Skill UI

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.

Used to verify the actual billing reality, revenue, and pricing mechanics—such as team billing, quotas, and refunds—based on underlying product code, rather than sales documents or simple customer fixes. This skill separates billing facts, customer impact, and the true product implementation logic.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

Comprehensive patterns and tools for writing robust tests for F# applications within the modern .NET ecosystem. This guide covers unit testing (xUnit, FsUnit), property-based testing (FsCheck), mocking dependencies (NSubstitute), and full integration testing (WebApplicationFactory). Ideal for improving test coverage, validating complex business logic, and establishing a resilient testing infrastructure.

A comprehensive guide to professional Git version control. It covers best practices for branching strategies (GitHub Flow, GitFlow, Trunk-Based), writing standardized commit messages using Conventional Commits, managing merges vs. rebases, and defining effective Pull Request workflows. Ideal for any development team looking to improve code collaboration and maintain a clean, traceable history.

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

A comprehensive 7-section readiness report generated for a repository, analyzing key metrics including risk score, test confidence, and publish readiness. It assesses architectural viability, agent topology recommendations, and potential risks, making it essential for pre-mint reviews, CI/CD gating, and detecting architectural drift in large codebases.

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.

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

This guide details the Hexagonal Architecture (Ports and Adapters) pattern, a robust method for decoupling core business logic from external infrastructure concerns. It teaches how to structure applications to make the domain model independent of databases, UI frameworks, or message queues. Implementing this pattern drastically improves testability, maintainability, and the ability to swap out external services without rewriting business rules.

This skill analyzes existing legacy codebases to detect implicit coding conventions across four meta-architectural dimensions (File Anatomy, State & Control Flow, Infrastructure, Error Handling). It resolves conflicts one by one with the user, generating a structured rules file (.ai-style-rules.md) and optionally installing persistent hooks to prevent AI-generated code style drift in future development.

This skill facilitates the crucial process of sharing learned intelligent patterns across isolated projects or machines using the InterPlanetary File System (IPFS). It allows users to publish a project's accumulated knowledge base to a decentralized network and later fetch, apply, and merge these patterns into new or existing projects. This is invaluable for bootstrapping new applications with prior knowledge or consolidating learnings across a large monorepo.