Agent Skills Hub

explore

Meta-skill for structured, multi-depth codebase exploration including architectural analysis, fast structural overviews, and deep-dive documentation workflows.

context-driven-testing

Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.

MultiQC

Automates the triage, prioritization, and feedback process for new MultiQC module requests by analyzing repository activity, community engagement, and technical feasibility.

docs-generator

Generate hierarchical, AI-optimized documentation structures (AGENTS.md, agent.d) to streamline codebase context, setup, and navigation for AI coding assistants and developers.

technical-writing

Produce clear, professional technical documentation, blog posts, and tutorials based on real engineering experience, prioritizing value and actionable insights.

explaining-code

Explains code using visual diagrams, relatable analogies, step-by-step walkthroughs, and common pitfalls.

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.

design-excellence

A design system and anti-pattern guide to make AI-generated UI look human-crafted. Ensures professional aesthetics by managing color, typography, spacing, and animations for the Toh Framework.

frontend-to-backend-requirements

Standardize frontend communication by documenting data requirements and business rules for backend developers, ensuring clear alignment without dictating implementation details.

template-structure

Standardize project scaffolding with pre-configured Claude Code directories, commands, and agents to ensure consistency across all your development templates.

cost-optimization

Systematic cloud cost optimization for AWS, Azure, GCP, and OCI through resource rightsizing, automated governance, pricing model analysis, and architectural best practices.

ai-llm-patterns

Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.