thehardwarestore.ai

The BMAD Method is an AI-assisted software development framework that provides a modular set of skills (workflows) to guide AI agents and humans through the entire development lifecycle. Each skill is a self-contained unit that can be invoked to perform specific tasks such as brainstorming, creating product requirements, designing architecture, implementing features, reviewing code, and conducting retrospectives. The framework maintains project context in `AGENTS.md` to ensure AI alignment with project-specific rules and supports deep customization via layered TOML configuration. Built for integration with AI-powered IDEs (like Claude Code, Cursor), the system emphasizes evidence-based practices, including validation lenses, memlog-based session tracking, and ticketing integration for work tracking. It also offers web bundle exports for deploying skills as Gemini Gems or ChatGPT Custom GPTs. The architecture features a clean separation of concerns, with skills encapsulating their own documentation, scripts, assets, and tests, while shared utilities handle configuration resolution, memlog logging, and validation. The project demonstrates strong engineering practices through automated validation tools, multi-language documentation, and a structured release process. While it manages complexity through modularity, users should be aware of maintenance risks such as duplicated validation text across skills and the need to place customizations in override directories to prevent loss during updates. Overall, the BMAD Method provides a robust, reusable toolkit for teams seeking to institutionalize AI-assisted development workflows.

This repository implements a 'Repository Harness'—a Rust-based CLI framework designed to make software codebases legible and operable for AI agents. It functions as a control plane for agent-assisted development, managing project state (stories, decisions, traces) through a combination of Git-native documents and a durable SQLite layer. The system enforces architectural rules and invariants via mechanical validation rather than process bureaucracy, distinguishing between a generic 'Core' payload and product-specific 'Consumer' work (e.g., Symphony). Key capabilities include self-updating binaries, semantic changeset replay for auditability, and explicit agent skills for onboarding and auditing. Architecturally, the codebase follows a clean separation of concerns with a Rust workspace containing `harness` (core logic) and `harness-cli` (interface). It utilizes a repository pattern to decouple business logic from database access, implementing transactional logging via JSONL changesets that support idempotent replay. The tool manages its own lifecycle through a robust installer that supports cross-platform profiles (Core vs. Core+CLI) and handles file conflicts via deterministic three-way merges. Evidence capsules and epoch fencing ensure data integrity during agent interactions and state transitions. The project demonstrates a high level of engineering maturity through its focus on reproducibility and trust boundaries. It includes extensive documentation (ADRs, execution plans, stories) and a comprehensive test suite covering installer contracts, changeset rebuilds, and boundary checks. The separation of the 'Symphony' product into a distinct repository while retaining protocol primitives in the core highlights a mature approach to library vs. application boundaries.

This repository showcases a production-tested infrastructure for enhancing Claude Code, an AI-assisted development tool. It provides a system for automatically suggesting and enforcing development best practices via hooks that trigger on user prompts and file operations. The core innovation is a skill auto-activation mechanism that uses contextual cues (keywords, intent, file paths, content) to recommend relevant guidelines, with a two-try blocking model for mandatory skills to ensure compliance without excessive nagging. The infrastructure is built around modular skills (each under 500 lines with progressive disclosure), specialized agents for complex tasks, and a dev docs pattern that maintains context across sessions using vector search. It also includes an AI provider abstraction layer supporting multiple LLMs (Gemini, OpenAI, Anthropic, Ollama) for flexible integration. Designed as a reference library, users are encouraged to copy and adapt the hooks, skills, and configurations into their own Claude Code setups, with setup and verification scripts to streamline installation. While not a standalone application, this collection of patterns and tools represents a sophisticated approach to scaling AI-assisted development practices in enterprise environments. Its strength lies in the reusable components: the hook-based skill system, modular skill documentation pattern, and provider abstraction layer, all demonstrating non-obvious craftsmanship in balancing automation with developer autonomy.

This repository is a comprehensive configuration and extension set for Anthropic's Claude Code AI coding assistant. It enhances Claude Code with a sophisticated hook system for event-driven automation, a specialized agent delegation system for task-specific AI assistants, and an activity tracking mechanism that injects context-aware guidance based on user behavior. The system also includes templates for generating project documentation and integrates with external services like Pushover for notifications. While it contains example documentation for a game project (likely 'Mystica'), its primary purpose is to provide a customizable development environment for AI-assisted software engineering.

This repository provides a collection of Claude Code plugins designed to enhance AI-assisted development workflows. It consists of four specialized utilities: deslop for removing AI-generated code artifacts, dig for conducting deep exploratory interviews to clarify plans, fix-ci for automatically diagnosing and resolving CI failures, and decomposition for breaking down complex tasks into detailed, actionable todos. Each plugin implements sophisticated patterns for improving developer productivity when working with AI assistants. The system follows a modular architecture where each plugin is self-contained with its own command implementation, skill definitions, and metadata. Plugins can be installed via Claude Code's plugin marketplace system and integrate directly into the AI assistant's workflow. The plugins leverage Claude Code's built-in tools (Read, Edit, Write, Bash, etc.) to interact with the development environment, analyze code, and make targeted improvements. Rather than being a monolithic application, it offers composable tools that address specific pain points in AI-assisted development.

This is a collection of 12 lessons on effective AI-assisted software development, derived from real-world experience with tools like Cursor, Windsurf, Cline, and Claude. It is not a software application or data research project—it is pure documentation. The lessons cover project structure, planning, problem decomposition, test writing, commit discipline, and prompt engineering. The content is clear, concise, and immediately applicable. No code or data pipelines are present; the value lies in the reusable practices and mental models it teaches.

tsumiki is a Claude Code plugin and skill library that operationalizes AI Test-Driven Development (AITDD). It exposes slash commands (`/dev-impl`, `/dev-debug`, `/ipa-security-check`, `/orchestrate`) and Markdown-based skills that guide an AI coding agent through a Red-Green-Refactor-Validation cycle. The repository is not a standalone runtime; it is a development methodology toolkit whose primary artifacts are command definitions, skill instructions, and workflow orchestration logic consumed by Claude Code.