Jupyter Live Kernel

Operational Steps

1. 确认输入参数完整
2. 执行核心操作（参考本目录下的 scripts/ 或 references/）
3. 验证输出符合契约
4. 保存结果并报告

Pitfalls

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Verification

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1. 2. 3.

IO_CONTRACT

• input: request: str, context: dict — 用户请求描述、上下文信息
• output: result: dict — 技能执行结果（结构因技能而异）

对应原则：P2（机械原子暴露输入输出规范）

Jupyter Live Kernel (hamelnb)

Gives you a stateful Python REPL via a live Jupyter kernel. Variables persist across executions. Use this instead of execute_code when you need to build up state incrementally, explore APIs, inspect DataFrames, or iterate on complex code.

When to Use This vs Other Tools

Rule of thumb: If you'd want a Jupyter notebook for the task, use this skill.

Prerequisites

1. uv must be installed (check: which uv)
2. JupyterLab must be installed: uv tool install jupyterlab
3. A Jupyter server must be running (see Setup below)

Setup

The hamelnb script location:

SCRIPT="$HOME/.agent-skills/hamelnb/skills/jupyter-live-kernel/scripts/jupyter_live_kernel.py"

If not cloned yet:

git clone https://github.com/hamelsmu/hamelnb.git ~/.agent-skills/hamelnb

Starting JupyterLab

Check if a server is already running:

uv run "$SCRIPT" servers

If no servers found, start one:

jupyter-lab --no-browser --port=8888 --notebook-dir=$HOME/notebooks \
  --IdentityProvider.token='' --ServerApp.password='' > /tmp/jupyter.log 2>&1 &
sleep 3

Note: Token/password disabled for local agent access. The server runs headless.

Creating a Notebook for REPL Use

If you just need a REPL (no existing notebook), create a minimal notebook file:

mkdir -p ~/notebooks

Write a minimal .ipynb JSON file with one empty code cell, then start a kernel session via the Jupyter REST API:

curl -s -X POST http://127.0.0.1:8888/api/sessions \
  -H "Content-Type: application/json" \
  -d '{"path":"scratch.ipynb","type":"notebook","name":"scratch.ipynb","kernel":{"name":"python3"}}'

Core Workflow

All commands return structured JSON. Always use --compact to save tokens.

1. Discover servers and notebooks

uv run "$SCRIPT" servers --compact
uv run "$SCRIPT" notebooks --compact

2. Execute code (primary operation)

uv run "$SCRIPT" execute --path <notebook.ipynb> --code '<python code>' --compact

State persists across execute calls. Variables, imports, objects all survive.

Multi-line code works with $'...' quoting:

uv run "$SCRIPT" execute --path scratch.ipynb --code $'import os\nfiles = os.listdir(".")\nprint(f"Found {len(files)} files")' --compact

3. Inspect live variables

uv run "$SCRIPT" variables --path <notebook.ipynb> list --compact
uv run "$SCRIPT" variables --path <notebook.ipynb> preview --name <varname> --compact

4. Edit notebook cells

# View current cells
uv run "$SCRIPT" contents --path <notebook.ipynb> --compact

# Insert a new cell
uv run "$SCRIPT" edit --path <notebook.ipynb> insert \
  --at-index <N> --cell-type code --source '<code>' --compact

# Replace cell source (use cell-id from contents output)
uv run "$SCRIPT" edit --path <notebook.ipynb> replace-source \
  --cell-id <id> --source '<new code>' --compact

# Delete a cell
uv run "$SCRIPT" edit --path <notebook.ipynb> delete --cell-id <id> --compact

5. Verification (restart + run all)

Only use when the user asks for a clean verification or you need to confirm the notebook runs top-to-bottom:

uv run "$SCRIPT" restart-run-all --path <notebook.ipynb> --save-outputs --compact

Practical Tips from Experience

1. First execution after server start may timeout — the kernel needs a moment to initialize. If you get a timeout, just retry.

2. The kernel Python is JupyterLab's Python — packages must be installed in that environment. If you need additional packages, install them into the JupyterLab tool environment first.

3. --compact flag saves significant tokens — always use it. JSON output can be very verbose without it.

4. For pure REPL use, create a scratch.ipynb and don't bother with cell editing. Just use execute repeatedly.

5. Argument order matters — subcommand flags like --path go BEFORE the sub-subcommand. E.g.: variables --path nb.ipynb list not variables list --path nb.ipynb.

