thehardwarestore.ai

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.

OpenTeams is a multi-agent AI coding assistant platform that enables users to collaborate with multiple AI coding agents (Claude Code, Codex, Gemini CLI, etc.) in a single shared session. The system provides a local-first desktop application built with Tauri (Rust backend + React/TypeScript frontend), a standalone CLI tool (`openteams-cli`) written in TypeScript with Bun, and a web frontend. The backend is an Axum HTTP server in Rust with SQLite persistence managed via SQLx, supporting chat sessions, workflow orchestration, source control integration, approval systems, and a skill registry. The architecture is modular: a `crates` workspace separates database models, services, executors, deployment, and utilities, while the CLI uses Effect-TS for dependency injection and includes a TUI built with SolidJS. In production, the system orchestrates AI agents through the Agent Client Protocol (ACP) and provider-specific SDKs, managing execution processes, worktrees, and chat message queues. Key data flows include: user input → chat runner → agent execution (with tool calls, file edits, git operations) → event streaming back to frontend/CLI. The platform integrates with GitHub for PR review, issue tracking, and work item management, and supports custom MCP servers and skill plugins. External dependencies include SQLite, Tauri, Bun, various AI provider APIs (Anthropic, OpenAI, Gemini, Copilot), and GitHub CLI for certain operations.

CyberClaw is an enterprise-grade transparent AI agent architecture that implements a dual-watermark memory system, full behavior auditing, zero-trust execution via two-phase skill invocation, and a heartbeat task engine for scheduled operations. The system provides an interactive conversational interface where users can query time, perform calculations, manage files, execute shell commands within a secure sandbox, and utilize pluggable skills. All agent actions are transparently logged to JSONL files for monitoring and compliance. The architecture features clean separation of concerns with distinct modules for agent logic, skill loading, context management, logging, heartbeat tasks, and tool execution.

Packmind is a developer productivity platform that centralizes engineering standards and distributes them to AI coding agents. It solves the problem of scattered standards (in Slack, Notion, PR comments) that AI agents can't access and the manual copy-pasting of standards across repositories. The platform provides organization/space-based collaboration, Git provider integrations, LLM/AI service abstraction, marketplace for sharing components, deployment/target management, and change proposal workflows. It features a hexagonal architecture with clear separation of concerns across domains (accounts, organizations, spaces, standards, skills, commands, deployments), using NestJS for the backend, React for the frontend, and a separate CLI tool for local artifact management.

This repository is a comprehensive knowledge base and reference implementation for Claude Code best practices, curated by Shayan Rais. It is not a standalone application but rather a living documentation and configuration library that demonstrates how to effectively use Claude Code CLI through agents, skills, commands, hooks, and workflows. The core architecture follows a Command → Agent → Skill orchestration pattern, with working examples such as a weather reporter system and a structured RPI (Research-Plan-Implement) development workflow. The repository includes reusable configuration patterns (YAML frontmatter schemas for agents and skills), a cross-platform Python hook script for lifecycle events, detailed changelogs that track drift against official Claude Code documentation, and extensive reference guides covering settings, environment variables, MCP servers, and startup flags. While primarily a documentation and reference project, it contains runnable Claude Code configurations that can be directly adopted or adapted by other projects.

This repository is a comprehensive educational guide for Claude Code, an AI coding assistant by Anthropic. It provides copy-paste templates, Mermaid architecture diagrams, and progressive learning modules covering all Claude Code features: slash commands, memory, skills, subagents, MCP, hooks, plugins, checkpoints, advanced features, and CLI usage. The repo also includes production-ready build/validation scripts (EPUB builder, static site generator, link checker, Mermaid syntax checker), multi-language translations (Vietnamese, Japanese, Chinese, Ukrainian), CI/CD workflows, and a design system. It is not a software application itself but a knowledge base with supporting tooling. The architecture is a static documentation site with Python helper scripts and pre-commit hooks. Key entry points are the CLAUDE.md project overview and the numbered module directories (01-10). The repository is actively maintained with frequent updates tracking Claude Code releases.

