NVIDIA-AI-Blueprints

Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.

Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.

Use when changing AI-Q continuous integration, pre-commit, or contributor governance — editing .github/workflows/ (ci, ui, skills-eval, request-nvskills-ci), .pre-commit-config.yaml hooks, .github/CODEOWNERS, .coderabbit.yaml, copy-pr-bot, or the .github/skill-eval harness — and validating those changes without breaking the gate.

Use when validating an AI-Q change before opening or merging a PR — choosing and running the right Python, frontend, docs, or eval checks for the surfaces you touched instead of one fixed command list.

Use when adding or changing an AI-Q data source under sources/, registering it as a NeMo Agent Toolkit function, wiring it into the data_source_registry for UI toggles, or validating retrieval behavior with tests.

Use when adding or changing a general-purpose AI-Q tool (a NeMo Agent Toolkit function) under sources/, defining its FunctionBaseConfig schema, registering it with @register_function, wiring it into an agent's tools list, or testing it.

Use when composing, adapting, or validating an AI-Q workflow YAML under configs/ — selecting a shipped profile, enabling tools and data_source_registry sources, wiring agents and the chat_deepresearcher_agent workflow, configuring general.telemetry (Phoenix, LangSmith, Weave, OTEL) and general.front_end aiq_api settings, and pre-flighting cross-references before deploy or serve. Hand off deploy to aiq-deploy, live research to aiq-research, prompt/model edits to aiq-customize-prompts-models, and new source code to aiq-add-tool or aiq-add-data-source.

Use when customizing AI-Q agent behavior through Jinja2 prompt templates or per-agent model selection — editing prompts under src/aiq_agent/agents/*/prompts/, adding template variables, or assigning/swapping LLMs per agent role via config (the llms section plus per-agent fields like orchestrator_llm, planner_llm, researcher_llm, writer_llm, source_router_llm).

Discoverability shim for the Retail-Agentic-Commerce CVE remediation Codex skill. Use for CVE, BDSA, GHSA, OSV, Dependabot, Black Duck, Snyk, npm, PyPI, or package advisory remediation.

Discoverability shim for the Retail-Agentic-Commerce backend feature Codex skill. Use when writing or modifying Python backend code, routes, services, models, tests, or protocol logic.

Discoverability shim for the Retail-Agentic-Commerce pre-commit analysis Codex skill. Use before commits, after significant changes, or for implementation-quality validation.

Discoverability shim for the Retail-Agentic-Commerce setup Codex skill. Use when the user asks to setup, install, start, launch, or run the project stack.

Discoverability shim for the Retail-Agentic-Commerce UI Codex skill. Use when creating or modifying frontend pages, components, hooks, styling, tests, or browser behavior.

Dependency vulnerability remediation workflow for this repository. Use when fixing CVE, BDSA, GHSA, OSV, npm, PyPI, Dependabot, Black Duck, Snyk, or other package vulnerability findings, especially when the user asks for minimal package-only updates, validation, commits, branches, or pull requests.

Python backend development standards for FastAPI, SQLModel, pytest, Ruff, and Pyright. Use when writing or modifying backend code in src/merchant, src/payment, src/apps_sdk, agents, tests, API routes, services, models, or protocol logic.

Pre-commit implementation analysis for Retail-Agentic-Commerce. Use before commits, after significant changes, or when validating Apps SDK tools, mock data, MCP communication, widget isolation, or implementation quality.

Start, stop, configure, inspect, and troubleshoot the Retail Shopping Assistant locally with backend Python and React code outside containers, local Milvus infra containers, and remote NIM endpoints from docker-compose-nim-local.yaml.

Run the Retail Shopping Assistant test suites under tests/, including offline pytest unit tests in tests/unit and live integration scripts in tests/integration for conversation, quality, timing, and plots.

Start, stop, configure, inspect, and troubleshoot the Retail Shopping Assistant locally with backend Python and React code outside containers, local Milvus infra containers, and remote NIM endpoints from docker-compose-nim-local.yaml.

Run the Retail Shopping Assistant test suites under tests/, including offline pytest unit tests in tests/unit and live integration scripts in tests/integration for conversation, quality, timing, and plots.

Configure Nemotron Voice Agent runtime via `.env`, example-local `services.{cloud,local}.yaml`, and example-local `prompts.yaml`. Use when changing prompts, tracing, audio knobs, exposed pipelines or transports, or local NIM image overrides.

Deploy Nemotron Voice Agent via root compose using recipe profiles. Use when deploying or troubleshooting auth/startup.

Upgrade the Nemotron Voice Agent to a new Pipecat (pipecat-ai) version. Reads release notes for every release in range, diffs old vs new, discovers every example pipeline and Pipecat call site, implements changes, then runs multi-agent gap analysis until clean. Generic across Pipecat versions.

Update the BDD test container image when its dependencies or runtime change. Determines version bump (major vs minor vs patch), rebuilds the Docker image, pushes to GitLab registry, and updates the image tag in start_test.sh. Test source (tests/, features/, scripts/, data/, conftest.py) is bind-mounted from the host repo at runtime, so pure test code changes do NOT require a container rebuild. Use only when files that the container bakes in are modified: Dockerfile, docker-entrypoint.sh, pyproject.toml, poetry.lock.

Generates VSS video summary reports with LVS HITL and optional Enterprise RAG document grounding. Trigger when the user asks for a frag/RAG-assisted video report, knowledge-enhanced analysis, or Enterprise RAG context in a video summary.

NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).

Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, Mean-Variance/SOCP variance caps, efficient frontiers, scenario generation, or NVIDIA cuOpt.