ChicagoHAI

Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

Train or fine-tune models on Modal cloud GPUs (LoRA SFT, full SFT, data prep, eval) with strict leave-no-trace lifecycle. Use when an experiment needs GPU training, fine-tuning, or model evaluation that exceeds local Docker capacity.

Serve a model via vLLM on Modal as an HTTPS endpoint with strict leave-no-trace lifecycle. Use when an experiment needs to query an LLM as a remote API (chat completions, generation) rather than running it locally.

Use the University of Chicago Data Science Institute Slurm cluster, called dsi-cluster here, for NeuriCo training, evaluation, sweeps, and batch jobs. Use only when NeuriCo is run with --compute-backend dsi-slurm. Run jobs in the runtime-provided remote workspace, monitor Slurm safely, and copy required outputs back to the local NeuriCo workspace.

Formally verify Lean 4 proofs against Mathlib using the hosted Axiom AXLE API — no local Lean toolchain. Use in the mathematics_lean domain, when a proof needs machine-checked verification but installing Lean/Mathlib locally is undesirable, or when translating informal proofs into formally verified Lean 4 code checked in the cloud.

Autonomous research framework that orchestrates AI agents (Claude Code, Codex, Gemini) to design, execute, analyze, and document scientific experiments. Takes a structured research idea (YAML with title, domain, hypothesis) and produces code, results, plots, LaTeX papers, and GitHub repositories.

Write academic papers from experiment results using LaTeX. Use when experiments are complete and REPORT.md exists, when asked to write a paper, or when generating publication-ready documents in NeurIPS style.

Find and search for academic papers using the paper-finder service. Use when conducting literature review, searching for related work, finding baseline papers, or looking for methodology references.

Conduct systematic literature reviews using a structured workflow. Use when starting a new research project, surveying a field, or documenting related work for a paper.