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":"2.2.0","author":"NVIDIA AI-Q Blueprint Team <[e-mail]>","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

When to Use This Skill

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.

Workflow

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 local path. Do not expect base64 image data in the report itself.

For a self-contained, shareable report, run python3 $SKILL_DIR/scripts/aiq.py report <JOB_ID> --out-dir ./my-report. It writes report.md plus an artifacts/ folder and rewrites each artifact://<id> link to the matching local file, so the report renders (charts and all) in any markdown viewer without a running backend.

Step 5 - Present the report

When research_poll completes successfully, fetch and present the full report. Keep citations and source URLs intact. If the job status is failed, failure, or cancelled, show the error from the status response and ask whether the user wants to retry with a narrower query or different approach.

Step 6 - Follow up: ask about, edit, or redo a report

After a report is presented, the user often wants to go deeper or adjust scope. Reuse the existing backend flow — the same auth boundary, polling, and report retrieval from Steps 1-5 apply; there is no separate follow-up endpoint.

Ask — a follow-up question about a report already in hand:

• For a question answerable from the report you already have, answer directly from its content and citations; do not call the backend again.

• For a question that needs new investigation, send a fresh request that carries the needed context from the prior question and report into the new query text, then present the new result:

  python3 $SKILL_DIR/scripts/aiq.py chat "<FOLLOW_UP_QUESTION> (context: <PRIOR_TOPIC>)"
  

  If this returns a deep_research_running job ID, poll it with research_poll exactly as in Step 3.

Edit — rewrite a report with cosmetic changes. This skill only has access to the data used to generate the initial report. No tools are available:

python3 $SKILL_DIR/scripts/aiq.py report_edit <JOB_ID> "<EDIT_INSTRUCTIONS>"

Redo — re-run research with adjusted scope (a narrower query, a corrected question, or a different depth):

python3 $SKILL_DIR/scripts/aiq.py research "<REFINED_QUERY>" [agent_type]

• Choose agent_type to match the desired depth (for example a deep agent for a thorough pass, or shallow_researcher for a quick one); list options with agents if unsure.
• Treat a redo as a new job: state the target endpoint again before sending (Step 2), then poll and present as in Steps 3-5.

Do not send credentials or secret values in follow-up query text, and keep citations and source URLs intact in every follow-up answer.

Version Compatibility

IMPORTANT: This skill is designed for NVIDIA AI-Q Blueprint version 2.2.0.

Semantic Versioning Compatibility Rules:

Skill version: X.Y.Z
Blueprint or endpoint 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)

Examples:

• Skill version 2.2.0 is compatible with Blueprint version 2.2.0.
• Skill version 2.2.0 is compatible with Blueprint version 2.3.0.
• Skill version 2.2.0 is compatible with Blueprint version 2.2.5.
• Skill version 2.2.0 is not compatible with Blueprint version 3.0.0.
• Skill version 2.2.0 is not compatible with Blueprint version 2.1.0.

If your Blueprint version is not compatible:

1. Check for an updated skill version matching your Blueprint version.
2. Use a Blueprint version compatible with this skill.
3. Proceed with caution only when the user accepts the compatibility risk; API routes or response shapes may have changed.

Available Scripts

Script	Purpose	Arguments
scripts/aiq.py health	Check whether the configured server responds	none
scripts/aiq.py chat	POST /chat; may return inline output or a deep-research job ID	<query>
scripts/aiq.py agents	List available async agent types	none
scripts/aiq.py submit	Submit an explicit async job	<query> [agent_type]
scripts/aiq.py research	Submit an async job, poll, and print the final report JSON	<query> [agent_type]
scripts/aiq.py research_poll	Resume polling an existing async job	<job_id>
scripts/aiq.py status	Fetch job status plus /state artifacts	<job_id>
scripts/aiq.py state	Fetch event-store artifacts only	<job_id>
scripts/aiq.py report	Fetch the final report; with --out-dir DIR, export a portable report.md + artifacts/ folder with links rewritten to local files	<job_id> [--out-dir DIR]
scripts/aiq.py report_edit	Edit a completed report with cosmetic changes	<job_id> <edit_instructions>
scripts/aiq.py artifacts	List durable artifacts; with --download-dir DIR, download them and print local paths	<job_id> [--download-dir DIR]
scripts/aiq.py stream	Stream SSE events from a job	<job_id>
scripts/aiq.py cancel	Cancel a running job	<job_id>

When the host supports a run_script() helper, call it with scripts/aiq.py and the arguments above. Otherwise, run the equivalent shell command, such as python3 $SKILL_DIR/scripts/aiq.py health.

