Loading...
Loading...
Found 2,492 Skills
Python testing with pytest covering fixtures, parametrization, mocking, and test organization for reliable test suites
AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 44 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
AI/ML APIs, LLM integration, and intelligent application patterns
A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.
Store a learning, pattern, or decision in the memory system for future recall
Stop your AI from making things up. Use when your AI hallucinates, fabricates facts, isn't grounded in real data, doesn't cite sources, makes unsupported claims, or you need to verify AI responses against source material. Covers citation enforcement, faithfulness verification, grounding via retrieval, and confidence thresholds.
Design short-term, long-term, and graph-based memory architectures
Generate failing tests for the TDD red phase to define expected behavior and edge cases.
General testing best practices and guidelines for writing comprehensive, maintainable tests across different testing frameworks and languages.
Health check your knowledge base — find broken links, missing metadata, gaps
Principal AI Architect and Machine Learning Engineer.