chubbyskills is a personal knowledge management CLI toolkit that ingests content from over a dozen platforms (YouTube, Bilibili, X, Xiaohongshu, WeChat, Douyin, Podcasts, etc.), transcribes audio/video locally or via cloud providers, and outputs structured Markdown notes with YAML frontmatter (schema v1). The system is architected as a collection of modular "skills" (one per platform) orchestrated by a unified CLI (`tools/chubby.py`), with shared utilities in `chubby_common/`. It includes an incremental knowledge base indexer using SQLite FTS5 and optional semantic embeddings, an MCP server exposing six tools for AI agent integration, and a deterministic evidence brief generator with integrity validation. Operation is local-first: transcription uses SenseVoice and faster-whisper, video downloading uses yt-dlp, and all processing is designed to work offline. Cloud transcription (Atlas/MuAPI) and LLM enrichment (DeepSeek) are optional extensions. The repository also provides a skill installer, platform smoke testing, output validation, and retry mechanisms with credential redaction. Despite the breadth, the codebase maintains clean separation of concerns, with the shared library standing out as a reusable zero-dependency core.

This repository is a hybrid: it is primarily a curated awesome list for Claude Code resources, but it is backed by a real automation layer that treats the README as a generated artifact. The single source of truth is a CSV resource table plus a config.yaml that defines category ordering; a Python script (generate_readme.py) renders the README, ticker SVGs, and recently-added carousels. The repo also includes GitHub Actions workflows that validate new resource submissions via issue forms, handle maintainer approval/rejection commands, and automatically open PRs to add approved resources. This makes it less of a static list and more of a self-maintaining content pipeline.

This repository is a meta-skill/plugin for Claude Code that serves as a 'team-architecture factory'. It is not a traditional software application but a collection of highly structured Markdown files (SKILL.md and references) that act as instructions for the Claude Code AI to design and evolve multi-agent teams. The system takes a domain description from a user and generates agent definitions, skills, and orchestrators to automate complex workflows. It demonstrates deep expertise in multi-agent orchestration: three execution modes (workflow orchestration, persistent agent collaboration, sub-agent delegation), six team architecture patterns, and workflow-native quality patterns (adversarial verification, judge panels, loop-until-dry). The project underwent a ground-up rebuild from v1 to v2 to align with the current Claude Code runtime, removing reliance on deprecated APIs like TeamCreate. The codebase is exceptionally well-documented for its domain, with clear principles, phased workflows, testing methodologies, and migration guides. The primary artifact is documentation, not executable code, which limits traditional software engineering evaluation but the craftsmanship in structuring meta-instructions for an AI is high. It is actively maintained with recent commits, thorough changelog, and professional issue templates.

This repository provides `claude-monitor`, a privacy-first CLI application for real-time monitoring of Claude Code usage. It reads local JSONL session logs, computes token consumption, costs, and burn rates, and displays them via a Rich-based terminal UI. The tool supports multiple subscription plans (Pro, Max5, Max20, Custom), integrates with Claude Code's official rate limits via statusline capture, and offers an opt-in persistent usage warehouse for historical analysis. It is distributed as a PyPI package with command aliases `cmonitor` and `ccm`. The architecture is modular, with separate packages for CLI, core logic, data ingestion/analysis, monitoring, output formatting, terminal management, UI components, and utilities. Key entry points include the CLI main module (`cli/main.py`), snapshot builder (`output/snapshots.py`), and monitoring orchestrator (`monitoring/orchestrator.py`). The application is well-tested with over 700 tests covering snapshot, trust-layer, warehouse, UI, timezone, and CLI paths. While primarily an application, it incorporates data engineering patterns such as sessionization, percentile-based limit estimation, and a versioned JSON warehouse. The codebase demonstrates strong craftsmanship with reusable components like the P90Calculator, PricingCalculator, TimezoneHandler, and UsageWarehouse. However, it has some technical drift risks due to hardcoded pricing rates, dependency on Claude Code's internal log format, and experimental API features that may change.