Environment Variables

Variable	Required	Default	Description
AIQ_SERVER_URL	No	http://localhost:8000	Local or self-hosted AI-Q server base URL

Security Best Practices

• Do not put API keys, bearer tokens, cookies, or basic-auth credentials in AIQ_SERVER_URL.
• Store backend credentials in the AI-Q deployment environment, not in this skill or command examples.
• User query text is transmitted to the configured AIQ_SERVER_URL. Confirm the endpoint is trusted before sending sensitive or confidential information.
• Treat returned reports as potentially sensitive if the backend uses private data sources.
• Do not truncate citations or source URLs from returned reports.

Limitations

• This skill requires a running AI-Q backend; it does not deploy one.
• The public helper does not manage authentication tokens or cookies.
• Remote AIQ_SERVER_URL endpoints may log prompts, responses, and metadata.
• If the backend returns HTTP 500 or lacks async agents, report the failure instead of fabricating a research answer.

Examples

Example 1: Run a routed chat or research request

python3 $SKILL_DIR/scripts/aiq.py health
python3 $SKILL_DIR/scripts/aiq.py chat "Compare local AIQ deep research with a standard web search workflow"

Expected output:

<health JSON from AI-Q>
<JSON chat response or {"status": "deep_research_running", "job_id": "<JOB_ID>"}>

If AI-Q returns a job ID, continue with research_poll.

Example 2: Resume an existing job

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

Replace <JOB_ID> with the UUID returned by AI-Q. Expected output: status JSON followed by the report JSON when the job completes. If the job failed, show the returned status and do not retry automatically.

Example 3: Ask a follow-up or redo with a refined query

# Ask: a follow-up that needs new investigation, carrying prior context.
python3 $SKILL_DIR/scripts/aiq.py chat "How does that compare on cost? (context: local AIQ deep research vs web search)"

# Redo: re-run research with a narrower query and explicit depth.
python3 $SKILL_DIR/scripts/aiq.py research "AIQ deep research cost on a single workstation" shallow_researcher

Expected output: a routed chat response or a new deep_research_running job ID to poll with research_poll. Present the follow-up answer with citations and source URLs intact.

References

Topic	Documentation
Helper script	scripts/aiq.py
Deployment and backend validation	../aiq-deploy/SKILL.md

Common Issues

Issue: No backend is reachable

Symptoms:

• health fails with connection refused.
• The default http://localhost:8000 URL does not respond.

Causes:

• AI-Q is not running.
• AI-Q is running on a different host or port.
• A local firewall or network setting blocks the connection.

Solutions:

1. Ask whether the user has an existing AI-Q backend URL.
2. If they provide one, set it and rerun health:

   export AIQ_SERVER_URL="http://localhost:<PORT>"
   python3 $SKILL_DIR/scripts/aiq.py health
   

3. If they want a local backend, hand off to aiq-deploy and preserve the original research request.

Issue: Backend requires authentication

Symptoms:

• Requests fail with HTTP 401 or HTTP 403.
• The backend is reachable but rejects /chat or async job calls.

Causes:

• The backend was deployed with authentication enabled.
• The public helper does not attach user tokens or cookies.

Solutions:

1. Stop and explain that this public skill does not manage authentication.
2. Ask the user to use an authenticated AI-Q skill or configure their backend for this public local workflow.
3. Rerun health and the original query only after the authentication boundary is resolved.

Issue: Health succeeds but research routes fail

Symptoms:

• health returns successfully.
• /chat, /v1/jobs/async/agents, or polling commands fail.

Causes:

• The backend is not using an API-enabled AI-Q config.
• The async job registry is not available in the selected backend.
• The backend version is incompatible with this skill.

Solutions:

1. Run:

   python3 $SKILL_DIR/scripts/aiq.py agents
   

2. If agents are unavailable, report the compatibility failure and offer to run aiq-deploy validation.
3. Confirm the deployed Blueprint version is compatible with skill version 2.2.0.

Issue: Job is interrupted or appears stuck

Symptoms:

• Local polling is interrupted.
• The job keeps showing running.
• Poll output shows running, but a report is returned or cancel says the job is already success.

Causes:

• Deep research is asynchronous and continues server-side.
• Local polling output can lag behind terminal server state.

Solutions:

1. Check current state:

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

2. If has_report: true or job_status.status: success, fetch the report:

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

3. If the job is still running, continue polling:

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