holistic-testing-pact
Apply Holistic Testing with PACT (Proactive, Autonomous, Collaborative, Targeted) principles to build quality into team culture and test strategies for modern software systems.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
391 skills found
Apply Holistic Testing with PACT (Proactive, Autonomous, Collaborative, Targeted) principles to build quality into team culture and test strategies for modern software systems.
Statistical modeling and econometrics library for Python. Performs OLS, GLM, mixed models, ARIMA, diagnostics, and inference for rigorous scientific analysis.
Evaluate scientific claims and research methodology for rigor, bias, and validity. Use evidence-based frameworks like GRADE and Cochrane to analyze experiments, protocols, and study conclusions.
Expert SQL agent for modern database systems, query optimization, HTAP environments, and data architecture patterns. Optimize performance, schema design, and analytical workloads effectively.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
Analyze, validate, and generate Schema.org structured data (JSON-LD). Detect markup, identify opportunities, and ensure compliance with Google's rich result guidelines.
Architectural expert for the SpecKit template, managing Spec-Driven Development, design patterns, and microservices lifecycle automation.
A suite of professional tools for auditing, evaluating, chunking, and scaffolding production-ready RAG pipelines within Claude Code.
Expert database design and access patterns: schema architecture, indexing strategies, query optimization, repository patterns, and transaction management for SQL and NoSQL databases.
Create polished animated terminal demos for pull requests and documentation using asciinema, agg, and svg-term-cli.
Comprehensive AI-generated text detection framework. Features multi-layer analysis of vocabulary, structural patterns, model-specific fingerprints, and technical metadata artifacts to identify AI authorship.
An automated memory middleware for AI agents, implementing a Retrieve-Respond-Save loop to maintain long-term persistent context across conversations.