Skill UI

This skill provides comprehensive commands for creating, managing, versioning, and querying structured datasets within the Arize platform. It covers dataset CRUD operations, appending examples, and exporting data in various formats (JSON, CSV, Parquet) using the `ax` CLI. Ideal for preparing test data, generating evaluation examples, or retrieving golden datasets for LLM development and model testing.

A persistent, structured knowledge base designed to accumulate and connect all facets of a research project—including published papers, initial ideas, conducted experiments, and testable scientific claims. Inspired by advanced LLM patterns, it builds a comprehensive knowledge graph, ensuring that research findings are cumulative and durable, rather than isolated notes. It manages typed relationships like contradiction, extension, and support to track the entire scientific lifecycle.

Biopython is a comprehensive Python library designed for molecular biology and bioinformatics. It provides powerful tools for sequence manipulation (DNA, RNA, Protein), parsing standard biological file formats (FASTA, GenBank, PDB), accessing external databases like NCBI (PubMed, GenBank) via Entrez, and performing complex analyses such as sequence alignment, phylogenetic tree construction, and structural bioinformatics. Ideal for building robust, automated bioinformatics pipelines.

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive, versioned repository containing over 61 million single-cell genomics records from human and mouse. It is essential for conducting large-scale cross-dataset analyses, querying gene expression data across diverse tissues, cell types, and diseases, and training machine learning models in bioinformatic research. It supports both in-memory and out-of-core computation using advanced libraries like scanpy.

Polars is a lightning-fast DataFrame library built on Apache Arrow for Python and Rust. It provides high-performance data manipulation using an expressive, lazy evaluation framework. Ideal for building efficient ETL pipelines and migrating from slower pandas versions, especially for datasets that fit comfortably in memory. Its lazy API automatically optimizes query plans for maximum speed and resource efficiency.

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust tools for sequence manipulation (DNA, RNA, Protein), performing sophisticated sequence alignments (global, local, multiple), constructing and analyzing phylogenetic trees, and conducting complex ecological statistics. Key features include calculating diversity metrics (alpha/beta), running ordination analyses (PCoA), and handling diverse biological file formats (FASTA, Newick, BIOM). Ideal for researchers in microbiology, genomics, and ecology.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

This guide provides production-ready patterns for integrating with Bright Data proxy services. It covers critical aspects like setting up a proxy client, implementing robust retry logic for transient errors (e.g., 502, 503), managing sticky sessions for consistent IPs, and parsing complex HTML responses. Essential for building reliable, high-scale web scrapers using TypeScript and Python.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

This guide provides a comprehensive walkthrough for interacting with the Clari API to export critical business data. It covers listing available forecasts, initiating the export of forecast and quota snapshots, monitoring the job status, and ultimately downloading and parsing the structured data. This skill is ideal for testing API connectivity and integrating Clari's revenue intelligence into external data pipelines.

This skill manages the complexities of Clari API version changes, ensuring smooth transitions (e.g., v3 to v4). It guides developers through detecting schema drift, performing data field compatibility checks, and executing necessary database migrations (SQL/Python), ensuring reliable data pipelines after platform updates.

This skill guides users on automating fundraising workflow analysis using local data methods. Since Finta lacks a public API, it teaches how to export pipeline data (CSV) and use Python (Pandas) scripts to calculate key funnel metrics, conversion rates, and generate detailed weekly reports for investor tracking and reporting.