“notebook”的搜索结果

Integration with LabArchives electronic lab notebook platform

中文简介正在准备中。当前显示原文说明。

Build, run, debug, and operate applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments that run inside a container with up to 8-hour lifetimes. Triggers on: Lambda MicroVMs, Firecracker isolation, snapshot-resumable compute, suspend/resume, sandboxed or untrusted code execution, AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, isolated security scanners, long-lived sessions, or port-listening servers (gRPC, WebSocket, custom TCP). For standard event-driven Lambda functions, use the aws-lambda skill instead.

中文简介正在准备中。当前显示原文说明。

Manages project directory setup and artifact organization. Use when starting a new project, resuming an existing one, or when a PLAN.md needs to be associated with a project directory. Creates the project folder structure (specs/, scripts/, notebooks/, manifests/, agent_memory/) and resolves project naming.

中文简介正在准备中。当前显示原文说明。

Use when a team wants to design, audit, or operate an AI Marketing OS — a Brand Hub source of truth, role-based AI projects for leader, content, design, performance, and knowledge, skill chains, ChatGPT plus Claude plus NotebookLM workflows, MCP connectors, a Notion or Drive second brain, ads data loops, SOPs, weekly reviews, and handoffs. Trigger on 'AI marketing OS', 'set up our marketing AI system', 'Brand Hub', 'AI workflow for the team', 'MCP for ads data', 'how do I make AI actually useful for marketing'. Not for — one campaign brief, see `02-campaign-brief-global`; the weekly report, see `07-marketing-report-global`; the brand visual rules, see `46-brand-guideline-global`; the product context file, see `product-marketing-context-global`.

中文简介正在准备中。当前显示原文说明。

Use when the user is explicitly working with Quarto, .qmd files, _quarto.yml, Quarto projects, or Quarto features such as callouts, cross-references, citations, Mermaid diagrams, extensions, websites, books, presentations, and reports. Also use for explicit migration from or comparison with R Markdown, bookdown, blogdown, xaringan, distill, or Jupyter notebooks to Quarto. Do not use for general R Markdown or related-format questions unless Quarto or migration to Quarto is explicitly mentioned.

中文简介正在准备中。当前显示原文说明。

Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.

中文简介正在准备中。当前显示原文说明。

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

中文简介正在准备中。当前显示原文说明。

Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.

中文简介正在准备中。当前显示原文说明。

Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.

中文简介正在准备中。当前显示原文说明。

marimo ノートブックを正しいフォーマットでPythonファイルに作成するスキル。 「marimoノートブック作成」「インタラクティブノートブック」「Pythonノートブック」等のリクエストで発動。

中文简介正在准备中。当前显示原文说明。

Operate Fugue workflows, FugueSQL, backend selection, and notebook magics for pandas and distributed dataframe execution.

中文简介正在准备中。当前显示原文说明。

Guides fg-data-profiling data profiling, report generation, configuration, CLI, comparison, privacy, and optional Spark/notebook integration workflows.

中文简介正在准备中。当前显示原文说明。

Route BertViz Transformer attention visualization, neuron-view, notebook, and HTML export workflows.

中文简介正在准备中。当前显示原文说明。

Turn one document into a kawaii NotebookLM slide-deck PDF. Use for "wowerpoint this", "make a deck about <file>", "turn this report into slides", or any request to render a single document as shareable narrative slides.

中文简介正在准备中。当前显示原文说明。

Use when reviewing Python changes - Django, Flask and FastAPI handlers, subprocess, SQL, Jinja2, format strings, and data/ML code that loads pickles, models, notebooks or remote model code

中文简介正在准备中。当前显示原文说明。

Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building a notebook or app, or running the full onboarding pipeline. Time budget ≤15 min autonomous, ≤90 min HITL. Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or Cypher migration — use neo4j-migration-skill. Does NOT cover CLI/admin tasks on an existing DB — use neo4j-cli-tools-skill.

中文简介正在准备中。当前显示原文说明。

Use this when reviewing statistical analysis, experiments, model evaluation, or ML work done by someone else (a notebook, PR, or report) — checking that the required context is stated and that every method applied comes with the figures and values required by the matching *-diagnostics skill. When running an analysis yourself, apply the matching *-diagnostics skill directly.

中文简介正在准备中。当前显示原文说明。

Use this when managing Python dependencies with uv, running tests with pytest, linting with ruff, formatting code, type checking with mypy, or executing notebooks.

中文简介正在准备中。当前显示原文说明。

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

中文简介正在准备中。当前显示原文说明。

Field observation, data collection, and research methods for geographic inquiry. Covers site selection and sampling strategies, field observation techniques (landscape reading, transects, quadrats), survey and interview methods for human geography, GPS and field mapping, environmental monitoring and instrumentation, data recording and field notebooks, ethical considerations in fieldwork, and the integration of field data with GIS analysis. Use when planning geographic field research, designing data collection protocols, evaluating field evidence, or connecting field observations to broader geographic analysis.

中文简介正在准备中。当前显示原文说明。

Apply best practices for Jupyter notebooks: cell ordering, reproducibility, parameterisation. Use when working with .ipynb files.

中文简介正在准备中。当前显示原文说明。

Adhoc workflow for small targeted changes to existing behavior or appearance, without the full story ceremony. Use when the orchestrator encounters a bug with a clear root cause and surgical solution, OR a small enhancement to something already built (a different icon, wording, spacing, color). Triggers on: 'fix this', 'this is broken', 'the alignment is off', 'that button doesn't work', 'quick fix', 'patch this', 'tweak this', 'change the icon', 'adjust the wording', post-story corrections, or any situation where the write gate blocks a small targeted change. Acts NOW - a defect to file and fix later belongs in /craft:bugs, other deferred work in the notebook. Do NOT use for new features, design exploration, or changes requiring creative spark.

中文简介正在准备中。当前显示原文说明。

Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.

中文简介正在准备中。当前显示原文说明。

Apache Superset is a widely adopted open-source BI platform for SQL exploration, chart building, and dashboard delivery. This skill is useful when an agent needs to query warehouse data, assemble dashboards, or explain metrics using a mature analytics interface instead of ad hoc notebook code.

中文简介正在准备中。当前显示原文说明。