Skill UI

Automates detection and deep exploratory analysis across 200+ scientific data formats, assessing quality, extracting metadata, and recommending visualizations while generating markdown reports that guide downstream workflows and highlight structure and distributions.

Direct Python helpers for KEGG's REST API, covering database metadata, listing, search, retrieval, ID conversion, linkage, and drug interactions to power pathway and gene analysis.

Perform comprehensive digital forensic analysis on raw (dd), E01, or AFF disk images using industry-standard tools like Autopsy and The Sleuth Kit. This process allows investigators to recover deleted files, examine file system metadata, search for specific keywords, and build detailed investigation timelines. It is essential for generating structured reports from digital evidence in legal and cybersecurity investigations.

A comprehensive toolset for digital forensics investigators, enabling the parsing of proprietary Microsoft Outlook PST and OST files. It extracts critical evidence including full message content, detailed email headers (for message routing/tracing), attachments, and deleted items from the Recoverable Items folder. Essential for e-discovery, legal investigations, and security incident response to reconstruct communication patterns and analyze message metadata.

This skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive collection of versioned public single-cell and spatial transcriptomics data. It enables efficient querying of population-scale cell metadata, gene expression slices, and embeddings without downloading entire datasets. Suitable for analyzing human, mouse, and other organism data across tissues and diseases. Integrates with Scanpy, AnnData, and TileDB-SOMA for large-scale analysis and machine learning workflows.

COBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of genome-scale metabolic models. It supports FBA, FVA, gene knockouts, flux sampling, and SBML I/O for systems biology and metabolic engineering.

Perform bounded, local exploratory analysis on supported scientific files like CSV, JSON, and HDF5. Inspect metadata, check missingness, and audit outliers without executing code or accessing networks. Generates deterministic reports for data validation before modeling.

A comprehensive guide for deep debugging complex Snowflake issues. This guide covers advanced techniques including detailed query profiling (spill analysis), diagnosing lock contention and transaction conflicts, analyzing metadata operations, and optimizing partition usage. Use it when standard troubleshooting fails to address slow queries or warehouse performance bottlenecks.

A comprehensive playbook for digital forensic experts. It details systematic techniques and tools for detecting and extracting hidden data embedded across various media types, including images (LSB, PNG chunks, JPEG DCT, EXIF metadata), audio (spectrogram, DTMF), and files (polyglots, appended data). Essential for deep-dive forensic investigations.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.

This meta-skill orchestrates a comprehensive workflow for analyzing PDF documents. It handles complex tasks such as summarizing, cross-document comparison, and targeted Q&A. It ensures findings are traceable by indexing evidence from various sources (full PDFs, pasted excerpts, etc.) and synthesizing structured insights across multiple files.