Deploys, validates, troubleshoots, and stops NVIDIA AI-Q Blueprint 2.1.x (Docker Compose, Kubernetes/Helm, local process, CLI, or web UI). Use when installing or cloning AI-Q, starting the backend, checking /health, fixing port or credential failures, or handing a verified AIQ_SERVER_URL to aiq-research. Not for deep research report generation (that is aiq-research).
Deploys, validates, troubleshoots, and stops NVIDIA AI-Q Blueprint 2.1.x (Docker Compose, Kubernetes/Helm, local process, CLI, or web UI). Use when installing or cloning AI-Q, starting the backend, checking /health, fixing port or credential failures, or handing a verified AIQ_SERVER_URL to aiq-research. Not for deep research report generation (that is aiq-research).
version
2.1.1
license
Apache-2.0
compatibility
Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Git, network
access to GitHub, and one selected runtime path: Docker Compose v2 for the default local deployment,
Python 3.11+ and uv for local process or CLI mode, Node.js 20+ and npm for local web UI mode, or
kubectl 1.28+ and Helm 3.12+ for Kubernetes and Helm mode.
metadata
{"author":"NVIDIA AI-Q Blueprint Team <aiq-blueprint@nvidia.com>","github-url":"https://github.com/NVIDIA-AI-Blueprints/aiq","tags":["nvidia","aiq","blueprint","deploy","operations","agent-skills"]}
allowed-tools
Read Bash
AIQ Deploy Skill
Overview
This skill owns setup, deployment, operational checks, troubleshooting, and shutdown of the NVIDIA AI-Q Blueprint server. It does not run deep research itself. After deployment is healthy, hand off the verified server URL to aiq-research.
The workflow stays explicit so deployment validation and handoff are repeatable across supported agent clients.
Version Compatibility: This skill targets NVIDIA AI-Q Blueprint version 2.1.0.
Skill version: X.Y.Z
Blueprint version: A.B.C
Compatible IF:
1. A == X (Major versions MUST match)
2. B >= Y (Minor version must be equal or greater)
3. C can be anything (Patch version does not affect compatibility)
Skill 2.1.0 ↔ Blueprint 2.1.0 ✅
Skill 2.1.0 ↔ Blueprint 2.2.0 ✅
Skill 2.1.0 ↔ Blueprint 2.1.5 ✅
Skill 2.1.0 ↔ Blueprint 3.0.0 ❌
Skill 2.1.0 ↔ Blueprint 2.0.0 ❌
If the Blueprint version is not compatible, check for an updated skill, use a compatible Blueprint version, or proceed only when the user accepts the risk.
When to Use
Use this skill when the user asks to:
Install or clone the NVIDIA AI-Q Blueprint repository.
Deploy or run AI-Q locally or on a cluster (Docker Compose, Kubernetes/Helm, local process, CLI, or browser UI).
Validate that the AI-Q backend is healthy and ready for aiq-research.
Stop, restart, rebuild, or clean up AI-Q services.
Hand off a verified AIQ_SERVER_URL to aiq-research.
Do not use this skill for deep research report generation — that belongs to aiq-research.
Prerequisites
Access to clone or update https://github.com/NVIDIA-AI-Blueprints/aiq.
Git available in the shell.
One deployment runtime:
Docker Engine with Docker Compose v2 — default durable local deployment.
Python 3.11+ and uv — local process or CLI mode.
Node.js 20+ and npm — local browser UI development mode.
kubectl 1.28+, Helm 3.12+, and cluster access — Kubernetes/Helm mode.
Network access to GitHub, NVIDIA-hosted model endpoints, and any selected search provider.
Credentials stored outside chat. Hosted-model usage requires NVIDIA_API_KEY; web research requires at least one supported search provider key: TAVILY_API_KEY, SERPER_API_KEY, or EXA_API_KEY.
System capacity for the selected runtime. Docker Compose mode starts the AI-Q backend and PostgreSQL by default; browser UI mode also uses frontend port 3000. Self-hosted model or RAG deployments may require GPU resources.
Windows host (PowerShell) note: Commands shown in bash syntax work in Git Bash or WSL. In PowerShell, replace test -f with Test-Path, cp with Copy-Item, and export VAR=... with $env:VAR = "...". Use curl.exe (not the curl alias to Invoke-WebRequest) for health checks.
Procedure
Step 1 — Locate or clone AI-Q
When to load: If no AI-Q checkout exists or the user asks to install/clone AI-Q, read references/locate-or-clone.mdbefore cloning.
In an existing checkout, confirm the required files:
pwdtest -f pyproject.toml
test -f deploy/.env.example
test -d configs
Expected output:pwd prints the AI-Q repository path; the test commands exit with status 0 and produce no output.
If the user asks to install, deploy, set up, or run AI-Q without naming a mode, ask:
How do you want to run AI-Q?
1. Skill backend - backend-only service for aiq-research w/o browser UI.
2. CLI - interactive terminal AI-Q.
3. UI - browser AI-Q app with backend and frontend.
4. Custom - choose an existing AI-Q config or review advanced customization docs before deployment.
Wait for the user's answer before starting services.
Do not ask this question when:
The user already specified a mode (Docker Compose, Helm, UI, CLI, or Agent Skill backend).
aiq-research routed here because a deep research request needs a backend. In that case, prefer Agent Skill backend and ask only for permission to start it if needed.
Step 3 — Prepare environment and secrets
When to load: Read references/env-and-secrets.mdbefore changing deploy/.env.
if [ ! -f deploy/.env ]; thencp deploy/.env.example deploy/.env
echo"created deploy/.env from deploy/.env.example"fi
Expected output when the file is missing:created deploy/.env from deploy/.env.exampleExpected output when the file exists: no output; the existing file is preserved.
