ddadmin
0009270c6e
- Зачем: - расширены возможности AI-ассистентов для работы с документами и презентациями - Что: - добавлен skill pdf для работы с PDF файлами - добавлен skill doc для работы с Word документами - добавлен skill slides для создания презентаций PowerPoint - добавлен skill jupyter-notebook для работы с Jupyter ноутбуками - добавлен skill spreadsheet для работы с таблицами (Excel/CSV) - изменена модель Claude с sonnet на opus - Проверка: - проверка доступности skills в AI-ассистентах
108 lines
4.1 KiB
Markdown
108 lines
4.1 KiB
Markdown
---
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name: "jupyter-notebook"
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description: "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."
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---
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# Jupyter Notebook Skill
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Create clean, reproducible Jupyter notebooks for two primary modes:
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- Experiments and exploratory analysis
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- Tutorials and teaching-oriented walkthroughs
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Prefer the bundled templates and the helper script for consistent structure and fewer JSON mistakes.
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## When to use
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- Create a new `.ipynb` notebook from scratch.
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- Convert rough notes or scripts into a structured notebook.
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- Refactor an existing notebook to be more reproducible and skimmable.
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- Build experiments or tutorials that will be read or re-run by other people.
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## Decision tree
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- If the request is exploratory, analytical, or hypothesis-driven, choose `experiment`.
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- If the request is instructional, step-by-step, or audience-specific, choose `tutorial`.
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- If editing an existing notebook, treat it as a refactor: preserve intent and improve structure.
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## Skill path (set once)
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```bash
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export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
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export JUPYTER_NOTEBOOK_CLI="$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py"
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```
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User-scoped skills install under `$CODEX_HOME/skills` (default: `~/.codex/skills`).
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## Workflow
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1. Lock the intent.
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Identify the notebook kind: `experiment` or `tutorial`.
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Capture the objective, audience, and what "done" looks like.
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2. Scaffold from the template.
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Use the helper script to avoid hand-authoring raw notebook JSON.
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```bash
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uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
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--kind experiment \
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--title "Compare prompt variants" \
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--out output/jupyter-notebook/compare-prompt-variants.ipynb
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```
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```bash
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uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
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--kind tutorial \
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--title "Intro to embeddings" \
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--out output/jupyter-notebook/intro-to-embeddings.ipynb
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```
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3. Fill the notebook with small, runnable steps.
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Keep each code cell focused on one step.
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Add short markdown cells that explain the purpose and expected result.
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Avoid large, noisy outputs when a short summary works.
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4. Apply the right pattern.
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For experiments, follow `references/experiment-patterns.md`.
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For tutorials, follow `references/tutorial-patterns.md`.
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5. Edit safely when working with existing notebooks.
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Preserve the notebook structure; avoid reordering cells unless it improves the top-to-bottom story.
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Prefer targeted edits over full rewrites.
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If you must edit raw JSON, review `references/notebook-structure.md` first.
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6. Validate the result.
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Run the notebook top-to-bottom when the environment allows.
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If execution is not possible, say so explicitly and call out how to validate locally.
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Use the final pass checklist in `references/quality-checklist.md`.
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## Templates and helper script
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- Templates live in `assets/experiment-template.ipynb` and `assets/tutorial-template.ipynb`.
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- The helper script loads a template, updates the title cell, and writes a notebook.
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Script path:
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- `$JUPYTER_NOTEBOOK_CLI` (installed default: `$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py`)
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## Temp and output conventions
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- Use `tmp/jupyter-notebook/` for intermediate files; delete when done.
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- Write final artifacts under `output/jupyter-notebook/` when working in this repo.
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- Use stable, descriptive filenames (for example, `ablation-temperature.ipynb`).
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## Dependencies (install only when needed)
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Prefer `uv` for dependency management.
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Optional Python packages for local notebook execution:
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```bash
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uv pip install jupyterlab ipykernel
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```
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The bundled scaffold script uses only the Python standard library and does not require extra dependencies.
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## Environment
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No required environment variables.
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## Reference map
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- `references/experiment-patterns.md`: experiment structure and heuristics.
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- `references/tutorial-patterns.md`: tutorial structure and teaching flow.
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- `references/notebook-structure.md`: notebook JSON shape and safe editing rules.
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- `references/quality-checklist.md`: final validation checklist.
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