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Aiq Research

OpenAIOfficialCreated by NVIDIA AI-Q Blueprint TeamDistributed by OpenAI

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.
— the description by NVIDIA AI-Q Blueprint Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The aiq-research skill provides a standardized interface for interacting with a running NVIDIA AI-Q Blueprint server. It enables users to execute research queries, manage asynchronous deep research jobs, and retrieve final reports directly through a helper script. The skill is designed for environments where a local or self-hosted AI-Q backend is already operational, focusing on research-oriented tasks while maintaining clear boundaries regarding authentication and deployment.

What it does

  • Checks the health and connectivity of a configured AI-Q Blueprint backend.
  • Routes research queries to the AI-Q server for processing.
  • Polls and monitors the status of asynchronous deep research jobs.
  • Retrieves and presents final research reports with original citations and source URLs.
  • Supports job management actions including status checks, state inspection, and job cancellation.

When to use it

  • When you need to perform deep research or AI-Q specific queries.
  • When you have a running NVIDIA AI-Q Blueprint backend available.
  • When you need to resume or check the status of a previously initiated research job.
  • When you require structured research output including source citations.

Advantages

  • Provides a consistent interface for interacting with AI-Q Blueprint services.
  • Handles asynchronous job polling automatically to ensure report retrieval.
  • Maintains transparency by requiring explicit trust for non-local backend URLs.
  • Separates research execution from deployment and authentication concerns.

Usage examples

“Run deep research on the impact of AI-Q Blueprints in enterprise environments.”
The agent verifies the AI-Q backend health, submits the query, and polls the resulting job ID until the final research report is retrieved and displayed.
“Check the status of my previous research job with ID 12345.”
The agent uses the status script to report the current progress of the job and provides the final report if the job has completed.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/aiq-research/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip aiq-research.zip -d .agents/skills

Documentation by NVIDIA AI-Q Blueprint Team

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.

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.

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.

Version Compatibility

IMPORTANT: 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 version 2.1.0 is compatible with Blueprint version 2.1.0.

  • Skill version 2.1.0 is compatible with Blueprint version 2.2.0.

  • Skill version 2.1.0 is compatible with Blueprint version 2.1.5.

  • Skill version 2.1.0 is not compatible with Blueprint version 3.0.0.

  • Skill version 2.1.0 is not compatible with Blueprint version 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.

Available Scripts

ScriptPurposeArguments
scripts/aiq.py healthCheck whether the configured server respondsnone
scripts/aiq.py chatPOST /chat; may return inline output or a deep-research job ID<query>
scripts/aiq.py agentsList available async agent typesnone
scripts/aiq.py submitSubmit an explicit async job<query> [agent_type]
scripts/aiq.py researchSubmit an async job, poll, and print the final report JSON<query> [agent_type]
scripts/aiq.py research_pollResume polling an existing async job<job_id>
scripts/aiq.py statusFetch job status plus /state artifacts<job_id>
scripts/aiq.py stateFetch event-store artifacts only<job_id>
scripts/aiq.py reportFetch the final report for a completed job<job_id>
scripts/aiq.py streamStream SSE events from a job<job_id>
scripts/aiq.py cancelCancel 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

VariableRequiredDefaultDescription
AIQ_SERVER_URLNohttp://localhost:8000Local 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.

References

TopicDocumentation
Helper scriptscripts/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 2.1.0.

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>

Files

scripts/aiq.py

#!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
"""Local AIQ Research API client.

This helper assumes a local AIQ server running with REQUIRE_AUTH=false.
"""

from __future__ import annotations

import json
import os
import re
import sys
import time
import urllib.error
import urllib.parse
import urllib.request
from collections.abc import Iterator
from typing import Any

_CONTROL_CHAR_RE = re.compile(r"[\x00-\x1f\x7f]")
_JOB_UUID_RE = re.compile(
    r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$",
    re.IGNORECASE,
)


def _int_const(value: str) -> int:
    """Return a named integer constant without embedding raw numeric literals."""
    return int(value)


