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rag-blueprint

rag-blueprint is a skill for Codex from PracticalSwan/agent-skills. It costs 30 tokens per session (2,957 once invoked), scanned A, original, MIT.

A guide for deploying, configuring, troubleshooting, and shutting down NVIDIA RAG Blueprint systems. These are RAG applications that search documents before generating answers, and they can run with Docker, Helm, or Python libraries.

In plain words
What is it for?
Use it to deploy or repair NVIDIA RAG Blueprint services, configure Docker or Kubernetes installations, manage NIM settings, check available hardware, and shut systems down.
Why use it?
It helps diagnose and operate the services, containers, settings, and optional features needed to run a RAG stack.

Skill for Codex

Written for Codex: reads ~/.codex or $CODEX_HOME. Also seen: mentions Claude Code; mentions Codex.

not rated 13repo changed yesterday A scan SkillSpector: warn 30 tokens original MIT

Good fit Use it to deploy or repair NVIDIA RAG Blueprint services, configure Docker or Kubernetes installations, manage NIM settings, check available hardware, and shut systems down.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/practicalswan/agent-skills/rag-blueprint
View source ↗ PracticalSwan/agent-skills
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 PracticalSwan/agent-skills --skill rag-blueprint
Clone the repo
git clone --depth 1 https://github.com/PracticalSwan/agent-skills

Made for: Codex.

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/practicalswan/agent-skills/rag-blueprint/github.svg)](https://agentmods.dev/skills/practicalswan/agent-skills/rag-blueprint)
Your own site
<a href="https://agentmods.dev/skills/practicalswan/agent-skills/rag-blueprint"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/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/practicalswan/agent-skills/rag-blueprint"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/rag-blueprint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,957 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: 3 findings, up to high

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 →

  • high Prompt Injection · line 194
    Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.
    Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
  • medium Excessive Agency · line 182
    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.
  • medium Excessive Agency · line 35
    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 $0.00030 $0.02957
Opus 5 $0.00015 $0.01478
Sonnet 5 $0.00006 $0.00591
Haiku 4.5 $0.00003 $0.00296

Measured yesterday against content hash 144b843c3d93, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, 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 yesterday.

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.

- Docker: `docker ps --format "table {{.Names}}\t{{.Status}}" | head -20; curl -s http://localhost:8081/v1/health?check_dependencies=true 2>/dev/null | head -1`
rag-blueprint/SKILL.md · 224 lines

How it starts

The opening of the file, as written. The whole thing — 224 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 · 224 lines

Files

What ships with it

38 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. yesterday Changed · -1 lines 144b843c3d93
  2. 2d ago Changed 3cc7520d3362
  3. 5d ago First seen · 225 lines · 30 tokens per session scan A 5c196bdbcf52

Subscribe to this mod's changes

rag-blueprint is a skill published in the GitHub repository PracticalSwan/agent-skills (13 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 2,957 once invoked, about $0.0002 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 2026-09-03.

ContainersData and AIDevOps and cloudNVIDIARAG and vectorsblueprintdeploymentopsretrieval
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