risk-based-testing
Focus testing effort on highest-risk areas using risk assessment and prioritization. Use when planning test strategy, allocating resources, or making coverage decisions.
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154 skills found
Focus testing effort on highest-risk areas using risk assessment and prioritization. Use when planning test strategy, allocating resources, or making coverage decisions.
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Operate Railway infrastructure: manage projects, services, databases, object storage, deployments, environments, variables, logs, and performance metrics.
Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.
A structured decision-making tool that applies RICE, MoSCoW, Kano, and value-effort frameworks to prioritize software features, roadmap items, and build-vs-defer decisions with data-driven objectivity.
Manage Fly.io edge infrastructure: deploy apps, scale machines, configure volumes, secrets, and networking via the Fly.io Machines API. Python-based, zero-dependency.
Create, manage, and debug dlt (data load tool) pipelines for ingesting data from APIs, databases, and custom sources into destinations like DuckDB, BigQuery, and Snowflake.
Automate quality observability with DORA metrics, defect density tracking, and intelligent quality gate configuration for continuous delivery pipelines.
A structured repository of Agent Skills for context engineering, multi-agent architectures, and production-grade agent system optimization.
Security advisory monitoring for NanoClaw WhatsApp bots, providing vulnerability scanning, skill safety checks, and integrity protection through MCP tools.
Identify, categorize, and troubleshoot flaky tests by analyzing CI history, execution patterns, and code structure to improve test suite reliability.
Tools for deploying, managing, and monitoring DataRobot models, including prediction environment configuration, champion/challenger workflows, and deployment operations.