thehardwarestore.ai

This monorepo (Nx) implements a complete ecosystem for managing and distributing 'skills'—curated, structured instructions for AI coding agents. The codebase reveals a production CLI (`agent-skills`) built with Ink/React for interactive terminal UI, an MCP server (`agent-skills-mcp`) for agent-in-context skill lookup via FastMCP, a Next.js marketplace website for browsing, and a skills catalog with over 150 skill definitions across architecture, cloud, development, quality, security, and other domains. The core library (`@tech-leads-club/core`) uses a clean hexagonal architecture with port/adapter separation for file system, HTTP, environment, logging, and shell operations, enabling testability and future portability. The CLI supports interactive and non-interactive install/remove/update workflows with atomic lockfile writes, append-only audit logging, and content-hash integrity verification. The MCP server implements a progressive disclosure pattern (search → read → fetch/prepare) to minimize token usage, with registry caching and ETag revalidation. The marketplace is highly SEO-optimized with JSON-LD structured data, breadcrumbs, and static generation. Property-based tests (fast-check) for UI components and cache boundaries demonstrate strong engineering discipline. The skills catalog itself is the core product—each skill is a markdown file with YAML frontmatter, optional scripts/references, and trigger phrases, all validated by a custom validation tool and security-scanned in CI. While the architecture is solid and the codebase is actively maintained with frequent releases, some areas show drift risk: duplicate validation scripts across skills, hardcoded benchmark data in the marketplace, and tight coupling to external CDN availability for registry downloads. The cloud-deploy skill references an enormous set of Cloudflare product docs that will inevitably become outdated. Overall, this is a well-crafted, modern TypeScript monorepo that effectively solves the problem of giving AI agents reusable, safe, and discoverable instructions.

A comprehensive tool for removing watermarks from Gemini AI-generated images and videos. It provides multiple deployment options: a Chrome extension, a Tampermonkey userscript, an npm package (SDK), a CLI, and an AI agent skill. The core algorithm uses mathematically exact reverse alpha blending with pre-calibrated alpha maps to remove the Gemini logo without degrading underlying image quality. The system includes a sophisticated pipeline for detection, candidate selection, alpha trial, repair, and quality evaluation, with support for both image and video watermark removal. The repository is hybrid: while primarily an application, it contains an extensive set of data/research scripts for benchmarking, alpha profile analysis, video residual analysis, and release quality gating, reflecting a data-driven development approach. The codebase is well-structured with clear separation of concerns, extensive test coverage, and modular components that can be reused in other projects.

This is a Rust CLI tool called `facts` that implements a fact-driven development workflow. It manages a `.facts` file containing atomic, verifiable claims about a project, each with lifecycle tags (`@draft` → `@spec` → `@implemented`) and optional shell commands for automated verification. The tool provides a rich set of subcommands for listing, adding, editing, removing, linting, and checking facts, along with a terminal UI (TUI) built with Ratatui and Crossterm. It integrates with AI coding agents (Claude, Codex) to verify manual facts via an XML-based verdict protocol, and ships with bundled agent skills for discovery, refinement, and implementation. The project is self-hosting—it uses its own `.facts` file to describe itself—and is distributed via npm, PyPI, and direct install. The architecture is a monolith with clearly separated modules for parsing, writing, ID generation, tag filtering, file locking, and agent communication. The codebase demonstrates strong engineering practices: deterministic round-trip serialization, atomic file writes, advisory locking with stale detection, and a boolean expression evaluator for tag filters. It is actively maintained with a comprehensive test suite and CI/CD pipeline.

This repository is a curated resource hub (awesome list) for Claude Code skills, targeting Chinese-speaking developers. It contains 20 original SKILL.md files that can be directly installed into Claude Code, plus a curated directory of 441+ third-party skills organized by categories such as development efficiency, content creation, platform operations, and more. The repo includes a static website (docs/index.html) that mirrors the README, automated Python/Node.js scripts for synchronization, and rigorous CI/CD pipelines with pinned dependencies and comprehensive tests. It is not a running application but a content curation project with high-quality infrastructure for maintaining consistency between the README and the website, including atomic file writes and strict anchor validation. The original skills are well-structured and cover practical scenarios like code review, documentation generation, API testing, and refactoring, all in Chinese.

