菜单
Run Python data-science work through Jupyter — execute notebooks headlessly, run cells against a live kernel, and convert notebooks to scripts/HTML. Keyless, local. Use when asked to "run this notebook", "execute these cells", "analyze this dataset in Python", "convert notebook to".
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。
基于 SOC 职业分类
Read and write Airtable bases via the official REST API and the user's personal access token — list/filter records, create, update, delete. Use when asked to "add a row to Airtable", "find records in my base", "update this Airtable record".
Search and retrieve academic papers from arXiv by keyword, author, category, or ID, and read abstracts or full PDFs. Free, keyless. Use when asked to "find papers on", "search arXiv", "summarize this paper", or given an arXiv ID/link.
Render ASCII/terminal art — big text banners (figlet/toilet), colorized output (lolcat), and image-to-ASCII/terminal previews (chafa/jp2a). Keyless, local. Use when asked to "make a banner", "ascii art of", "big text", "show this image in the terminal".
Create, list, and manage calendar events / meetings from the terminal using whatever native CLI is installed (gcalcli for Google Calendar, khal for CalDAV, or AppleScript Calendar on macOS). Use when asked to "schedule a meeting", "create an event", "what's on my calendar", "am I free", "book".
Send and read email from the terminal using whatever native CLI is installed (himalaya, msmtp/mutt/neomutt on Linux, or AppleScript Mail on macOS). Use when asked to "send an email", "email someone", "check my inbox", "read my email", "reply to".
Manage the full GitHub workflow from the terminal — auth, issues, pull request lifecycle (branch/commit/open/CI/merge), code review, and repo management. Uses the gh CLI when present, with a git + REST API fallback. Use when asked to "open a PR", "create an issue", "review this PR", "check CI", "list issues".
| name | jupyter |
| description | Run Python data-science work through Jupyter — execute notebooks headlessly, run cells against a live kernel, and convert notebooks to scripts/HTML. Keyless, local. Use when asked to "run this notebook", "execute these cells", "analyze this dataset in Python", "convert notebook to". |
| allowed-tools | ["@coder","Bash","@read","Write"] |
| triggers | ["jupyter","run this notebook","execute notebook","run these cells","analyze dataset","convert notebook","rodar o notebook","executar notebook","analisar os dados"] |
Local, keyless notebook execution. Detect: command -v jupyter python3 /
Get-Command jupyter, python -ErrorAction SilentlyContinue.
Install: pipx install jupyter or pip install jupyterlab nbconvert ipykernel.
jupyter nbconvert --to notebook --execute --inplace analysis.ipynb
jupyter nbconvert --to html --execute report.ipynb # produce a shareable HTML
Read results with @read on the output, or inspect the executed .ipynb cells.
For quick analysis, write a .py and run it via @coder exec python3 script.py — manage deps
in a venv: python3 -m venv .venv && . .venv/bin/activate && pip install pandas matplotlib.
jupyter nbconvert --to script notebook.ipynb # → .py
jupyter nbconvert --to pdf notebook.ipynb # needs LaTeX (see paper-writing skill)
For a persistent kernel across steps, start jupyter console or use jupyter run. Keep the
kernel alive only as long as needed; one-shot --execute is simpler for batch work.
plt.savefig) and report the path — terminals can't show inline plots.