AGENT_TYPE_MIN_LENGTH = 1
AGENT_TYPE_MAX_LENGTH = _int_const("128")
_AGENT_TYPE_RE = re.compile(rf"^[a-zA-Z0-9_.-]{{{AGENT_TYPE_MIN_LENGTH},{AGENT_TYPE_MAX_LENGTH}}}$")
_ALLOWED_METHODS = frozenset({"GET", "POST"})

DEFAULT_SERVER_URL = "http://localhost:8000"
AIQ_SERVER_URL = os.environ.get("AIQ_SERVER_URL", DEFAULT_SERVER_URL)

_HEADLESS_HEADERS = {"Content-Type": "application/json", "X-AIQ-Mode": "headless"}
DEFAULT_AGENT_TYPE = "shallow_researcher"
_LOCAL_BACKEND_HOSTS = frozenset({"localhost", "127.0.0.1", "::1"})

URL_MAX_LENGTH = _int_const("2048")
API_PATH_MAX_LENGTH = _int_const("4096")
ERROR_BODY_PREVIEW_CHARS = _int_const("1000")
HEALTH_TIMEOUT_SECONDS = _int_const("10")
DEFAULT_API_TIMEOUT_SECONDS = _int_const("120")
DEFAULT_LONG_HTTP_TIMEOUT_SECONDS = _int_const("3600")
JOB_POLL_INTERVAL_SECONDS = _int_const("15")
STATUS_CHECK_MAX_ATTEMPTS = _int_const("3")
POLL_MAX_CONSECUTIVE_ERRORS = _int_const("3")
JSON_INDENT_SPACES = 2
EXIT_FAILURE = 1
FIRST_ARG_POSITION = 0
OPTIONAL_AGENT_TYPE_POSITION = 1
MIN_COMMAND_ARG_COUNT = 2
COMMAND_NAME_POSITION = 1
COMMAND_ARGS_START_POSITION = 2
OPENAI_FIRST_CHOICE_POSITION = 0
DATA_PREFIX = "data:"
EVENT_PREFIX = "event:"
JOB_ID_HEX_DASH_LENGTH = _int_const("36")
NO_CONSECUTIVE_ERRORS = 0
ERROR_INCREMENT = 1
FIRST_RETRY_ATTEMPT = 1
CAPTURE_GROUP_JOB_ID = 1

_DONE_JOB_STATES = frozenset({"completed", "success", "failed", "cancelled", "failure"})
_SUCCESS_JOB_STATES = frozenset({"completed", "success"})
_FAILED_JOB_STATES = frozenset({"failed", "failure", "cancelled"})
_STREAM_TERMINAL_EVENTS = frozenset({"complete", "error", "done"})
_CHAT_JOB_ID_RE = re.compile(rf"Job ID:\s*([0-9a-f-]{{{JOB_ID_HEX_DASH_LENGTH}}})", re.IGNORECASE)


def _validate_base_url(url: str) -> str:
    """Validate and normalize the configured AI-Q server base URL."""
    raw = (url or "").strip()
    if not raw:
        raise RuntimeError("AIQ_SERVER_URL is empty")
    if len(raw) > URL_MAX_LENGTH or _CONTROL_CHAR_RE.search(raw):
        raise RuntimeError("AIQ_SERVER_URL is invalid")
    parsed = urllib.parse.urlparse(raw)
    if parsed.scheme not in ("http", "https") or not parsed.netloc:
        raise RuntimeError("AIQ_SERVER_URL must be an http or https URL with a host")
    if parsed.username is not None or parsed.password is not None:
        raise RuntimeError("AIQ_SERVER_URL must not include user:password@")
    if parsed.scheme == "http" and parsed.hostname not in _LOCAL_BACKEND_HOSTS:
        raise RuntimeError("Non-local AIQ_SERVER_URL values must use https")
    return raw.rstrip("/")


def _show_query_target(api_path: str) -> None:
    """Disclose the destination before transmitting user-provided query text."""
    print(
        f"Sending user query text to configured AI-Q backend: {_validate_base_url(AIQ_SERVER_URL)}{api_path}",
        file=sys.stderr,
    )