GCLI2API is a production-grade API proxy that converts Google Gemini CLI and Antigravity credentials into OpenAI, Gemini, and Claude compatible API endpoints. It manages OAuth credentials with automatic rotation, error-based cooldowns, and load balancing, providing a unified interface for multiple backend services. The system includes a web-based control panel for credential management, real-time logs, configuration editing, and batch operations. The architecture is a FastAPI web application with modular routers for each API format (OpenAI, Gemini, Claude) and two backend modes (geminicli, antigravity). Format converters handle bidirectional translation between Anthropic, OpenAI, and Gemini schemas, including tool calls, streaming, and thinking blocks. Storage is abstracted via an adapter pattern supporting SQLite, MongoDB, and PostgreSQL, with optional Redis caching for credential pools. Key features include streaming support with fake streaming fallback, anti-truncation via synthetic tool calls, thought signature preservation across format conversions, and a keepalive service. The system relies on httpx for async HTTP, Pydantic for data models, and external Google Cloud APIs for OAuth and quota management. Deployment is supported via Docker, docker-compose, and platform-specific install scripts for Linux, macOS, Windows, and Termux. This is a pure application with no data research or ML components. It demonstrates strong engineering practices in credential management, format conversion, and async I/O, though it suffers from some code duplication between backend modes and limited test coverage.

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.

FigMirror is a reference-conditioned scientific figure style transfer system that iteratively generates matplotlib scripts matching the visual style of a reference figure. It operates as a multi-agent loop (Drawer/Reviewer) orchestrated by a runner backend (Codex, Claude, or Mock), with a local web UI for upload, iteration browsing, and refinement. The system is packaged as skills for both Claude Code and Codex, plus a standalone web server. While primarily an application, it has a strong research component: the prompts, aesthetic library, and evaluation benchmarks (PlotTwin-Bench) are the result of extensive empirical iteration. The codebase demonstrates high craftsmanship in its runner architecture, atomic file operations, SSE streaming, and deterministic review gates. However, it suffers from dual skill bundles (Claude/Codex) that must stay synchronized, retired artifacts, and some incomplete features (ClaudeRunner, SSE integration).

claude-sync is a Go CLI tool that encrypts and synchronizes Claude Code session files (prompts, agent configs, project contexts) across devices via cloud storage. It uses a pluggable storage backend (S3, R2, GCS, WebDAV), end-to-end encryption with age (X25519 + Argon2id key derivation), gzip compression, and state-based incremental sync. The tool provides commands for push, pull, status, diff, conflict resolution, history rebuild, and auto-sync hooks. It is distributed as an npm package that downloads a platform-specific Go binary. The architecture follows a clean internal separation: storage adapters, crypto, config, paths, syncer, and CLI commands. The code is well-tested (unit + integration) and includes CI/CD pipelines, though there is a Go version mismatch (go.mod requires 1.24, CI uses 1.21) and some security notes (plaintext credentials, no checksum verification in postinstall). The project is actively developed with a mature feature set and offers reusable patterns like the storage interface, cross-device path mapping, and three-way MCP merge.

Total Recall is a memory system for Claude Code that provides persistent, curated memory across sessions. It implements a tiered architecture with daily logs for raw capture, registers for structured domain knowledge, working memory for essential context (auto-loaded every session), and archive for historical data. The core innovation is its write gate mechanism that evaluates whether information should be saved based on whether it changes future behavior, preventing memory bloat. The system includes automatic hooks for session context loading and pre-compaction flushing, a sophisticated ID-based metadata system for tracking memory entries, and maintenance routines for pressure-based cleanup. It's designed as both a plugin and standalone installation with comprehensive slash commands for all memory operations.

prove_it is a config-driven hook framework specifically designed for Claude Code, an AI coding assistant. It intercepts lifecycle events (SessionStart, PreToolUse, PostToolUse, Stop, TaskCompleted) and git hooks (pre-commit, pre-push) to run configurable verification tasks. These tasks include shell scripts, AI agent reviews (using Claude subprocesses), and environment variable injection. The tool enforces code quality by blocking or approving AI actions based on pass/fail results, preventing premature completion of tasks. Architecture-wise, prove_it is a Node.js CLI tool (≥18) with a modular design: config loading from layered JSON files, condition evaluation (`when`) for gating tasks, git-backed churn tracking, template expansion, and session state management. The dispatcher module handles all hook events, evaluates conditions, runs tasks synchronously or asynchronously, and returns structured responses to Claude Code. A sophisticated appeal system allows developers to override AI reviewer failures via a backchannel mechanism. The codebase demonstrates strong software engineering practices: extensive unit and integration tests (using node:test), clean functional modules, and a clear separation of concerns. Key reusable patterns include the layered config merging algorithm, the template engine with conditional blocks, and the condition evaluation system with AND/OR semantics. While the tool is tightly coupled to Claude Code's hook API (introducing ecosystem drift risk), its internal architecture is well-crafted and could serve as a reference for building similar verification frameworks. The project is actively maintained with frequent releases (v0.72.0), comprehensive documentation, and real-world example projects. Its quality is solid but not exceptional due to the inherent complexity of the config surface and the reliance on external AI services. The tool effectively solves the problem of AI overconfidence in code generation by forcing verification steps to pass before progress can be made.

