/aiq-research

name: aiq-research
description: |
  Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
license: Apache-2.0
permissions:
  env:
    - AIQ_SERVER_URL
  network:
    - http://localhost:8000
compatibility: |
  Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools. Requires Python 3.11+ and network
  access to a running local AI-Q Blueprint server at `http://localhost:8000` by default. Non-local backends must be
  explicitly trusted by the user and granted by the host tool outside this public skill.
metadata:
  version: "2.1.0"
  author: "NVIDIA AI-Q Blueprint Team <aiq-blueprint@nvidia.com>"
  github-url: "https://github.com/NVIDIA-AI-Blueprints/aiq"
  tags:
    - nvidia
    - aiq
    - blueprint
    - deep-research
    - research-agents
    - agent-skills
  languages:
    - python
    - bash
  domain: "research-agents"
allowed-tools: Read Bash

AIQ Research Skill

Purpose

Use this skill to call a locally running NVIDIA AI-Q Blueprint server through the helper script at `scripts/aiq.py`.

Use this skill for research-shaped requests, including:

• "deep research on ..."
• "AIQ research ..."
• "research ..."
• "use AI-Q to answer ..."
• "ask AI-Q about ..."

Do not use this skill for install, deploy, start, stop, UI, CLI, Docker, Helm, or troubleshooting requests. Those belong to `aiq-deploy`.

Prerequisites

Users need:

• Python 3.11+ available as `python3`.
• A reachable local or self-hosted AI-Q Blueprint backend.
• `AIQ_SERVER_URL` set when the backend is not running at `http://localhost:8000`; non-local values must be trusted by

the user before any query is sent.

• A backend configured with authentication disabled for this public helper, or a separate authenticated AI-Q skill for

authenticated environments.

• Network access from the local machine to the AI-Q backend URL.
• Credentials configured in the backend environment, not in this skill. This public helper does not collect or manage

API keys.

The helper script has no third-party Python package dependencies; it uses Python standard-library HTTP modules.

Instructions

1. Resolve the target backend URL. 2. Run `health` before sending research requests. 3. If no backend is reachable, ask for a backend URL or hand off to `aiq-deploy`. 4. Before sending any user query, state the exact AI-Q backend URL that will receive it. For non-local URLs, continue only if the user has explicitly confirmed that URL is trusted in the current conversation. 5. Poll asynchronous deep research jobs when AI-Q returns a job ID. 6. Present returned reports with citations and source URLs intact. 7. Stop on failed jobs and show the returned error; do not retry automatically. 8. After presenting a report, support follow-up: answer questions about it (ask) or run a refined research pass (redo) using the same commands.

Step 1 - Resolve the backend

Use `AIQ_SERVER_URL` when set. Otherwise try the default local backend:

python3 $SKILL_DIR/scripts/aiq.py health

Expected output: JSON from a reachable AI-Q health endpoint.

If `health` fails and no explicit `AIQ_SERVER_URL` was set, ask:

I do not see a reachable local AI-Q backend. Do you already have an AI-Q backend URL you want to use, or should I deploy a local Skill backend?

• If the user provides a URL, set `AIQ_SERVER_URL` for subsequent helper calls and rerun `health`.
• If the user wants local deployment, hand off to `aiq-deploy` and preserve the original research request.
• If a reachable backend returns `401` or `403`, stop and explain that this public skill does not manage

authentication. Ask the user to use an authenticated AI-Q skill or configure authentication for their environment.

• If `health` succeeds but `/chat` or `/v1/jobs/async/agents` fails, report that the backend is reachable but not

compatible with this public research flow, then offer to run `aiq-deploy` validation.

Step 2 - Send the routed research request

Before sending the request, state the resolved endpoint:

I will send this query to <AIQ_SERVER_URL>. Make sure this endpoint is trusted before sending sensitive information.

Do not send credentials, cookies, bearer tokens, or secret values through the query text.

Run:

python3 $SKILL_DIR/scripts/aiq.py chat "<USER_QUESTION>"

Expected output:

• A normal JSON response for shallow or direct answers.
• Or structured JSON containing `{"status": "deep_research_running", "job_id": "<JOB_ID>"}` for asynchronous deep

research.

If the response is normal JSON, present the result immediately. Do not force polling when there is no `job_id`.

Step 3 - Poll asynchronous jobs

If the response includes `deep_research_running`, extract the `job_id` and poll with the same absolute script path:

python3 $SKILL_DIR/scripts/aiq.py research_poll <JOB_ID>

Expected output: the final report JSON when the job completes successfully.

Use the runtime's non-blocking or background execution mechanism when available. If the chosen execution method requires escalated permissions, request explicit user approval first and explain why. Tell the user that deep research is running in the background.

Step 4 - Resume after interruptions

If polling is interrupted, the job continues server-side. Resume with:

python3 $SKILL_DIR/scripts/aiq.py status <JOB_ID>
python3 $SKILL_DIR/scripts/aiq.py report <JOB_ID>
python3 $SKILL_DIR/scripts/aiq.py research_poll <JOB_ID>

Use `status` to inspect job status and saved artifacts. Use `report` when the job has already finished and you only need the final output. Use `research_poll` to keep waiting for completion.

The final report may reference generated artifacts (charts, CSVs) as `artifact://<id>` links. To materialize them as local files, run `python3 $SKILL_DIR/scripts/aiq.py artifacts <JOB_ID> --download-dir ./aiq-artifacts`; it downloads each artifact and prints the loca