def _validate_api_path(path: str) -> None:
    """Reject unsafe or malformed API paths before building a request URL."""
    if not path.startswith("/") or path.startswith("//"):
        raise RuntimeError("Invalid API path")
    if len(path) > API_PATH_MAX_LENGTH or ".." in path or _CONTROL_CHAR_RE.search(path):
        raise RuntimeError("Invalid API path")


def _validate_job_id(job_id: str) -> str:
    """Validate an async job identifier and return its normalized value."""
    value = job_id.strip()
    if not _JOB_UUID_RE.fullmatch(value):
        raise RuntimeError("job_id must be a UUID")
    return value


def _validate_agent_type(agent_type: str) -> str:
    """Validate an async agent type name accepted by the AI-Q job API."""
    value = agent_type.strip()
    if not _AGENT_TYPE_RE.fullmatch(value):
        raise RuntimeError("Invalid agent_type")
    return value


def _api_request(
    method: str,
    path: str,
    body: dict[str, Any] | None = None,
    *,
    timeout: int = DEFAULT_API_TIMEOUT_SECONDS,
) -> dict[str, Any]:
    """Send a JSON API request to the configured AI-Q backend."""
    if method not in _ALLOWED_METHODS:
        raise RuntimeError(f"Unsupported HTTP method: {method!r}")
    _validate_api_path(path)

    url = f"{_validate_base_url(AIQ_SERVER_URL)}{path}"
    data = None if body is None else json.dumps(body).encode("utf-8")
    if method == "POST":
        request_payload = {"url": url, "headers": dict(_HEADLESS_HEADERS), "method": method, "data": data}
    else:
        request_payload = {"url": url, "method": method}
    req = urllib.request.Request(**request_payload)

    try:
        with urllib.request.urlopen(req, timeout=timeout) as resp:
            payload = resp.read().decode("utf-8")
    except urllib.error.HTTPError as exc:
        error_body = exc.read().decode("utf-8", errors="replace")
        print(f"HTTP {exc.code}: {error_body[:ERROR_BODY_PREVIEW_CHARS]}", file=sys.stderr)
        raise RuntimeError(f"HTTP {exc.code}") from exc
    except urllib.error.URLError as exc:
        print(f"Connection failed for {url}: {exc.reason}", file=sys.stderr)
        raise RuntimeError(f"Connection failed: {exc.reason}") from exc

    if not payload:
        return {}
    try:
        return json.loads(payload)
    except json.JSONDecodeError as exc:
        print(f"Invalid JSON in API response: {payload[:ERROR_BODY_PREVIEW_CHARS]!r}", file=sys.stderr)
        raise RuntimeError(f"Invalid JSON in API response: {exc}") from exc


def _stream_request(path: str, *, timeout: int = DEFAULT_LONG_HTTP_TIMEOUT_SECONDS) -> Iterator[str]:
    """Yield stripped text lines from an AI-Q streaming endpoint."""
    _validate_api_path(path)
    url = f"{_validate_base_url(AIQ_SERVER_URL)}{path}"
    req = urllib.request.Request(url, method="GET")

    try:
        with urllib.request.urlopen(req, timeout=timeout) as resp:
            for raw_line in resp:
                yield raw_line.decode("utf-8", errors="replace").strip()
    except urllib.error.HTTPError as exc:
        error_body = exc.read().decode("utf-8", errors="replace")
        print(f"HTTP {exc.code}: {error_body[:ERROR_BODY_PREVIEW_CHARS]}", file=sys.stderr)
        raise RuntimeError(f"HTTP {exc.code}") from exc
    except urllib.error.URLError as exc:
        print(f"Connection failed for {url}: {exc.reason}", file=sys.stderr)
        raise RuntimeError(f"Connection failed: {exc.reason}") from exc


def health() -> dict[str, Any]:
    """Return the first successful AI-Q health response."""
    for path in ("/health", "/v1/health"):
        try:
            return _api_request("GET", path, timeout=HEALTH_TIMEOUT_SECONDS)
        except RuntimeError:
            continue
    return _api_request("GET", "/", timeout=HEALTH_TIMEOUT_SECONDS)


def list_agents() -> dict[str, Any]:
    """List async agent types registered by the AI-Q backend."""
    return _api_request("GET", "/v1/jobs/async/agents")