CCPM is a structured project management skill for AI agents (Claude Code, Codex, etc.) that enforces a spec-driven development workflow: PRD → Epic → GitHub Issues → Parallel Agents → Shipped Code. The codebase consists of markdown reference documents (plan.md, structure.md, sync.md, execute.md, track.md, conventions.md) and a set of deterministic bash scripts that read project state from .claude/ directory (PRDs, epics, tasks with frontmatter). The system does not contain any runtime code—it is purely a collection of instructions and helper scripts that an AI agent reads and executes. Its architecture is a flat skill structure following the agentskills.io standard, with no external dependencies beyond git, gh CLI, and a GitHub repository. The primary data flows are local file operations (creating/reading/updating markdown files) and GitHub API calls via gh CLI for issue creation, syncing, and closing. The skill is harness-agnostic and works with any compatible agent framework.

This repository provides a comprehensive AI-assisted UI/UX design system toolkit, primarily packaged as a skill for AI coding assistants (Claude Code, Cursor, Windsurf, Copilot, etc.) and distributed via the 'ui-ux-pro-max-cli' npm package. Its core functionality includes a curated knowledge base of 79 UI styles, 192 product color palettes, 74 font pairings, 119 UX guidelines, 22 tech stacks, and 192 reasoning rules – all stored as structured CSV files. A BM25 search engine (implemented in Python) enables domain‑aware retrieval, while a design system generator aggregates search results and applies reasoning rules to output complete design system recommendations (colors, typography, style, layout patterns, motion, anti‑patterns). The gallery Next.js app provides a browsable, filterable interface for the style catalog. Architecturally, the project is a hybrid of data curation and application delivery. The canonical source of truth lives in `src/ui-ux-pro-max/`, which is synchronised to `cli/assets/` and `.claude/skills/` via a CI‑enforced sync script. It includes sub‑skills for brand management, slide/presentation generation, banner design, icon/logo generation, and UI styling (with shadcn/ui and Tailwind integration). The CLI tool installs these skills into the appropriate AI assistant configuration directories, with platform‑specific templates for 20+ assistants. Automated workflows refresh Google Fonts and Phosphor icon catalogs weekly, and a suite of Python tests validates data contracts, stack freshness, and search relevance. Despite its active maintenance and rich functionality, the codebase suffers from significant file duplication across three directory trees, hardcoded version pins for frameworks, and test brittleness tied to exact data row counts. The design system generator and search engine are well‑crafted and reusable, but the overall project is tightly coupled to its curated CSV schema and the ecosystem of AI coding assistants. It represents a mature, production‑grade toolkit for developers who want AI‑assisted design generation, but the heavy duplication and external API dependencies (Gemini, Atlas Cloud, MuAPI) introduce moderate technical drift risk.

SuperClaude Framework is a context engineering framework for Claude Code, transforming it into a structured development platform via behavioral instruction injection. It provides 30+ slash commands, 20+ domain-specific agents, 7 behavioral modes, and 8 optional MCP server integrations. The core delivery mechanism is a Python package (pip-installable) with a CLI that installs Markdown instruction files into Claude Code's configuration directory, enabling automated workflows, quality gates (confidence checks, self-validation, error learning via Reflexion), and parallel execution patterns. The architecture is hybrid: a Python package (src/superclaude/) provides the CLI, pytest plugin, and execution engine, while a parallel plugin directory (plugins/superclaude/) holds the Markdown-based agent definitions and command instructions. This dual structure indicates an ongoing migration from a pure Markdown configuration to a Python-backed system, with significant duplication between the two trees. The framework is designed for token efficiency, employing progressive context loading, symbol compression, and lazy-loaded skills to reduce startup costs. It includes a Project Manager Agent (PM Agent) that orchestrates sub-agents, enforces PDCA cycles, and maintains session persistence with cross-session learning via JSONL-based memory. While the repo contains extensive research documentation (token efficiency, parallel execution, migration studies), the core deliverable is a developer tool, not a data pipeline.