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研究检索执行命令github未标认证来源可访问许可证需确认审计异常

notebook-clinotebook CLI 搜索

Agent Skill

notebook-cli 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

832

周安装

35

GitHub Stars

21

下载量

291
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:notebook-cli(notebook CLI 搜索)
来源仓库:https://github.com/jupyter-ai-contrib/nb-cli
仓库路径:skills/notebook-cli
安装命令:
npx skills add https://github.com/jupyter-ai-contrib/nb-cli --skill notebook-cli
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jupyter-ai-contrib/nb-cli --skill notebook-cli

简介

用于查找、检索和筛选相关信息,快速定位候选结果。

  • 适合在关键词搜索、任务场景或来源线索下使用,提升信息获取效率。
  • 可结合来源仓库和原始 README 核验具体用法,确保适用性。
  • 安装方式:通过 npx 从 GitHub 仓库添加,支持 Codex、Claude、Cursor、Gemini CLI。
  • 注意:安装前建议确认权限范围和维护状态,避免触发不必要的联网或文件操作。

SKILL.md

Notebook CLI

Use nb for every .ipynb operation. Do not read, write, patch, or edit notebook JSON directly when nb can perform the task.

Core Rules

  • Inspect before editing: run nb read <notebook> --no-output unless outputs are relevant.
  • Prefer the default AI-Optimized Markdown output from nb read; use --json only when nbformat JSON is specifically needed.
  • Prefer cell IDs (--cell / -c) for durable edits after inspecting a notebook. Use indexes (--cell-index / -i) for quick positional work; negative indexes are supported.
  • Use --no-output when summarizing structure or source. Include outputs only when diagnosing results, failures, plots, or displayed values.
  • Use stdin (--source -) for multi-line or quoted content to avoid shell escaping mistakes.
  • If the user specifies uv or the workspace is clearly a uv project, treat uv as the notebook execution environment for the whole task. Use --uv on nb commands that support it, such as nb create and nb execute.
  • When creating a notebook with several sections, add cells in batches of roughly 3–5 cells grouped by logical section using multi-cell sentinels (@@markdown, @@code). Execute and verify each batch before adding the next. Do not add the entire notebook in one call — it increases latency and makes errors harder to catch.
  • Every markdown cell must contain a heading and at least one sentence of prose explaining what the following code does or why it matters. A bare heading is not sufficient — see best-practices.md.
  • When notebook code depends on third-party packages, add a dependency-install cell at the top of the notebook before imports if package availability is uncertain.
  • If a uv environment is present, the dependency cell should use !uv pip install....
  • If a uv environment is not present, the dependency cell should use %pip install....
  • Run nb <command> --help or nb cell <subcommand> --help when command syntax is uncertain.
  • In connected mode, let nb use the saved connection from nb connect. Do not write secret tokens into commands, prompts, logs, or examples; if auto-detection is unavailable, ask the user to establish the connection manually.
  • Before running non-notebook Python commands that should match the active notebook environment, use nb status --python and run commands through the returned prefix.

Trust Boundary

nb is an external executable that can read notebook content and, in connected mode, use saved Jupyter connection state. Before first use in a workspace, verify the command you will run:

command -v nb
nb --version

Use the repository-built or documented nb-cli binary. If nb is missing, resolves to an unexpected path, or reports an unexpected version/name, stop and ask the user to install or select the trusted nb-cli binary. Do not pass notebook contents, outputs, server URLs, or connection state to an unverified executable.

Common Workflows

Create a Notebook

nb create analysis.ipynb
nb create analysis.ipynb --kernel python3
nb create notes.ipynb --markdown

Read references/create.md when creating new notebooks, choosing kernels, or overwriting an existing notebook.

Inspect a Notebook

nb read analysis.ipynb --no-output
nb read analysis.ipynb --cell-index 3
nb search analysis.ipynb "fit_model"

Read references/read.md for filters, output handling, and parsing the AI markdown format.

Edit Cells

nb cell update analysis.ipynb --cell "cell-id" --source -
nb cell add analysis.ipynb --type markdown --source "# Results"
nb cell delete analysis.ipynb --cell-index -1

Read references/edit-cells.md before making multi-cell edits, preserving metadata, inserting around cell IDs, or deleting ranges.

Execute and Debug

nb execute analysis.ipynb --cell-index 4
nb execute analysis.ipynb --start 0 --end 5 --allow-errors
nb read analysis.ipynb --cell-index 4

Read references/execute.md for kernel selection, timeouts, environment flags, and failure-oriented workflows.

Work with JupyterLab

nb connect
nb status
nb cell update analysis.ipynb --cell "cell-id" --source -
nb disconnect

Read references/connect-mode.md when a notebook is open in JupyterLab or changes must sync through a running server.

Manage Outputs

nb read analysis.ipynb --limit 8000 --output-dir ./notebook-outputs
nb output clear analysis.ipynb
nb output clean

Read references/output-format.md for sentinel parsing, externalized output files, clearing outputs, and commit hygiene.

Author Quality Notebooks

Read references/best-practices.md for research-based guidelines on narrative structure, code organization, reproducibility, and naming conventions when creating or reviewing notebooks.

Permission Note

If the agent cannot run nb or connected-mode commands because of sandbox policy, ask the user to allow the needed command. For recurring use, the project or user rules should allow the nb command prefix.

Validation Prompts

Use references/validation-prompts.md when checking whether this skill still guides agents toward the intended nb workflows.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.43%
按下载量换算106

Claude

27.93%
按下载量换算81

Cursor

19.7%
按下载量换算57

Gemini CLI

8.98%
按下载量换算26

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/jupyter-ai-contrib/nb-cli --skill notebook-cli 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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