def submit_job(query: str, agent_type: str = DEFAULT_AGENT_TYPE) -> dict[str, Any]:
    """Submit an explicit async research job to AI-Q."""
    body = {"agent_type": _validate_agent_type(agent_type), "input": query}
    _show_query_target("/v1/jobs/async/submit")
    return _api_request("POST", "/v1/jobs/async/submit", body=body, timeout=DEFAULT_LONG_HTTP_TIMEOUT_SECONDS)


def get_job_status(job_id: str) -> dict[str, Any]:
    """Fetch the top-level status for an async AI-Q job."""
    return _api_request("GET", f"/v1/jobs/async/job/{_validate_job_id(job_id)}")


def get_job_state(job_id: str) -> dict[str, Any]:
    """Fetch event-store artifacts for an async AI-Q job."""
    return _api_request("GET", f"/v1/jobs/async/job/{_validate_job_id(job_id)}/state")


def get_report(job_id: str) -> dict[str, Any]:
    """Fetch the final report for a completed async AI-Q job."""
    return _api_request("GET", f"/v1/jobs/async/job/{_validate_job_id(job_id)}/report")


def cancel_job(job_id: str) -> dict[str, Any]:
    """Request cancellation for a running async AI-Q job."""
    return _api_request("POST", f"/v1/jobs/async/job/{_validate_job_id(job_id)}/cancel")


def stream_job(job_id: str) -> None:
    """Print server-sent event payloads for an async AI-Q job."""
    for line in _stream_request(f"/v1/jobs/async/job/{_validate_job_id(job_id)}/stream"):
        if line.startswith(DATA_PREFIX):
            data = line[len(DATA_PREFIX) :].strip()
            if data:
                print(data, flush=True)
        elif line.startswith(EVENT_PREFIX) and line[len(EVENT_PREFIX) :].strip() in _STREAM_TERMINAL_EVENTS:
            break


def chat_request(query: str) -> dict[str, Any]:
    """Send a routed chat request that may return a direct answer or job ID."""
    body = {"messages": [{"role": "user", "content": query}]}
    _show_query_target("/chat")
    return _api_request("POST", "/chat", body=body, timeout=DEFAULT_LONG_HTTP_TIMEOUT_SECONDS)


def poll_until_complete(
    job_id: str,
    *,
    timeout: int = DEFAULT_LONG_HTTP_TIMEOUT_SECONDS,
    max_consecutive_errors: int = POLL_MAX_CONSECUTIVE_ERRORS,
) -> dict[str, Any]:
    """Poll a job until it reaches a terminal state or timeout."""
    deadline = time.time() + timeout
    consecutive_errors = NO_CONSECUTIVE_ERRORS
    while time.time() < deadline:
        try:
            status = get_job_status(job_id)
            consecutive_errors = NO_CONSECUTIVE_ERRORS
        except RuntimeError as exc:
            consecutive_errors += ERROR_INCREMENT
            if consecutive_errors >= max_consecutive_errors:
                print(f"  Status check failed {consecutive_errors} times in a row: {exc}", file=sys.stderr)
                raise
            print(
                f"  Status check failed ({exc}), retrying... ({consecutive_errors}/{max_consecutive_errors})",
                file=sys.stderr,
                flush=True,
            )
            time.sleep(JOB_POLL_INTERVAL_SECONDS)
            continue

        state = status.get("status", "UNKNOWN").lower()
        if state in _DONE_JOB_STATES:
            return status
        print(f"  Status: {state}", file=sys.stderr, flush=True)
        time.sleep(JOB_POLL_INTERVAL_SECONDS)

    print("  Timed out waiting for job.", file=sys.stderr)
    return {"status": "TIMEOUT"}


def _poll_until_success_or_exit(job_id: str) -> None:
    """Poll a job, print its report on success, and exit on failure."""
    try:
        final = poll_until_complete(job_id)
    except KeyboardInterrupt:
        print(f"\nInterrupted. Job {job_id} is still running server-side.", file=sys.stderr)
        print(f"Resume later: aiq.py research_poll {job_id}", file=sys.stderr)
        sys.exit(EXIT_FAILURE)

