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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/NVIDIA-AI-Blueprints/aiq --skill aiq-release-qa命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| 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.