EvoMap Evolver is a self-evolving AI agent engine built as a Node.js >=22.12 CLI daemon. It runs a genome-evolution loop: scans session memory/logs, extracts signals, selects 'Genes' (prompt/strategy patches) and 'Capsules' (validated skill bundles) from a local JSON/JSONL asset store, generates GEP-framed prompts for coding agents (Claude Code, Codex, Cursor, Gemini CLI, opencode, Kiro), executes those sessions, and records outcomes in a memory graph to adapt future selections. The system solidifies successful patterns into new reusable assets, auto-distills failure patterns and SKILL.md files into Genes/Capsules, and can open PRs or run validation sandboxes against candidate changes. It also ships an LLM proxy (Anthropic/OpenAI/Gemini/Vertex/Ollama routes with trace encryption and token-savings attribution), an ATP (Agent Transaction Protocol) marketplace for buying/completing agent tasks with auto-buyer and auto-deliver, a validation daemon for decentralized task verification, a local Web UI dashboard, and an experiment runner for A/B comparing evolution strategies. Hub synchronization (SSE event delivery, mailbox sync, OAuth device login) connects nodes to the evomap.ai network, but the system is designed to degrade gracefully into fully offline solo mode.

This is a VS Code extension that provides a unified interface for discovering, installing, and managing MCP (Model Context Protocol) servers and AI agent skills. It integrates with multiple sources: the Official MCP Registry, skills.sh, GitHub repositories, and CloudMCP (a hosted alternative). The extension offers a chat participant (`@mcp`) for natural-language search and install, and a sidebar webview with search and management tabs. It supports 30+ AI coding agents including GitHub Copilot, Claude Code, and Codex CLI. The architecture consists of a TypeScript extension backend (using esbuild for bundling) and a React 19 + Vite + Tailwind CSS + shadcn/ui frontend, communicating via typed RPC messages. Telemetry is collected anonymously with standardized error classification. The codebase is well-structured with clear separation of concerns, CI/CD pipelines, and unit tests.

This Rust codebase is an LLM inference proxy and tool-routing gateway. It exposes an OpenAI-compatible HTTP API (`/v1/chat/completions`, `/v1/models`) and forwards requests to multiple upstream LLM providers (OpenAI, Anthropic via direct/Bedrock, Vertex AI, vLLM DeepSeek, TokenHub) through a unified Provider trait. A QUIC server (s2n-quic, TLS 1.3) accepts connections from serverless tools written in Go or Node.js; the agent loop performs multi-round tool calling, invokes registered tools, and streams results back in SSE format. A separate model API proxy layer handles non-chat endpoint shapes (messages, responses, generateContent) with usage extraction and injection. Architecturally, the system is a Rust CLI binary (`yomo`) with three subcommands: `serve` starts the HTTP API and QUIC zipper, `init` scaffolds serverless tool projects, and `run` launches a tool subprocess and connects it to the zipper. Tool registration is done at runtime over QUIC and stored in-memory, so state is lost on restart. The HTTP layer is Axum, auth is bearer-token, and observability uses OpenTelemetry. The serverless execution model is subprocess-based: it creates temporary directories, runs `npm install`/`go mod tidy`, and exchanges metadata over stdout. Reusable engineering exists in the framing protocol (`io`), streaming SSE mappers, usage aggregation logic, TLS configuration, and provider error separation. However, the provider implementations are duplicated and brittle to upstream API drift; hard-coded model limits, ephemeral tool state, and near-empty serverless stubs reduce long-term maintainability. No integration tests against real upstream APIs were found in the reviewed surface.

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 curated 'awesome list' and comprehensive documentation hub for the Agent Skills ecosystem. It does not contain any executable application code, data pipelines, or machine learning models. Instead, it serves as a static directory of links to official projects (Anthropic, OpenAI, Google, Vercel, etc.), community-contributed skills, tutorials, and best-practice guides. The core value lies in its translated and aggregated documentation, including detailed guides on Skill design patterns, practical experience from Anthropic, and a complete build guide. It is a reference resource, not a software tool. Engineers and AI agents should use this repo to discover the Agent Skills specification, find relevant skills to install, and learn how to create their own. It is not a library or framework to be imported.

Chamilo LMS is a PHP-based Learning Management System built with Symfony framework and Vue.js frontend. It provides course management, assessments, gradebook, attendance tracking, and collaboration tools through a modular plugin architecture. The system features RESTful API endpoints consumed by Vue services, Doctrine ORM for data persistence, and supports over 60 languages. While actively modernizing with Vue.js 3 and Symfony 6, it maintains legacy PHP components alongside new implementations, creating some technical debt from gradual migration. The plugin system follows a standardized structure enabling extensibility, and the codebase demonstrates solid engineering practices with clear separation of concerns between CoreBundle, CourseBundle, and PluginBundle.