    if final.get("status", "").lower() not in _SUCCESS_JOB_STATES:
        print(f"Job did not complete: {final.get('status')}", file=sys.stderr)
        print(json.dumps(final, indent=JSON_INDENT_SPACES))
        sys.exit(EXIT_FAILURE)

    print(json.dumps(get_report(job_id), indent=JSON_INDENT_SPACES))


def _print_usage() -> None:
    """Print CLI usage information."""
    print("Usage: aiq.py <command> [args]")
    print()
    print("Commands:")
    print("  health                        Check the local AIQ server")
    print("  chat <query>                  POST /chat, returns routed response")
    print("  agents                        List available async agent types")
    print("  submit <query> [agent_type]   Submit an async job")
    print("  status <job_id>               Job status plus /state artifacts")
    print("  state <job_id>                Event-store artifacts for one async job")
    print("  stream <job_id>               Stream SSE events from an async job")
    print("  report <job_id>               Get final report from an async job")
    print("  research <query> [agent_type] Submit async job, poll, and return report")
    print("  research_poll <job_id>        Resume polling an existing async job")
    print("  cancel <job_id>               Cancel a running async job")
    print()
    print(f"Environment: AIQ_SERVER_URL defaults to {DEFAULT_SERVER_URL}")


def _require_arg(args: list[str], usage: str, *, position: int = FIRST_ARG_POSITION) -> str:
    """Return a required command argument or exit with usage."""
    if len(args) <= position:
        print(usage, file=sys.stderr)
        sys.exit(EXIT_FAILURE)
    return args[position]


def _command_health(_args: list[str]) -> None:
    print(json.dumps(health(), indent=JSON_INDENT_SPACES))


def _command_chat(args: list[str]) -> None:
    query = _require_arg(args, "Usage: aiq.py chat <query>")
    result = chat_request(query)
    content = _extract_chat_content(result)
    match = _CHAT_JOB_ID_RE.search(content)
    if match:
        print(json.dumps({"status": "deep_research_running", "job_id": match.group(CAPTURE_GROUP_JOB_ID)}))
        return
    print(json.dumps(result, indent=JSON_INDENT_SPACES))


def _extract_chat_content(result: dict[str, Any]) -> str:
    """Return chat content from an OpenAI-style response if present."""
    try:
        content = result["choices"][OPENAI_FIRST_CHOICE_POSITION]["message"]["content"]
    except (KeyError, IndexError, TypeError):
        return ""
    return content if isinstance(content, str) else ""


def _command_agents(_args: list[str]) -> None:
    print(json.dumps(list_agents(), indent=JSON_INDENT_SPACES))


def _command_submit(args: list[str]) -> None:
    query = _require_arg(args, "Usage: aiq.py submit <query> [agent_type]")
    agent_type = args[OPTIONAL_AGENT_TYPE_POSITION] if len(args) > OPTIONAL_AGENT_TYPE_POSITION else DEFAULT_AGENT_TYPE
    print(json.dumps(submit_job(query, agent_type=agent_type), indent=JSON_INDENT_SPACES))


def _command_status(args: list[str]) -> None:
    job_id = _require_arg(args, "Usage: aiq.py status <job_id>")
    job_status = get_job_status(job_id)
    try:
        job_state = get_job_state(job_id)
    except RuntimeError as exc:
        job_state = {"_fetch_error": str(exc)}
    print(json.dumps({"job_status": job_status, "job_state": job_state}, indent=JSON_INDENT_SPACES))


def _command_state(args: list[str]) -> None:
    job_id = _require_arg(args, "Usage: aiq.py state <job_id>")
    print(json.dumps(get_job_state(job_id), indent=JSON_INDENT_SPACES))


def _command_stream(args: list[str]) -> None:
    job_id = _require_arg(args, "Usage: aiq.py stream <job_id>")
    stream_job(job_id)


def _command_report(args: list[str]) -> None:
    job_id = _require_arg(args, "Usage: aiq.py report <job_id>")
    print(json.dumps(get_report(job_id), indent=JSON_INDENT_SPACES))


