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Found 39 Skills
Access PUDL table data plus table/column/source metadata in Jupyter or Marimo notebooks for debugging and visualization. Use when users ask what a table contains, how to read it, or how columns are defined.
Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.
Generates a Jupyter notebook that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
Guide for using Microsoft MarkItDown - a Python utility for converting files to Markdown. Use when converting PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs, Jupyter notebooks, RSS feeds, or Wikipedia pages to Markdown format. Also use for document processing pipelines, LLM preprocessing, or text extraction tasks.
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.
For the creation, review, refactoring, and presentation of .ipynb Notebooks (Jupyter / JupyterLab / Google Colab / VS Code). Covers engineered directory structures, efficient token processing, demonstration/sharing patterns, and reproducible workflows with uv/venv.
A high-level interactive graphing library for Python. Ideal for web-based visualizations, 3D plots, and complex interactive dashboards. Built on plotly.js, it allows users to zoom, pan, and hover over data points in a browser-based environment. Use for interactive charts, web applications, Jupyter notebooks, 3D data visualization, geographic maps, financial charts, animations, time-series analysis, and building production-ready dashboards with Dash.
Disposable builder skill (delete after the harness is built): scaffolds a Colab-centric project — a repository whose deliverable is Google Colab notebooks, mirrored locally and validated in an official Colab-runtime devcontainer before running on real Colab. Use when creating or hardening a repository that produces notebooks people open and run in Colab — tutorials, course materials, runnable demos, shareable analyses — or when a project uses Colab as its runtime, including driving Colab VMs as disposable compute for local scripts. Not for generic Jupyter-notebook work, projects where notebooks are incidental exploration beside a library or pipeline, or mature migrations; preserves working choices.
Provision a Runpod Pod for interactive, development or training use and tell the user exactly how to connect to it. Reads stock before creating, picks the image and GPU class from the catalog reads, exposes only the ports the workload needs, hands back the working proxy URL or SSH address, and for training co-locates a network volume so checkpoints outlive the Pod. Also covers creating a Pod and stopping it immediately, so that only its disk bills until the user comes back. Use when the user wants a GPU or CPU machine of their own (a web app, Jupyter, SSH, a training box) rather than a Serverless endpoint, through the Runpod MCP tools.
Prepare organized packages of project files for sharing at different levels - from summary PDFs to fully reproducible archives. Creates copies with cleaned notebooks, documentation, and appropriate file selection. After creating sharing package, all work continues in the main project directory.