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jupyter-notebook 精选

Použijte, když uživatel požádá o vytvoření, inicializaci nebo úpravu

在 GitHub 打开

安装方式

cp -r translations/cs/.agents/skills/jupyter-notebook ~/.claude/skills/jupyter-notebook-5

把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/

整仓一次性获取:git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners.git

依赖与权限

  • 无需 API Key
  • 需要联网访问外部服务
  • 依赖外部命令:python pip uv
  • 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。

SKILL.md 正文

来源 microsoft/ai-agents-for-beginners@4c9a275 · 许可 MIT · 内容未修改

Dovednost Jupyter Notebook

Vytvářejte čisté, reprodukovatelné Jupyter notebooky pro dva hlavní režimy:

  • Experimenty a průzkumné analýzy
  • Tutoriály a výukové průvodce

Upřednostněte dodané šablony a pomocný skript pro konzistentní strukturu a méně chyb v JSONu.

Kdy použít

  • Vytvořte nový .ipynb notebook od začátku.
  • Převeďte hrubé poznámky nebo skripty do strukturovaného notebooku.
  • Přepracujte existující notebook tak, aby byl více reprodukovatelný a přehledný.
  • Vytvářejte experimenty nebo tutoriály, které budou čteny nebo znovu spouštěny jinými lidmi.

Rozhodovací strom

  • Pokud je požadavek průzkumný, analytický nebo založený na hypotéze, zvolte experiment.
  • Pokud je požadavek instruktážní, krok za krokem nebo zaměřený na konkrétní publikum, zvolte tutorial.
  • Při úpravě existujícího notebooku jej považujte za refaktorizaci: zachovejte záměr a vylepšete strukturu.

Cesta dovednosti (nastavit jednou)

export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export JUPYTER_NOTEBOOK_CLI="$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py"

User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).

Pracovní postup

  1. Lock the intent.

Identify the notebook kind: experiment or tutorial.

Capture the objective, audience, and what "done" looks like.

  1. Scaffold from the template.

Use the helper script to avoid hand-authoring raw notebook JSON.

uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
  --kind experiment \
  --title "Compare prompt variants" \
  --out output/jupyter-notebook/compare-prompt-variants.ipynb
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
  --kind tutorial \
  --title "Intro to embeddings" \
  --out output/jupyter-notebook/intro-to-embeddings.ipynb
  1. Fill the notebook with small, runnable steps.

Keep each code cell focused on one step.

Add short markdown cells that explain the purpose and expected result.

Avoid large, noisy outputs when a short summary works.

  1. Apply the right pattern.

For experiments, follow references/experiment-patterns.md.

For tutorials, follow references/tutorial-patterns.md.

  1. Edit safely when working with existing notebooks.

Preserve the notebook structure; avoid reordering cells unless it improves the top-to-bottom story.

Prefer targeted edits over full rewrites.

If you must edit raw JSON, review references/notebook-structure.md first.

  1. Validate the result.

Run the notebook top-to-bottom when the environment allows.

If execution is not possible, say so explicitly and call out how to validate locally.

Use the final pass checklist in references/quality-checklist.md.

Šablony a pomocný skript

  • Šablony se nacházejí v assets/experiment-template.ipynb a assets/tutorial-template.ipynb.
  • Pomocný skript načte šablonu, aktualizuje titulní buňku a zapíše notebook.

Script path:

  • $JUPYTER_NOTEBOOK_CLI (installed default: $CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py)

Konvence pro dočasné a výstupní soubory

  • Používejte tmp/jupyter-notebook/ pro mezisoubory; smažte je po dokončení.
  • Zapisujte finální artefakty do output/jupyter-notebook/ při práci v tomto repozitáři.
  • Používejte stabilní, popisné názvy souborů (například ablation-temperature.ipynb).

Závislosti (instalovat pouze pokud je potřeba)

Preferujte uv pro správu závislostí.

Volitelné Python balíčky pro lokální spuštění notebooku:

uv pip install jupyterlab ipykernel

The bundled scaffold script uses only the Python standard library and does not require extra dependencies.

Prostředí

Žádné povinné proměnné prostředí.

Referenční mapa

  • references/experiment-patterns.md: struktura experimentu a heuristiky.
  • references/tutorial-patterns.md: struktura tutoriálu a průběh výuky.
  • references/notebook-structure.md: tvar JSONu notebooku a pravidla bezpečných úprav.
  • references/quality-checklist.md: finální kontrolní seznam pro ověření.

<!-- CO-OP TRANSLATOR DISCLAIMER START -->

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Tento dokument byl přeložen pomocí AI překladatelské služby Co-op Translator (https://github.com/Azure/co-op-translator). I když usilujeme o co nejvyšší přesnost, vezměte prosím na vědomí, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za rozhodující zdroj. Pro zásadní informace doporučujeme využít profesionální lidský překlad. Nejsme odpovědní za žádná nedorozumění nebo nesprávné výklady vyplývající z použití tohoto překladu.

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质量与来源

所属仓库
microsoft/ai-agents-for-beginners
仓库星标
7.7 万
最近更新
今天
采集时间
今天
commit
4c9a27595226
内容指纹
sha256:2d47c5890fec255
文件路径
translations/cs/.agents/skills/jupyter-notebook/SKILL.md

同仓库其它 Skill

查看全部 24 个
azure-openai-to-responses
Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).
精选 microsoft/ai-agents-for-beginners 7.7 万 原文
Codex CLIMCP 客户端
deploying-scalable-agents
Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.
精选 microsoft/ai-agents-for-beginners 7.7 万 原文
Codex CLIMCP 客户端
jupyter-notebook
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
精选 microsoft/ai-agents-for-beginners 7.7 万 原文
Codex CLIMCP 客户端
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
精选 microsoft/ai-agents-for-beginners 7.7 万 原文
Codex CLIMCP 客户端
microsoft-docs
Query official Microsoft documentation to find concepts, tutorials, and code examples across Azure, .NET, Agent Framework, Aspire, VS Code, GitHub, and more. Uses Microsoft Learn MCP as the default, with Context7 and Aspire MCP for content that lives outside learn.microsoft.com.
精选 microsoft/ai-agents-for-beginners 7.7 万 原文
Codex CLIMCP 客户端
testing-course-samples
Use when asked to validate, test, smoke-test, or run the course's notebook and code samples against a live Microsoft Foundry / Azure OpenAI configuration. Covers environment setup (.env, az login, packages), the scripts/validate-notebooks.ps1 runner, interpreting PASS/FAIL results, and which lessons need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).
精选 microsoft/ai-agents-for-beginners 7.7 万 原文
Codex CLIMCP 客户端

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