Agent Skills Hub

low-complexity

Enforces low Cognitive and Cyclomatic complexity in all code. Automatically maintains readability, modularity, and maintainability by preventing complex functions during development.

using-sdlc-engineering

CMMI-based SDLC router providing process guidance, requirements management, architectural decision support, quality assurance, and governance for GitHub and Azure DevOps workflows.

avoid-feature-creep

A framework for software teams and AI agents to prevent feature creep, enforce scope discipline, and ship focused MVPs by applying strict validation, backlog hygiene, and clear decision-making processes.

codebase-inspection

Analyze codebase statistics: LOC, language distribution, and code-to-comment ratios using pygount.

documentation-creation

Automated generation of project documentation from codebase analysis, ensuring accuracy, consistency, and alignment with VilnaCRM architecture patterns.

skill-repo

Standardize, validate, and manage Netresearch AI agent skill repositories with automated structure enforcement, distribution workflows, and licensing compliance tools.

frontend-design-review

Review, audit, and build production-grade frontend interfaces with high design quality, accessibility standards, and design system compliance.

long-running-agent

Framework for building AI agents that persist state across multiple context windows, enabling them to complete complex, multi-day coding tasks without losing progress or context.

shift-right-testing

Production-grade testing strategy implementing feature flags, canary releases, synthetic monitoring, and chaos engineering for continuous reliability in live environments.

ui-styling

Build accessible, consistent UIs using shadcn/ui and Tailwind CSS. Employs a component-first architecture for design systems, React Hook Form integration, and responsive mobile-first development.

study-note

Log ideas, notes, and learning progress chronologically to project archives using a CLI helper tool for systematic knowledge retention.

data-quality-frameworks

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