thehardwarestore.ai

GSD (Get Shit Done) is a mature, actively maintained meta-prompting and context-engineering system that orchestrates AI coding agents (Claude Code, OpenCode, Gemini, Codex, Copilot, Cursor, and 9+ other runtimes) through a structured milestone → phase → plan → execute → verify workflow. The system solves context decay in long AI-assisted coding sessions by maintaining a `.planning/` artifact tree (ROADMAP.md, STATE.md, PLAN.md, SUMMARY.md, VERIFICATION.md) and dispatching focused subagents with fresh 200K-token contexts for each discrete task. The codebase is a dual-package npm monorepo: a legacy CJS CLI (`get-shit-done-cc`) and a modern TypeScript SDK (`@gsd-build/sdk`) with a query-registry architecture, runtime-bridge sync primitive, and generated CJS mirrors that keep both surfaces in parity. The architecture is built around a 'thin orchestrator' pattern where slash-command entry points delegate to workflow markdown files that spawn specialized subagents (planner, executor, verifier, researcher, debugger, code-reviewer) via a registry-backed `gsd-sdk query` dispatch layer. The SDK exposes a `GSD` class that composes plan parsing, config loading, prompt building, and session running into `executePlan()` and `runPhase()` calls, with event-stream telemetry and CLI/WS transports. A `synckit`-based synchronous bridge (`executeForCjs`) lets CJS callers invoke async SDK handlers without subprocess overhead. The query registry resolves commands through a multi-strategy topology (exact, alias, dotted, spaced) with native handlers and transparent CJS fallback. What distinguishes this codebase is the obsessive engineering around installer migrations (transactional, with rollback and user-prompt resolution), cross-runtime path projection (PowerShell/cmd/Git-Bash/POSIX), adversarial parser testing (hostile frontmatter/roadmap fixtures), atomic state writes with file locking, prompt-injection scanning hooks, and a changeset-fragment workflow that eliminates CHANGELOG merge conflicts. The test suite is exceptionally thorough — 400+ test files covering mutation-resistant config validation, golden parity between CJS and SDK, security prompt injection, worktree safety, and regression coverage for 3800+ tracked issues. The system supports 15 AI runtimes with runtime-aware artifact layout, model-profile resolution, and per-agent model overrides.

This repository is a production-grade meta-framework for building and managing AI agent plugins across multiple coding assistants (Claude Code, Codex, Cursor, OpenCode, Gemini, Copilot). It establishes a single source of truth in Markdown (`plugins/`) that is compiled into harness-specific artifacts via Python adapters. Key features include a security layer using Cedar policies for tool-call authorization and signed audit receipts, a project management system (`conductor`) for track-based development, and a quality evaluation framework (`plugin-eval`) using static analysis and LLM-based scoring. It solves the fragmentation problem of AI agent ecosystems by enforcing portability and governance.

oh-my-antigravity (OmA) is an extension for Gemini CLI that adds a structured, role-driven multi-agent workflow layer on top of the base CLI. It transforms a single-session AI assistant into an orchestrated engineering pipeline with roles such as architect, planner, executor, reviewer, and verifier. The extension provides over 40 slash commands, 11 retained deep-work skills, 13 sub-agent definitions, and two shipped hooks (silent BeforeModel model router and AfterAgent learn-signal filter). The architecture is entirely extension-native, relying on Gemini CLI's extension manifest (`gemini-extension.json`), command definitions in TOML files, agent prompts in Markdown, and hook scripts in JavaScript. The primary data flow is user input → `/oma:*` command → staged pipeline (intent → workspace → team-assemble → team-plan → team-prd → team-exec → team-verify → team-fix → loop) with persistent state in `.omg/state/`. The codebase demonstrates strong engineering discipline with version consistency checks, skill metadata validation, atomic file writes, environment-variable-driven runtime controls, and thorough documentation. It is not a data research project; it is a workflow automation and context-engineering pack for an AI terminal agent.

This repository is a comprehensive, tool-agnostic skills and steering pack that teaches AI coding agents to perform AWS Well-Architected Framework reviews. It contains a curated reference corpus of over 2,000 markdown files (6 pillars, 307 best practices, 27 industry lenses) derived from official AWS documentation, plus per-tool adapters for 15 AI coding assistants (Claude Code, Cursor, Codex, GitHub Copilot, etc.). The repo also ships production-grade supporting tooling: a CI gating tool (wa-ci) that diffs structured review JSON against a baseline and blocks PRs on regressed gaps; an evaluation harness with blind multi-reviewer adversarial adjudication; a crawler to regenerate the reference corpus from AWS docs; and one-command installers. The core skill dispatches six parallel subagents (one per pillar) to keep the 2.2 MB corpus out of any single context window. Despite the extensive content, the repository has notable maintenance challenges: the reference snapshot will drift as AWS documentation evolves (users are responsible for regeneration); lens files are duplicated across `powers/` and `skills/` directories creating a sync hazard; and the flagship skill is explicitly deprecated in favor of a successor in Agent Toolkit for AWS. The evaluation harnesses and CI tooling are well-crafted and reusable, but the sheer scale of markdown files and platform-specific adapters makes ongoing synchronization complex.

Agentic Awesome Skills is a curated marketplace infrastructure for AI coding agent skills—instructional playbooks (SKILL.md files) with accompanying scripts and assets. The repository provides a multi-platform distribution system supporting Claude Code, Cursor, Gemini CLI, Codex CLI, Kiro, and others. It includes a React+Vite web application for browsing, searching, and installing skills; a CLI and MCP server for local agent integration; and a comprehensive validation, security scanning, and release pipeline. The codebase implements a transaction engine with deterministic content addressing (canonical JSON + SHA-256), durable state management, and schema-validated skill installation, forming a robust foundation for agent-owned tool selection and workflow planning. While the core infrastructure (AAS v1 library, MCP server, catalog search) demonstrates high craftsmanship and reusability, the repository is hampered by massive file duplication: identical skill scripts and governance modules appear under `skills/`, `plugins/agentic-awesome-skills/`, and `plugins/agentic-awesome-skills-claude/`, creating synchronization risk. Some skills contain hardcoded paths or incomplete boilerplate. The repository is actively maintained with CI/CD, automated audits, and security scrutiny, though the sheer volume of 2,474+ skills imposes ongoing maintenance burden. Overall, it is a well-engineered, production-quality skill distribution platform with notable reusable design patterns.

This repository is a curated collection of Claude skills—markdown instructions and supporting scripts that enable Claude (or any MCP-compatible agent) to perform a wide range of tasks, from document manipulation (DOCX, PPTX, PDF, XLSX) and GIF animation to web testing, MCP server building, and automation of hundreds of third-party SaaS services via Composio's Rube MCP. The repo is primarily a skill library with a standardized template for each skill, but it also contains several well-crafted Python libraries (e.g., easing functions, particle systems, OOXML validation frameworks) that are genuinely reusable. The architecture is modular: each skill lives in its own directory with a SKILL.md and optional scripts. The heavy reliance on external services (Composio, Rube MCP) and the massive duplication of templated skill documentation introduce maintenance risks, but the core utility scripts demonstrate solid engineering craftsmanship.