What does this skill do, and when should you use it?
AI-Q Research is one skill in NVIDIA’s skills catalog for research-shaped requests. It uses scripts/aiq.py to call a reachable NVIDIA AI-Q Blueprint backend and supports both inline responses and asynchronous research jobs. The workflow checks backend health, sends the query, polls jobs when needed, and presents reports with citations and source URLs intact. It is intended for users who already operate an AI-Q backend; it does not deploy the backend or manage authentication credentials.
Resolves AIQ_SERVER_URL, defaulting to http://localhost:8000; runs a health check; sends research queries through /chat; extracts and polls asynchronous job IDs; retrieves job status, reports, and durable artifacts; can export a portable report.md with local artifacts; and preserves citations and source URLs in returned reports.
- A researcher with a running NVIDIA AI-Q Blueprint backend wants to run a deep-research request.
- A developer wants to ask AI-Q a research question or compare approaches through a local service.
- A user needs to resume an interrupted asynchronous research job and retrieve its final report.
- A user wants to export a research report and associated charts or CSV artifacts for local sharing.
How do you install this skill?
- Confirm AIQ_SERVER_URL before sending queries; remote endpoints may log prompts, responses, and metadata, so do not send confidential information.
- The public helper relies on a backend with authentication disabled and is not suitable by default for identity-protected or strongly isolated data sources.
- Verify the deployed AI-Q Blueprint version against the 2.1.0 compatibility rules and review returned reports and citations manually.
- Chinese-language support and mainland-China network reachability are not established by the source files.
- Shell / CLI
- Network access
- Local filesystem
Python 3.11+运行中的 NVIDIA AI-Q Blueprint 后端
Use the repository README’s supported command: npx skills add nvidia/skills --skill aiq-research --yes. To target Codex, use npx skills add nvidia/skills --skill aiq-research --agent codex --yes. The README does not document a manual destination path for copying this skill alone.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/NVIDIA/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/aiq-research" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- deep research on ...
Provide Python 3.11+ as python3 and run a reachable AI-Q Blueprint backend. Without AIQ_SERVER_URL, the skill uses http://localhost:8000; remote URLs require explicit user trust confirmation. Run python3 $SKILL_DIR/scripts/aiq.py health first, then python3 $SKILL_DIR/scripts/aiq.py chat "deep research on ...". If the response contains a job ID, run python3 $SKILL_DIR/scripts/aiq.py research_poll <JOB_ID>; after completion, use report <JOB_ID> to retrieve the report.
What are this skill's strengths and limitations?
- Supports both inline answers and asynchronous deep-research jobs.
- Includes health, status, report, and artifact-management commands for recovery and retrieval.
- Has no third-party Python package dependencies because it uses Python standard-library HTTP modules.
- Designed for Claude Code, OpenCode, Codex, and Agent Skills-compatible tools.
- Requires a running and reachable AI-Q Blueprint backend.
- The public helper does not manage API keys, bearer tokens, cookies, or other authentication.
- Authenticated backends require a separate authenticated AI-Q skill or an environment configured for this public flow.
- Remote AIQ_SERVER_URL endpoints may log prompts, responses, and metadata; backend deployment is outside this skill’s scope.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| NVIDIA AI-Q Deep Research this page ✓ NVIDIA · Official | 55 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Exa Deep Research Orchestrator | 57 · Use with care | ★ 5.2k | 1d ago | MIT |
| DICOM Series Preflight ✓ NVIDIA · Official | 60 · Recommended | ★ 3.8k | today | Apache-2.0 |
| DeepTutor XLSX Skill | 60 · Recommended | ★ 50k | 2d ago | Apache-2.0 |
| Nature Literature Downloader | 59 · Recommended | ★ 51k | 2d ago | Apache-2.0 |
How did FollowSkills review this skill?
The evidence shows a single declared environment variable, Read/Bash as allowed tools, localhost as the default network target, and explicit disclosure and confirmation before sending queries to non-local endpoints. The script rejects embedded credentials, restricts remote HTTP, and warns that queries, reports, and metadata may be logged. Points are deducted because the public helper requires an unauthenticated backend and sensitive-data consent, retention controls, and recovery remain largely dependent on the host and backend.
The documentation and script cover health checks, requests, asynchronous polling, stop-on-failure behavior, resume guidance, identifier validation, and diagnostic errors, with no third-party Python dependencies. Points are deducted because this review is static and did not execute key paths; compatibility depends on an external AI-Q Blueprint version and available routes, so end-to-end stability is unverified.
Trigger phrases, research use cases, explicit non-fit deployment/troubleshooting cases, and the input/output flow are reasonably clear, with support for local or self-hosted backends. Points are deducted because a running AI-Q service is mandatory, Chinese-language behavior is not specified, and reachability and mainland-China network fit lack evidence.
SKILL.md provides progressive sections, prerequisites, command tables, version-compatibility rules, security guidance, limitations, examples, and troubleshooting. It identifies Apache-2.0, version 2.1.0, an author, and an upstream repository; repository material also supplies a benchmark report and sync-based maintenance context. Points are deducted for no skill-specific changelog, no clearly assigned maintenance owner or formal update procedure, and NOASSERTION license metadata.
The core outcome is clear: route research queries to the AI-Q backend, handle synchronous or asynchronous results, and preserve citations, source URLs, and downloadable artifacts; repository benchmark material reports limited correctness and effectiveness results. Points are deducted because no independently reproducible output is available in this static review, result quality depends on the external backend, and reports still require human verification.
The source code, command interface, error branches, evaluation tasks, and benchmark report provide auditable material. Points are deducted because the benchmark is an in-repository report rather than independently verifiable CI or test evidence, this review did not execute the skill, and there is no corroborating third-party evidence.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →