Aiq Research

AIQ Research Skill

When to Use

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

• Python 3.11+ available as python3 (or python on Windows PowerShell).
• 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.

Procedure

Step 1 — Resolve the backend URL

Use AIQ_SERVER_URL when set. Otherwise try the default local backend at http://localhost:8000.

Run a health check before sending any research request:

python $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 to the user:

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

Never send credentials, cookies, bearer tokens, or secret values through the query text.

Run:

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

Expected output:

• A normal JSON response for shallow or direct answers — present the result immediately. Do not force polling when there is no job_id.
• Or structured JSON containing {"status": "deep_research_running", "job_id": "<JOB_ID>"} for asynchronous deep research.

Step 3 — Poll asynchronous deep research jobs

If the response includes deep_research_running, extract the job_id and poll:

python $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 any of:

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

Step 5 — Present the report

When research_poll completes successfully, fetch and present the full report. Keep citations and source URLs intact — do not truncate or strip them.

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. Do not retry automatically.

Available Scripts

All commands are invoked via python $SKILL_DIR/scripts/aiq.py <subcommand>. When the host supports a run_script() helper, call it with scripts/aiq.py and the arguments below.

Environment Variables

Version Compatibility

This skill is designed for NVIDIA AI-Q Blueprint version 2.1.0.

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 2.1.0 is compatible with Blueprint 2.1.0, 2.2.0, and 2.1.5.
• Skill 2.1.0 is not compatible with Blueprint 3.0.0 or 2.0.0.

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.

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.

Pitfalls

No backend is reachable

Symptoms: health fails with connection refused; default http://localhost:8000 does not respond.

Causes: AI-Q is not running, is on a different host/port, or a firewall 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:

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

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

Backend requires authentication

Symptoms: Requests fail with HTTP 401 or 403; backend is reachable but rejects /chat or async job calls.

Causes: Backend 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.

Health succeeds but research routes fail

Symptoms: health returns successfully, but /chat, /v1/jobs/async/agents, or polling commands fail.

Causes: Backend not using an API-enabled AI-Q config; async job registry unavailable; backend version incompatible.

Solutions:

1. Run:

   python $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 2.1.x.

Job is interrupted or appears stuck

Symptoms: Local polling interrupted; 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:

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

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

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

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

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

Fabricating answers on backend failure

If the backend returns HTTP 500 or lacks async agents, report the failure to the user. Never fabricate a research answer when the backend is unreachable or returns an error.

Verification

1. Confirm the backend is reachable:

   python $SKILL_DIR/scripts/aiq.py health
   

   Expected: JSON health response with HTTP 200.

2. Confirm async agents are available:

   python $SKILL_DIR/scripts/aiq.py agents
   

   Expected: JSON listing available agent types.

3. Confirm a chat request returns either inline JSON or a deep_research_running job ID:

   python $SKILL_DIR/scripts/aiq.py chat "test query"
   

4. If a job ID was returned, confirm polling retrieves the final report:

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

   Expected: final report JSON with citations and source URLs intact.

Examples

Example 1: Run a routed chat or research request

python $SKILL_DIR/scripts/aiq.py health
python $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

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

References

Load scripts/aiq.py when executing any AI-Q command. Load ../aiq-deploy/SKILL.md only when handing off deployment, validation, or troubleshooting requests.

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.