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rag-blueprint

rag-blueprint is a skill for Claude Code from NVIDIA-AI-Blueprints/rag. It costs 92 tokens per session (2,877 once invoked), scanned A, original, Apache-2.0.

A guide for deploying and managing NVIDIA RAG Blueprint, a system for retrieving relevant information before generating answers. It covers Docker, Kubernetes, Helm, and Python library deployments.

In plain words
What is it for?
Deploying or removing RAG services, enabling or changing features such as search, ingestion, query rewriting, models, guardrails, and visual-language workflows, and diagnosing failures.
Why use it?
It provides a route for setup, configuration, troubleshooting, feature changes, and shutdown across different deployment methods, while checking the available hardware and software first.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

not rated 759repo +2 7d ago A scan SkillSpector: warn 92 tokens original Apache-2.0

Good fit Deploying or removing RAG services, enabling or changing features such as search, ingestion, query rewriting, models, guardrails, and visual-language workflows, and diagnosing failures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nvidia-ai-blueprints/rag/rag-blueprint
View source ↗ NVIDIA-AI-Blueprints/rag build.nvidia.com
Install

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.

Any agent
npx skills add NVIDIA-AI-Blueprints/rag --skill rag-blueprint
Clone the repo
git clone --depth 1 https://github.com/NVIDIA-AI-Blueprints/rag

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for rag-blueprint

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia-ai-blueprints/rag/rag-blueprint/github.svg)](https://agentmods.dev/skills/nvidia-ai-blueprints/rag/rag-blueprint)
Your own site
<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/rag/rag-blueprint"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/rag/rag-blueprint/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for rag-blueprint

Your own site · 80×15
<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/rag/rag-blueprint"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/rag/rag-blueprint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,877 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 63
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00092 $0.02877
Opus 5 $0.00046 $0.01438
Sonnet 5 $0.00018 $0.00575
Haiku 4.5 $0.00009 $0.00288

Measured 10d ago against content hash 7167ef6b9d4f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

rag-blueprint scanned grade A with 1 finding 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 10d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

allowed-tools: Bash(echo *) Bash(nvidia-smi *) Bash(curl --version *) Bash(docker ps *) Bash(docker info *) Bash(docker --version *) Bash(docker version *) Bash(docker logs *) Bash(docker inspect *) Bash(docker stats *)
skills/rag-blueprint/SKILL.md · 205 lines

How it starts

The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NVIDIA RAG Blueprint

Purpose

Use this skill for NVIDIA RAG Blueprint operations: deployment, configuration, troubleshooting, shutdown, and feature management across Docker, Helm, and library deployments.

Instructions

  1. Match the user request to the intent routing table below.
  2. Read the referenced playbook before making changes.
  3. Use repository docs and deployment config files as the source of truth.
  4. Verify the affected service or workflow after changes.

Prerequisites

  • NVIDIA RAG Blueprint repository checkout.
  • Docker/Compose or Kubernetes/Helm for deployments.
  • Python 3.11+ for library workflows.
  • NVIDIA GPU tooling for self-hosted NIM services.

Autonomy Principles

  • Auto-detect everything: GPU, VRAM, drivers, Docker, CUDA, disk, OS, ports, existing services, NGC key, repo state.
  • If it can be checked with a command, check it — don't ask the user.
  • Ask only when user action is required: providing an API key, confirming data deletion, or choosing between equally valid options.
  • Once analysis is done, route to the correct workflow and execute.

Intent Detection

Determine what the user wants and route immediately:

User Intent Action
Deploy, install, set up, start RAG Read and follow references/deploy.md
Configure, enable, change, toggle a feature Use the Configure section below
Troubleshoot, debug, fix, error, unhealthy Read and follow references/troubleshoot.md
Stop, shutdown, tear down, clean up Read and follow references/shutdown.md

If the intent is ambiguous, infer from context (e.g., "RAG isn't working" → troubleshoot; "get RAG running" → deploy). Only ask if genuinely unclear.

Configure

Requires a running RAG deployment. If services are not running, deploy first via references/deploy.md.

Match the user's request to a reference file, then read and follow it:

Feature Keywords Reference
VLM, VLM embeddings, image captioning references/configure/vlm.md
NeMo Guardrails references/configure/guardrails.md
Agentic RAG, planning/execution agent, agentic streaming, stage events references/configure/agentic-rag.md
Query rewriting, decomposition, multi-turn references/configure/query-and-conversation.md
Ingestion (text-only, audio, Nemotron Parse, OCR, batch CLI, NV-Ingest, volume mount, performance) references/configure/ingestion.md
Search, retrieval, hybrid search, multi-collection, metadata, filters, Elasticsearch filters, reranker, topK, accuracy/performance references/configure/search-and-retrieval.md
LLM/embedding/ranking model changes, vector DB, Milvus/Elasticsearch auth, service keys, model profiles, ports/GPU references/configure/models-and-infrastructure.md
Reasoning, thinking mode, reasoning_content, self-reflection, prompts, generation params (tokens, temperature, citations), per-request LLM params references/configure/reasoning-and-generation.md
Summarization references/configure/summarization.md
Observability (tracing, Zipkin, Grafana, Prometheus) references/configure/observability.md
Multimodal query (image + text) references/configure/multimodal-query.md
Data catalog (collection/document metadata) references/configure/data-catalog.md
User interface (UI settings, reasoning panel, metadata filters) references/configure/user-interface.md
API reference (endpoints, schemas) references/configure/api-reference.md
Evaluation (RAGAS metrics) references/configure/evaluation.md (and skill rag-eval)
MCP server & client, agent toolkit references/configure/mcp.md
Migration (version upgrades) references/configure/migration.md
Notebooks (setup and catalog) references/configure/notebooks.md

Read the full file on GitHub · 205 lines

Files

What ships with it

37 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 10d ago First seen · 205 lines · 92 tokens per session scan A 7167ef6b9d4f

Subscribe to this mod's changes

rag-blueprint is a skill published in the GitHub repository NVIDIA-AI-Blueprints/rag (759 stars, last pushed 7d ago), licensed Apache-2.0. It adds 92 tokens to every session and 2,877 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AIDebuggingDevOps and cloudRAG and vectorsblueprintnimretrieval-augmented-generation
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