Skill UI

A comprehensive guide covering standards for building highly maintainable Flutter applications using Dart. It details best practices across architecture (MVVM, layered design), state management (Cubit/BLoC), folder structure, widget implementation, and testing strategies. Use this skill when writing new code, reviewing existing codebases, or planning complex features to ensure scalable and clean development.

A sophisticated and print-friendly presentation template designed for explaining complex system architectures and data pipelines. Featuring a blueprint grid, cream background, and rust red accents, this deck is ideal for technical deep dives in engineering and system design.

Create engaging and intuitive process visualization using the whiteboard aesthetic. This template simulates a physical brainstorming session with sticky notes, hand-drawn SVG connections, and optional cursor interactions, perfect for onboarding, system architecture, or operational workflows.

Automates the creation of Architecture Decision Records (ADRs). This tool manages the entire lifecycle of an ADR: determining the next sequential ID, generating a standardized Markdown file, storing the record in the AgentDB graph, and automatically linking it to related decisions. Essential for documenting significant architectural choices, API designs, and data model changes to maintain system knowledge coherence.

Automatically indexes all Architecture Decision Records (ADRs) within a monorepo. It parses both v3-style and plugin-style ADR formats, persisting the content into the `adr-patterns` namespace and all relationships (supersedes, amends, related, depends-on) into the `adr-edges` namespace. This is crucial for maintaining a single source of truth for architectural history and dependencies, significantly improving knowledge retrieval and graph integrity.

This tool reviews code changes (via diff) against accepted Architecture Decision Records (ADRs) to detect potential architectural drift, compliance violations, and adherence issues. It simulates a pre-merge check, ensuring that new code respects established architectural constraints and decisions made by the team. Ideal for CI/CD pipelines and mandatory code reviews.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

A comprehensive guide to implementing Clean Architecture patterns for both Android and Kotlin Multiplatform (KMP) projects. It covers robust module structuring, dependency inversion rules, defining UseCases and Repositories, and designing the data layer using technologies like Room, SQLDelight, and Ktor. Learn how to separate concerns, maintain platform independence, and manage complex data flows.

This comprehensive guide covers best practices for building modern Angular applications. It provides detailed instructions on component architecture, state management using Signals, advanced routing, dependency injection, handling forms (prioritizing Signal Forms), and implementing accessibility (ARIA). It also details setup procedures for new projects and modern rendering strategies like SSR.

This guide outlines how to build new API connectors or providers that strictly adhere to the existing integration architecture and patterns of a target repository. It is designed for adding integrations while maintaining architectural consistency (e.g., layout, configuration schema, authentication model, and testing style), preventing the creation of novel, unsupported patterns.

This skill helps developers structure and record critical architectural decisions (ADRs) made during coding sessions. It automatically detects moments of decision, capturing the context, alternatives considered, and the rationale (trade-offs and consequences). This maintains a living log that explains 'why' the code base was built the way it is, preventing knowledge loss and improving project maintainability.