6. If a session doesn't exist yet, you need to start one via the REST API (see Setup section). The tool can't execute without a live kernel session.

7. Errors are returned as JSON with traceback — read the ename and evalue fields to understand what went wrong.

8. Occasional websocket timeouts — some operations may timeout on first try, especially after a kernel restart. Retry once before escalating.

Timeout Defaults

The script has a 30-second default timeout per execution. For long-running operations, pass --timeout 120. Use generous timeouts (60+) for initial setup or heavy computation.

验证清单 · VERIFICATION

• uv run "$SCRIPT" servers --compact 确认 Jupyter 服务器在线且 kernel 会话已启动
• 所有命令均带 --compact 标志，输出为可解析 JSON 而非冗余文本
• 跨 execute 调用状态持久化正确（上一步定义的变量在下一步可用）
• 子命令参数顺序正确：--path 等全局标志位于子子命令之前（如 variables --path nb.ipynb list）
• 首次执行/内核重启后超时已重试一次，而非直接报错
• 出错时已解析返回 JSON 的 ename/evalue 字段定位具体异常

约束规则 · RULES

1. 输入约束: 参数类型、范围、格式必须校验
2. 输出约束: 返回值结构、编码、命名必须一致
3. 异常约束: 错误信息必须包含上下文和恢复建议
4. 安全约束: 不执行未验证的任意代码，不暴露内部状态

Golden 集合 · GOLDEN SET

• Golden Input: 标准输入样本（覆盖正常路径）
• Golden Output: 预期输出（精确匹配或格式校验）
• Golden Error: 预期错误信息（覆盖失败路径）

Golden 集合是测试的单一真理来源。所有改进必须通过 golden 测试。

违反规则的操作视为不安全，必须拒绝或隔离。

每项验证必须可执行、可记录、可复现。验证失败时记录原因和修复。

Jupyter Live Kernel

Genes (策略基因)

紧凑策略表示。条件→策略。需要深度时参考完整文档。

• [JUPY-001] 需要跨步骤保持变量状态或进行迭代探索 → 使用 Jupyter Live Kernel 替代无状态的 execute_code
• [JUPY-002] 执行任何脚本命令以获取输出 → 始终添加 --compact 标志以节省 token 并简化 JSON 结构
• [JUPY-003] 服务器启动后首次执行代码或内核重启后 → 预期可能超时，直接重试一次而非立即报错
• [JUPY-004] 需要安装额外的 Python 包 → 必须安装到 JupyterLab 的工具环境中，而非系统全局环境
• [JUPY-005] 仅用于纯 REPL 交互且无需保存笔记结构 → 创建 scratch.ipynb 并仅使用 execute 命令，跳过单元格编辑
• [JUPY-006] 执行包含子命令的脚本参数 → 确保 --path 等全局标志位于子子命令之前（如 variables --path nb.ipynb list）
• [JUPY-007] 遇到执行错误 → 解析返回 JSON 中的 ename 和 evalue 字段以定位具体异常原因
• [JUPY-008] 执行长耗时计算或初始设置 → 显式传递 --timeout 参数（如 60 或 120 秒）以覆盖默认的 30 秒限制

示例 · EXAMPLES

示例 1：跨步骤保持状态的迭代探索

• 输入: 探索一个 DataFrame 并逐步变换
• 操作: execute --path scratch.ipynb --code 'import pandas as pd\ndf = pd.read_csv("data.csv")' → 再次 execute 用已存在的 df
• 验证: 第二次 execute 无需重新 import，variables --path scratch.ipynb list 能看到 df

示例 2：预览活变量

• 输入: 检查当前 kernel 中 result 变量的内容
• 操作: uv run "$SCRIPT" variables --path scratch.ipynb preview --name result --compact
• 验证: 返回 JSON 含变量预览；注意 --path 必须位于子子命令 preview 之前

示例 3：干净验证整本 notebook

• 输入: 用户要求确认 notebook 从头到尾可运行
• 操作: uv run "$SCRIPT" restart-run-all --path <nb.ipynb> --save-outputs --compact
• 验证: 全部 cell 运行完成；出错时解析返回 JSON 的 ename/evalue 定位异常，首次超时则重试一次