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Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/a5c-ai/babysitter --skill jupyter-notebook-executorコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SOC 職業分類に基づく
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SKILL.md を表示中
| name | jupyter-notebook-executor |
| description | Jupyter notebook execution skill for running notebooks programmatically and extracting outputs. |
| allowed-tools | ["Read","Write","Bash","Glob","Grep"] |
| graph | {"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:data-science-experimentation","skill-area:data-visualization"],"roles":["role:data-scientist","role:data-analyst"],"workflows":["workflow:experiment-design"]} |
Jupyter notebook execution skill for running notebooks programmatically, parameterizing inputs, and extracting outputs for ML workflows.
{
{
"type": "object",
"required": ["status", "action"],
"properties": {
"status": {
"type": "string",
"enum": ["success", "error", "timeout"]
},
"action": {
"type": "string"
},
"executionResult": {
"type": "object",
"properties": {
"outputPath": { "type": "string" },
"executionTime": { "type": "number" },
"cellsExecuted": { "type": "integer" },
"errors": { "type": "array" }
}
},
"conversionResult": {
"type": "object",
"properties": {
"outputPath": { "type": "string" },
"format": { "type": "string" }
}
},
"extractedData": {
"type": "object",
"properties": {
"variables": { "type": "object" },
"outputs": { "type": "array" },
"figures": { "type": "array", "items": { "type": "string" } }
}
}
}
}
{
kind: 'skill',
title: 'Execute EDA notebook with parameters',
skill: {
name: 'jupyter-notebook-executor',
context: {
action: 'execute',
notebookPath: 'notebooks/eda_template.ipynb',
executeConfig: {
parameters: {
data_path: 'data/train.csv',
output_dir: 'results/eda/',
sample_size: 10000
},
outputPath: 'notebooks/eda_results.ipynb',
kernel: 'python3',
timeout: 3600
}
}
}
}