Before writing secrets, verify deploy/.env is git-ignored:
git check-ignore deploy/.env
Expected output:deploy/.env or a matching ignore rule. If it is not ignored, stop and fix the ignore rule before placing credentials in the file.
HARD RULES:
Never print secret values. Check only whether required environment variables are set.
Never overwrite deploy/.env when it already exists.
Never ask the user to paste secret values into chat. Ask them to update deploy/.env directly.
Step 4 — Route to the selected deployment path
Match the user request, then read the referenced file before acting:
User Intent
Reference to Load
No AI-Q checkout exists, install AIQ, clone AIQ, locate repo
references/locate-or-clone.md
Configure environment, check API keys, inspect .env
references/env-and-secrets.md
Choose an AI-Q workflow config, understand config files, set BACKEND_CONFIG or CONFIG_FILE
references/configs.md
Backend-only local server for aiq-research, AIQ as an Agent Skill
references/skill-backend.md
Terminal assistant, CLI-only run, no web UI
references/terminal-cli.md
Quick local development run, start UI/backend without containers
references/local-web.md
Default durable local deployment, Docker Compose, containers, PostgreSQL
references/docker-compose.md
Kubernetes, Helm, cluster deployment
references/kubernetes-helm.md
Foundational RAG / FRAG integration
references/frag.md
Basic health checks, shallow smoke checks, handoff to aiq-research
references/validation.md
Optional deep research completion validation
references/end-to-end-validation.md
Logs, unhealthy services, port conflicts, config failures
references/troubleshooting.md
Stop services, restart, rebuild, safe cleanup
references/shutdown.md
Step 5 — Validate and hand off
When to load: After startup, read references/validation.md and run the appropriate checks for the selected mode.
For the default local backend, verify health:
curl -sf http://localhost:8000/health
Expected output: a successful JSON health response or an empty successful response depending on the server build. If the command fails, read references/troubleshooting.md and diagnose before claiming the backend is ready.
aiq-research needs a reachable AI-Q server URL. If the backend is on the default port:
AIQ_SERVER_URL=http://localhost:8000
If the backend runs elsewhere:
export AIQ_SERVER_URL="http://localhost:<PORT>"
PowerShell:
$env:AIQ_SERVER_URL = "http://localhost:8000"
HARD RULE: Do not continue into deep research or deep research completion validation unless the user asks for it or confirms the post-deploy validation prompt. This skill's success criterion is a deployed and basically validated server, not report generation quality.
Examples
Example 1: Deploy a backend-only Skill server with Docker Compose
Expected output: a successful health response. Then tell the user to keep AIQ_SERVER_URL set before invoking aiq-research.
Pitfalls
Backend port is already in use
Symptoms: Docker Compose fails to bind port 8000; curl -sf http://localhost:8000/health reaches an unexpected service or fails.
Causes: Another AI-Q backend or local dev server is running; PORT in deploy/.env conflicts.
Solutions:
Identify the process:
lsof -nP -iTCP:8000 -sTCP:LISTEN
Either stop the conflicting process with the user's approval or set a different port in deploy/.env (e.g., PORT=8100).
Restart and verify:
curl -sf http://localhost:8100/health
Required credentials are missing
Symptoms: Infrastructure starts, but model-backed chat or research requests fail. Logs mention unauthorized, forbidden, invalid key, or missing provider configuration.
Causes:NVIDIA_API_KEY is missing or empty; no supported search provider key is configured.
Solutions:
Check presence without printing values by following references/env-and-secrets.md.
Ask the user to update deploy/.env; do not ask them to paste secrets into chat.
Rerun references/validation.md after the user updates credentials.
Backend is healthy but not compatible with aiq-research
Symptoms:/health succeeds, but /chat or /v1/jobs/async/agents fails. aiq-research reports async agents unavailable.
Causes: The selected config is CLI-only or does not expose the web/API backend expected by the skill. BACKEND_CONFIG or CONFIG_FILE points at the wrong AI-Q config.
Solutions:
Read references/configs.md and confirm the selected config is API-enabled.
For the default Skill backend, use configs/config_web_default_llamaindex.yml.
Restart the backend and rerun references/validation.md.
Docker cleanup would remove useful state
Symptoms: Troubleshooting suggests docker compose down -v; the user may have local PostgreSQL job or checkpoint data they want to keep.
Causes:down -v removes Docker volumes. Rebuilds and restarts are often enough for config or image changes.
Solutions:
Prefer a normal restart from references/shutdown.md.
Ask for explicit approval before running volume deletion.
After cleanup, rerun deployment and validation from the selected route.
FRAG not ready
HARD RULE: Do not claim FRAG is ready unless both RAG_SERVER_URL and RAG_INGEST_URL are configured and reachable. Read references/frag.md for setup details.
Verification
Run these checks after the selected deployment path starts:
Health endpoint (default port 8000):
curl -sf http://localhost:8000/health
Expected: successful JSON or empty 200 response.
Non-default port (if applicable):
curl -sf "$AIQ_SERVER_URL/health"
Expected: successful health response.
Git ignore check (should already be done before secrets):
git check-ignore deploy/.env
Expected: deploy/.env or matching ignore rule.
Repository file check (in existing checkout):
test -f pyproject.toml && test -f deploy/.env.example && test -d configs && echo"OK"
If any check fails, read references/troubleshooting.md and diagnose before reporting success.
Related Skills
aiq-research — Consumes the verified AIQ_SERVER_URL produced by this skill to run deep research workflows. Hand off the URL only after validation passes.
