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Use when validating an AI-Q change before opening or merging a PR — choosing and running the right Python, frontend, docs, or eval checks for the surfaces you touched instead of one fixed command list.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/nvidia-ai-blueprints/aiq --skill aiq-release-qaコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
| name | aiq-release-qa |
| description | Use when validating an AI-Q change before opening or merging a PR — choosing and running the right Python, frontend, docs, or eval checks for the surfaces you touched instead of one fixed command list. |
| license | Apache-2.0 |
| compatibility | Claude Code, Codex, Cursor, OpenCode, and Agent Skills-compatible tools. |
| metadata | {"version":"0.1.0","source-repo":"NVIDIA-AI-Blueprints/aiq","tags":"aiq nemo-agent-toolkit validation testing release-qa"} |
| allowed-tools | Read Bash |
Use this skill to validate a change in the AI-Q repository before opening or merging a pull request. The goal is to run the narrowest set of checks that covers what you touched, then broaden only when a change crosses shared boundaries — not to run every command every time.
src/,
sources/, tests/), web UI (frontends/ui/), docs (docs/), or evals
(frontends/benchmarks/).src/aiq_agent/common/ or a config many agents load).aiq-prepare-pr requires this
as validation evidence.deploy/.env values into command output you share.pyproject.toml and uv.lock: root Ruff config (line length 120, rule sets
E,F,W,I,PL,UP) and the root dev environment.mcp/pyproject.toml and mcp/uv.lock: the independent MCP runtime and dev
environment. MCP is not a root dependency group.frontends/ui/package.json: the real scripts (lint, type-check,
test:ci, build) — use these names, do not invent npm scripts.For the full per-surface command list and expected results:
git status, git diff --name-only) and map them to
surfaces.Pick the block that matches the change. Run the narrowest first.
Backend Python (run from the repo root; the project uses uv):
uv run ruff check <changed paths> # lint
uv run ruff format --check <changed paths> # format check
uv run pytest <scoped test paths> # tests
MCP server (run from the repo root):
uv sync --project mcp --extra dev
uv run ruff check mcp
uv run ruff format --check mcp
uv run --project mcp --extra dev pytest mcp/tests
Broaden to the whole tree when the change crosses shared boundaries:
uv run ruff check .
uv run ruff format --check .
uv run pytest
uv run --project mcp --extra dev pytest mcp/tests
Frontend (from frontends/ui/):
npm run lint
npm run type-check
npm run test:ci
Expected: Ruff reports no lint or format failures, tests pass, and the frontend lint/type-check/test commands exit cleanly. For docs and eval changes, see the matrix reference.
uv run --project mcp so the
check consumes the isolated MCP lock and dev extra.ruff format --check and pushing unformatted code that fails CI.nat eval needs deploy/.env; run evals via
dotenv -f deploy/.env run nat eval ... (see the matrix reference).lint, type-check,
test:ci, and build defined in frontends/ui/package.json.aiq-prepare-praiq-add-toolaiq-add-data-sourceUse when composing, adapting, or validating an AI-Q workflow YAML under configs/ — selecting a shipped profile, enabling tools and data_source_registry sources, wiring chat or direct data-science workflows, configuring NeMo Relay and general.telemetry observability plus general.front_end aiq_api settings, and pre-flighting cross-references before deploy or serve. Hand off deploy to aiq-deploy, live research to aiq-research, prompt/model edits to aiq-customize-prompts-models, and new source code to aiq-add-tool or aiq-add-data-source.
Use this skill to turn researched or computed numeric data into source-grounded charts. It has two delivery modes and picks one from the tools available to you. When an `execute` tool (sandbox) is available, render a PNG chart plus its CSV with Python/matplotlib and embed it as a durable `artifact://` reference. When there is no sandbox, emit the chart as an inline fenced `chart` (or `chart-carousel`) JSON spec that the web UI renders deterministically, followed by a portable Markdown table. Triggers: "chart", "plot", "graph", "bar chart", "line chart", "visualize", "trend over time", "compare visually", "figure", "ranking", "top-N", "distribution". Outputs: either a PNG chart artifact (plus CSV and manifest) or an inline chart spec.
Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.