provides a standardized interface for interacting with the NeMo Retriever 26.8.1 CLI. It supports both local document indexing via LanceDB and connectivity to deployed Retriever services. The skill is designed to handle diverse file formats including PDFs, images, Office documents, HTML, text, audio, and video, enabling efficient retrieval-augmented generation workflows by allowing users to ingest data and query it for evidence-based answers.

What it does

  • Ingests local files and directories into LanceDB indexes.
  • Queries local indexes for specific information.
  • Connects to and interacts with remote, deployed Retriever services.
  • Supports multimedia ingestion including audio and video files.
  • Provides evidence-based responses with source and page metadata.

When to use it

  • When you need to build a local vector index from a collection of documents.
  • When querying an existing local LanceDB index for specific answers.
  • When interacting with a remote NeMo Retriever service via API.
  • When you need to extract information from diverse file types like PDFs or media.

Advantages

  • Standardizes retrieval workflows using a single CLI.
  • Supports both local and remote deployment architectures.
  • Enables evidence-based responses with citation support.
  • Flexible installation options based on specific ingestion needs.

Usage examples

“Ingest the documents in my ./data folder into a local LanceDB index.”
The agent runs the retriever ingest command on the specified directory, creating a local index at the defined LanceDB URI.
“Query the existing index for information about project timelines.”
The agent executes a retriever query against the local index and returns the top-k evidence snippets.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nemo-retriever/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nemo-retriever.zip -d .agents/skills

Documentation by NVIDIA

NeMo Retriever

Use the retriever CLI. Prefer it over hand-built retrieval code.

Install only when missing

Create a project-local Python environment:

uv venv .venv --python 3.12
export PATH="$PWD/.venv/bin:$PATH"

Install the package variant required by the workflow:

# Remote NIM or service client
uv pip install --python .venv/bin/python "nemo-retriever==26.8.1"

# Local GPU ingestion
uv pip install --python .venv/bin/python "nemo-retriever[local]==26.8.1"

# Local service using Hugging Face models
uv pip install --python .venv/bin/python \
  "nemo-retriever[service,local]==26.8.1"

# Local audio or video ingestion
uv pip install --python .venv/bin/python \
  "nemo-retriever[local,multimedia]==26.8.1"

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:

retriever ingest <file-or-directory> \
  --lancedb-uri lancedb --table-name nemo-retriever

Query it:

retriever query "<question>" \
  --lancedb-uri lancedb --table-name nemo-retriever \
  --top-k 5 --format evidence

Use retriever ingest batch only for an explicitly requested Ray batch run.

Service workflow

Use these forms for an already deployed Retriever service:

retriever ingest service <file-or-directory> \
  --service-url "$RETRIEVER_SERVICE_URL"

retriever query service "<question>" \
  --service-url "$RETRIEVER_SERVICE_URL" \
  --top-k 5 --format evidence

Set NEMO_RETRIEVER_API_TOKEN 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.

Files

skill-card.md

Description: <br>

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. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers use this skill to search, extract, ingest, and query document collections using the NeMo Retriever CLI, supporting PDFs, images, Office files, HTML, text, audio, and video formats. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [Optional] <br> Credential Type(s): [API key] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Shell commands, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8) <br>

  • Codex (openai/openai/gpt-5.5) <br>

Evaluation Tasks: <br>

4 evaluation tasks (3 positive, 1 negative), each running 3 attempts in isolated sandbox pods. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Whether the skill is safe to use — checks for unsafe operations, secret leakage, and unauthorized access. <br>

  • Correctness: Whether the final answer is correct against the reference answer. <br>

  • Discoverability: Whether the right skill was loaded and activated when needed, and decoys were avoided. <br>

  • Effectiveness: Whether the skill helped complete the user's goal (50% goal completion + 50% expected workflow adherence). <br>

  • Efficiency: Whether the skill avoided wasted tool calls and token usage (50% tool-call productivity + 50% token efficiency). <br>

Underlying evaluation signals used in this run: <br>

  • security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • skill_efficiency: Tool-call productivity — measures whether the skill avoided unnecessary tool calls. <br>

  • token_efficiency: Actual uncached prompt plus completion token usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall78.3%72.2%
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness27.5% → 90.0% (+62.5 points)26.0% → 70.0% (+44.0 points)
Discoverability78.3%73.3%
Effectiveness31.9% → 41.3% (+9.4 points)20.5% → 40.0% (+19.5 points)
Efficiency82.0%77.5%

Skill Version(s): <br>

e971432a (source: git SHA, committed 2026-09-11) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nemo-retriever · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nemo-retriever.zip