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.

This is a Claude Code plugin and resource library, not a deployable application or data pipeline: 27 slash commands, 9+ autonomous skills, 3 auditor subagents, and extensive prompt-engineering documentation that extend the Claude Code CLI. In production, the system functions as configuration-as-code content consumed by the Claude Code runtime — teaching the assistant how to create executable plans (PLAN.md is the prompt itself), chain meta-prompts, build MCP servers, manage subagent orchestration, run autonomous coding loops (the 'Ralph' technique), and apply iOS/macOS development expertise. The only real executable code is a handful of self-contained scripts: a TypeScript CLI for The Pirate Bay API, Python/TypeScript MCP server templates, project scaffolding shell scripts, and autonomous loop iteration scripts. The dominant architectural pattern is the 'router skill': a SKILL.md entry point with essential principles, an intake gate, a routing table, and progressive disclosure into workflows, references, templates, and scripts — implemented consistently across all skills with pure XML prompt structure and checkpoint-based plan execution. Despite being classified as an application (there is no data-research character — no notebooks, datasets, or pipelines), its substance is largely authored content rather than software. The craft is strong: consistent XML schemas across files, a genuine QA layer (skill-auditor agents), secret hygiene practices, and a well-developed checkpoint/auth-gate protocol for safe automation. However, the repo carries real drift risks: duplicated reference files (research-pitfalls.md, cli-observability.md are copied across skills), dangling references to not-yet-created files, tight coupling to Claude Code's evolving plugin API, version-specific Apple platform guidance that will age, and the included The Pirate Bay skill which depends on a fragile third-party API and presents a content-policy concern. An engineer seeking to reuse this should extract the self-contained templates and scripts (MCP server scaffolding, loop automation) and treat the reference material as opinionated prompt guidance rather than canonical documentation.

This is a comprehensive Chinese-language technical knowledge base and interview preparation guide for Java backend engineers. It covers Java core, JVM, concurrency, databases, distributed systems, system design, and increasingly AI/Agent engineering. The site is built with VuePress 2 and includes custom components for lazy-loading Mermaid diagrams, content unlocking, and image preview. While primarily a documentation site, it contains reusable Vue components and a DocSearch indexing script. The content is well-structured with consistent frontmatter, cross-references, and version-aware updates. However, the massive scope and fast-moving AI topics present maintenance challenges.

This is the official curated marketplace and development toolkit for Claude Code plugins, maintained by Anthropic. It contains both first-party plugins (e.g., claude-security, code-modernization, hookify, skill-creator, security-guidance) and third-party channel integrations (Discord, iMessage, Telegram, GitHub, Linear, etc.). The repository serves dual roles: as a plugin registry with standardized packaging (SKILL.md, agent definitions, hooks, commands, MCP servers) and as a meta-development kit providing tools for authoring and validating new plugins. The codebase is predominantly instructional content for Claude Code (markdown workflows, agent system prompts, skill references) orchestrated through hooks, workflows, and commands. While many plugins contain only reference documentation and no executable code, security-critical plugins like claude-security and security-guidance include sophisticated Python/JavaScript logic for vulnerability scanning, patch generation, session state management, and git diff analysis. The CI pipeline enforces licensing (Apache 2.0), validates plugin structure, checks MCP URL liveness, and manages SHA-pinned external plugin versions. The project is actively maintained but exhibits technical drift from duplicated access-control logic across channel servers, hardcoded model references, and reliance on Claude Code CLI internals that may change between versions.

This repository is a collection of Agent Skills for AI coding assistants (Claude Code, Codex, OpenCode) that enables them to interact with Obsidian vaults. It contains six skill definitions: obsidian-markdown (creating Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties), obsidian-bases (creating database-like .base files with views, filters, formulas), json-canvas (creating visual .canvas files with nodes and edges), obsidian-cli (interacting with Obsidian via CLI for note management and plugin development), defuddle (extracting clean markdown from web pages), and knap (rendering Markdown templates from structured data). Each skill is a self-contained markdown file following the Agent Skills specification, providing detailed syntax, examples, and validation steps. The repository is purely documentation with no executable code, serving as a knowledge base that AI agents use to perform Obsidian-related tasks on behalf of users.