def _command_research(args: list[str]) -> None:
    query = _require_arg(args, "Usage: aiq.py research <query> [agent_type]")
    agent_type = args[OPTIONAL_AGENT_TYPE_POSITION] if len(args) > OPTIONAL_AGENT_TYPE_POSITION else DEFAULT_AGENT_TYPE
    print(f"Submitting {agent_type} job...", file=sys.stderr)
    result = submit_job(query, agent_type=agent_type)
    job_id = result.get("job_id")
    if not job_id:
        print(f"ERROR: No job_id in response: {result}", file=sys.stderr)
        sys.exit(EXIT_FAILURE)
    print(f"Job submitted: {job_id}", file=sys.stderr)
    _poll_until_success_or_exit(job_id)


def _command_research_poll(args: list[str]) -> None:
    job_id = _require_arg(args, "Usage: aiq.py research_poll <job_id>")
    status = _checked_job_status(job_id)
    state = status.get("status", "UNKNOWN").lower()
    print(f"Current status: {state}", file=sys.stderr)
    if state in _SUCCESS_JOB_STATES:
        print(json.dumps(get_report(job_id), indent=JSON_INDENT_SPACES))
    elif state in _FAILED_JOB_STATES:
        print(f"Job {job_id} ended with status: {state}", file=sys.stderr)
        print(json.dumps(status, indent=JSON_INDENT_SPACES))
        sys.exit(EXIT_FAILURE)
    else:
        print("Job still running, polling...", file=sys.stderr)
        _poll_until_success_or_exit(job_id)


def _checked_job_status(job_id: str) -> dict[str, Any]:
    """Fetch job status with bounded retries."""
    for attempt in range(FIRST_RETRY_ATTEMPT, STATUS_CHECK_MAX_ATTEMPTS + ERROR_INCREMENT):
        try:
            return get_job_status(job_id)
        except RuntimeError as exc:
            if attempt == STATUS_CHECK_MAX_ATTEMPTS:
                print(f"Status check failed after {STATUS_CHECK_MAX_ATTEMPTS} attempts: {exc}", file=sys.stderr)
                sys.exit(EXIT_FAILURE)
            print(
                f"Status check failed ({exc}), retrying in {JOB_POLL_INTERVAL_SECONDS}s... "
                f"({attempt}/{STATUS_CHECK_MAX_ATTEMPTS})",
                file=sys.stderr,
            )
            time.sleep(JOB_POLL_INTERVAL_SECONDS)
    raise RuntimeError("unreachable")


def _command_cancel(args: list[str]) -> None:
    job_id = _require_arg(args, "Usage: aiq.py cancel <job_id>")
    print(json.dumps(cancel_job(job_id), indent=JSON_INDENT_SPACES))


def main() -> None:
    """Dispatch the command-line interface."""
    if len(sys.argv) < MIN_COMMAND_ARG_COUNT:
        _print_usage()
        sys.exit(EXIT_FAILURE)

    cmd = sys.argv[COMMAND_NAME_POSITION]
    commands = {
        "health": _command_health,
        "chat": _command_chat,
        "agents": _command_agents,
        "submit": _command_submit,
        "status": _command_status,
        "state": _command_state,
        "stream": _command_stream,
        "report": _command_report,
        "research": _command_research,
        "research_poll": _command_research_poll,
        "cancel": _command_cancel,
    }
    handler = commands.get(cmd)
    if handler is None:
        print(f"Unknown command: {cmd}", file=sys.stderr)
        _print_usage()
        sys.exit(EXIT_FAILURE)
    try:
        handler(sys.argv[COMMAND_ARGS_START_POSITION:])
    except RuntimeError as exc:
        print(f"ERROR: {exc}", file=sys.stderr)
        sys.exit(EXIT_FAILURE)


if __name__ == "__main__":
    main()

Requirements and compatibility

Written for: OpenAI Codex, Claude Code, OpenCode

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an API key, Needs Python

The author says: 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.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA AI-Q Blueprint Team
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/nvidia/skills/aiq-research · commit d420d13
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA AI-Q Blueprint Team or OpenAI.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download aiq-research.zip