Agent Skills Hub

implementation

Structured, template-driven workflow for end-to-end feature development including coding, automated testing, verification, and session-based improvement.

scikit-learn

Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.

ultraqa

Autonomous QA cycling workflow that runs test-verify-fix loops until your quality goals are met.

snowflake-connections

Configure and manage Snowflake connections for CLI, Streamlit, and Snowpark environments, including authentication methods like SSO, key pair, OAuth, and profile management.

data-quality-frameworks

Implement production-grade data quality validation using Great Expectations, dbt tests, and data contracts to ensure reliable pipelines.

read-file

Read and analyze any data file (CSV, JSON, Parquet, Avro, Excel, etc.) or remote URL (S3, HTTPS) using DuckDB. Automatically detect file formats and preview/profile datasets.

postgresql-table-design

A comprehensive guide for designing high-performance, maintainable PostgreSQL database schemas, covering best practices, data types, indexing, and advanced features.

symbols

Token-efficient codebase navigation through intelligent symbol indexing, domain chunking, and architectural layer filtering. Reduce token usage by 60-95% when exploring or developing complex systems.

paper-reproduce

Systematic methodology for reproducing published academic papers using provided data, including sample selection, statistical verification, and automated reporting.

qa

Browser-based QA automation for web applications. Performs automated site audits, visual regression testing, user flow verification, and issue tracking with real-time browser snapshots.

cyber-ir-playbook

Generate incident response timelines and structured report packs from event logs to facilitate efficient detection-to-recovery tracking.

networkx

Comprehensive toolkit for graph creation, network analysis, and visualization in Python. Ideal for analyzing relationships, centrality, community detection, and synthetic network generation across diverse research domains.