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當使用者要求建立、搭建或編輯 Jupyter 筆記本（`.ipynb`）用於實驗、探索或教學時使用；優先使用隨附範本並執行輔助腳本 `new_notebook.py` 來產生一個乾淨的起始筆記本。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Shift Python apps dem from Azure OpenAI Chat Completions go Responses API. E cover AzureOpenAI/AsyncAzureOpenAI client shift go v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, plus model compatibility checks. Na Python-focused, Azure OpenAI-specific. USE FOR: shift go responses API, change from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions go responses, gpt-5 migration, azure openai python migration, chat completions go responses, AzureOpenAI go OpenAI client, python azure openai upgrade. DO NOT USE FOR: build new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (dis skill na Python-only), Azure infrastructure setup (use azure-prepare), deploy models (use microsoft-foundry).
Take one working agent prototype go scalable, observable production deployment for Microsoft Foundry. E cover 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 one agent go production, scale one 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 one 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 we no relate to agents, non-Foundry deployment targets.
Build local-first AI agents wey dey run fully for developer workstation wit Microsoft Foundry Local and Qwen function-calling models. E cover Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG wit Chroma, local MCP servers, hybrid cloud/local routing, and di privacy/cost/offline trade-offs. E based on Lesson 17 of AI Agents for Beginners. USE FOR: run 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 for my machine. DO NOT USE FOR: deploying agents to di 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.
Use wen dem ask to check, test, do smoke-test, or run di course notebook and code samples against live Microsoft Foundry / Azure OpenAI setup. E cover how to set environment (.env, az login, packages), di scripts/validate-notebooks.ps1 runner, how to understand PASS/FAIL results, and which lessons ~need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).
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).
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).
| name | jupyter-notebook |
| description | 當使用者要求建立、搭建或編輯 Jupyter 筆記本（`.ipynb`）用於實驗、探索或教學時使用；優先使用隨附範本並執行輔助腳本 `new_notebook.py` 來產生一個乾淨的起始筆記本。 |
為兩種主要模式建立乾淨、可重現的 Jupyter 筆記本：
優先使用內建的範本與輔助腳本，以維持一致的結構並減少 JSON 錯誤。
.ipynb 筆記本。experiment。tutorial。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 $CODEX_HOME/skills (default: ~/.codex/skills).
Lock the intent.
Identify the notebook kind: experiment or tutorial.
Capture the objective, audience, and what "done" looks like.
Scaffold from the template. Use the helper script to avoid hand-authoring raw notebook JSON.
uv run --python 3.12 python "$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
填充筆記本，使用小且可執行的步驟。 保持每個程式碼儲存格專注於單一步驟。 新增簡短的 markdown 儲存格來說明目的與預期結果。 當簡短摘要足夠時，避免產生大量嘈雜的輸出。
應用正確的模式。
對於實驗，請遵循 references/experiment-patterns.md。
對於教學，請遵循 references/tutorial-patterns.md。
在處理現有筆記本時安全地編輯。
保留筆記本結構；除非能改善從上到下的敘事流程，否則避免重新排序儲存格。
偏好有針對性的編輯而非全部重寫。
如果必須編輯原始 JSON，先檢閱 references/notebook-structure.md。
驗證結果。
在環境允許時，從上到下執行筆記本。
如果無法執行，請明確說明並指出如何在本機驗證。
使用 references/quality-checklist.md 中的最終檢查清單。
assets/experiment-template.ipynb 與 assets/tutorial-template.ipynb。腳本路徑：
$JUPYTER_NOTEBOOK_CLI (installed default: $CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py)tmp/jupyter-notebook/；完成後刪除。output/jupyter-notebook/。ablation-temperature.ipynb）。偏好使用 uv 進行相依性管理。
供本機筆記本執行的可選 Python 套件：
uv pip install jupyterlab ipykernel
The bundled scaffold script uses only the Python standard library and does not require extra dependencies.
沒有必需的環境變數。
references/experiment-patterns.md: 實驗結構與啟發式規則。references/tutorial-patterns.md: 教學結構與教學流程。references/notebook-structure.md: 筆記本 JSON 形態與安全編輯規則。references/quality-checklist.md: 最終驗證檢查清單。免責聲明： 本文件已透過 AI 翻譯服務 Co-op Translator (https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意自動翻譯可能包含錯誤或不準確之處。原始語言版本的文件應被視為具權威性的來源。對於重要資訊，建議採用專業人工翻譯。因使用本翻譯而產生的任何誤解或誤譯，我們不承擔任何責任。