Skill UI

Utilize this skill to assess the health, adoption, and performance metrics of published EAS Updates directly via the CLI. It reports critical data points including crash rates, unique user counts, install volumes, payload size, and the split between embedded and OTA users, making it essential for post-publish monitoring and regression detection.

A comprehensive guide to designing effective free tiers, freemium models, and free trials for developer tools. It teaches how to create usage limits and feature gates that encourage natural conversion to paid plans without causing developer resentment or hindering initial adoption.

This skill guides users on optimizing their GitHub presence for maximum discoverability and professional appeal. It covers best practices for writing compelling README files, structuring comprehensive profile READMEs, and strategically implementing trust badges and marketing elements to enhance project visibility and adoption.

This skill covers the principles of building Software Development Kits (SDKs) that provide an exceptional developer experience (DX). Learn how to design intuitive APIs, utilize progressive disclosure, establish sensible defaults, and implement actionable, guiding error messages. The focus is on driving product adoption by making the developer's workflow seamless and productive. Ideal for product managers, technical leads, and API designers.

A comprehensive guide to designing transparent, predictable, and developer-friendly usage-based pricing models. Learn which metrics (API calls, compute time, storage) developers accept, how to structure clear pricing pages, and how to avoid confusing or punitive billing practices to maximize adoption and revenue.

This tool automates the creation of Architecture Decision Records (ADRs). It ensures sequential numbering, generates the standardized markdown file, and registers the decision in the AgentDB knowledge graph. Use it when documenting major architectural choices, such as new technology adoption, API design, or data model changes.

This skill executes the metaharness score to provide a comprehensive, multi-dimensional readiness scorecard for a codebase. It assesses critical dimensions like compilation confidence, task coverage, tool safety, and memory usefulness. Use it to evaluate if a repository is prepared for harness adoption before initiating development tools or framework setup.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.