This repository is a content-based Claude Code skill package rather than a conventional software service. It contains two agent skills — /tutor-setup and /tutor — that an LLM coding assistant executes using filesystem and WebFetch tools. tutor-setup scans a working directory, auto-detects whether it contains source code or documents, and generates an Obsidian StudyVault: concept notes, dashboards, exam-traps, and practice questions, with strict rules for source traceability, tag registries, wiki-linking, and folder conventions. tutor-setup operates through detailed phase models (D1-D9 for documents, C1-C9 for codebases), and codebase mode produces architecture maps, module notes, API surfaces, and onboarding exercises. tutor then reads that vault and runs interactive 4-question quiz rounds, updating concept-level progress files and a compact dashboard; it supports diagnostic, weak-area drill, section review, and hard-mode sessions, all governed by a zero-hint quiz design rule in references/quiz-rules.md. The implementation is almost entirely Markdown instructions plus two small Bash install/uninstall scripts; there is no compiled code, test suite, CI pipeline, or dependency manifest. The operational logic is prompt/instruction engineering that Claude Code interprets. External dependencies are Claude Code (or a compatible npx-skills-aware assistant), Obsidian for reviewing generated vaults, pdftotext/poppler for PDF extraction, and the assistant's built-in Read, Write, Edit, Glob, Grep, Bash, WebFetch, and AskUserQuestion tools. The data flow is unidirectional: source files in the CWD are scanned and extracted, transformed into StudyVault markdown, then quiz answers update concept files and dashboard aggregate stats. The skill enforces a CWD boundary to avoid accessing files outside the working directory. The strong point is unusually detailed and internally consistent procedural specification: mandatory source-content verification, per-topic coverage rules, tag registries, active-recall fold callouts, interlinking requirements, and self-review quality checklists. Weaknesses are the absence of automated tests/CI and versioned dependency pins, and behavior ultimately depends on the host LLM following prompt rules correctly. The README also references a different GitHub owner (RoundTable02) than the repository path (bevibing/tutor-skills), indicating a fork or repackage whose provenance should be verified before use. Overall, it fulfills its stated purpose well, and several Markdown modules are genuinely reusable as templates for other agent skills, study-vault generators, or adaptive quiz systems.

WarriorJS is a terminal-based educational game that teaches JavaScript and TypeScript through interactive coding challenges. Players write a `Player` class with a `playTurn(warrior)` method to control a warrior navigating grid-based towers filled with enemies, captives, and stairs. The game runs as a CLI application using Ink (React for terminal UI) and relies on a sandboxed code execution environment via Node's `vm` module and esbuild. The monorepo structure includes a CLI app, game engine (`@warriorjs/core`), abilities library (`@warriorjs/abilities`), units library (`@warriorjs/units`), scoring module, spatial math helpers, effects, and two sample towers. The codebase demonstrates clean architecture with well-separated concerns, extensive test coverage, and reusable patterns such as the ability/effect binding factory and sensed space wrappers. However, the project has been inactive since 2018 (last release v0.14.0), the documentation website uses the deprecated Docusaurus v1, and there are some mixed module system usages. Despite being stale, the game engine and ability system remain functional and could be extracted for similar turn-based coding games or sandboxed code execution.

This repository is a modern, minimalist portfolio template built with Astro and Tailwind CSS v4. It provides a single-page static site with sections for hero, about, projects, experience, education, and footer, all driven by a centralized configuration file (`src/config.ts`). The architecture is straightforward: Astro components render data from the config, with conditional section visibility based on data presence. The template uses TypeScript for type safety, Tabler Icons for social links, and IBM Plex Mono font. It is designed for easy customization by editing the config file, making it suitable for developers who want a quick, professional portfolio site. The codebase is clean and follows Astro best practices, but it is essentially a boilerplate template with no complex logic or reusable components beyond the template itself. It is not a production application but a static site generator template.

This repository is the documentation site for Factory, an AI-native software development platform centered on the Droid CLI. It contains bilingual (English and Japanese) documentation covering CLI usage, enterprise deployment, integrations, skills, hooks, and benchmarks. The repository also includes CI/CD workflows, changelog automation scripts, and a translation pipeline. While no application source code is present, the documentation is highly structured and includes reusable patterns for skill definitions, hooks, and plugin packaging. The codebase is actively maintained with frequent updates, but exhibits moderate technical drift due to rapid version churn, model name instability, and bilingual documentation divergence.