Skill UI

An interactive skill designed to interview users to capture project vision, core decisions, and technical preferences. It systematically converts raw discussions into structured documentation, maintaining the project's history in ADRs (Architectural Decision Records) and its overarching goal in the README.

Run comprehensive, read-only exposure checks against your local machine or project directories in response to security advisories and CVEs. This tool systematically checks various ecosystems (Node.js, Python, Homebrew, etc.) to generate a structured, non-invasive audit report, helping users understand their actual risk exposure without making any system changes.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

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.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

Automatically analyzes unfamiliar codebases to generate a comprehensive, structured onboarding guide. It systematically detects package manifests, framework fingerprints, architecture maps, key entry points, and coding conventions. Ideal for new developers joining a project or setting up a repository for the first time.

This guide outlines architectural patterns and best practices for building scalable, production-ready APIs with FastAPI. It covers proper project structure, separating concerns using routers, services, and CRUD layers, implementing dependency injection for database sessions and authentication, defining Pydantic schemas for request/response, handling asynchronous operations, and managing custom error responses.

A robust framework designed to track complex, long-running objectives that span multiple sessions, weeks, or even months. It manages goals through structured milestones, maintains persistent progress records, and includes critical drift detection to ensure projects stay on target and scope creep is managed effectively. Ideal for research programs, migration projects, or multi-stage development initiatives.

A comprehensive guide to mastering structured concurrency, reactive streams, and state management in Kotlin. Covers advanced topics like Flow operators (debounce, retryWhen), StateFlow for UI state, and implementing robust asynchronous logic for Android and Kotlin Multiplatform projects. Essential for modern Android development.

This memory bridge ingests Claude Code's local auto-memory markdown files and structures them into AgentDB. It generates high-fidelity ONNX embeddings, handles deduplication using cosine similarity, and indexes the memories in a scalable namespace. This enables seamless, unified semantic search across all historical sessions and projects, significantly enhancing RAG capabilities.

Provides a structured workflow for machine learning engineering tasks, covering model development, training, evaluation, and deployment. Use it to manage end-to-end ML projects efficiently.