thehardwarestore.ai

rtk is a Rust CLI proxy and hook system designed to minimize LLM token consumption for AI coding agents by filtering and compressing the output of shell commands. It operates as a command wrapper that intercepts commands like git status, pytest, or cargo test before they reach an agent's context window, runs the real tool, and emits a compact, token-optimized summary while preserving diagnostic value. The tool includes a sophisticated architecture: a core runner with a 'never-worse' guard that ensures filtered output never exceeds raw output in size, tiered parsing (JSON → regex → passthrough) for dozens of ecosystems, a declarative TOML filter DSL compiled into the binary, and a SQLite-based recall store for content-addressed output retrieval. It also features a permission model for hooks that can allow, deny, or ask on command rewrites, and integrates with 18+ AI agents (Claude Code, Copilot, Cursor, etc.) via custom hook formats. The repo is a single Rust binary with subcommands for execution, analytics (rtk gain), and session discovery (rtk discover), supported by an extensive test suite with fixture-based golden tests. While not a data research project, it incorporates data-driven elements like token savings accounting and telemetry, but its dominant character is a high-craft CLI application.

Herdr is a terminal workspace manager designed for AI coding agents. It features a client-server architecture where a persistent background server manages terminal panes, workspaces, and tabs, while thin clients (TUI and CLI) connect via Unix sockets/named pipes. The system enables detaching and reattaching sessions without losing agent state, provides agent-native control through CLI/socket API for spawning and managing AI agents (Claude, Codex, Copilot, etc.), and maintains session persistence across restarts and network disruptions. It includes a plugin system for extensibility, comprehensive AI agent integrations, and a rich terminal interface built with Ratatui.

Claude HUD is a real-time statusline plugin for Claude Code, the AI coding assistant. It runs as a subprocess and renders a multi-line or compact HUD below the input field, displaying context usage, rate limits, active tools, running agents, todo progress, git/jj status, memory usage, and cost tracking. The system reads session data from stdin (JSON payload) and optionally parses the Claude Code transcript JSONL file for detailed tool/agent activity. It is highly configurable with ~60+ options for layout, colors, visibility, and i18n (English, Simplified/Traditional Chinese). The architecture is modular: a CLI orchestrator (src/index.ts) loads config, runs git/jj/memory/extra-cmd in parallel, then renders via a pure render layer. It uses no external npm dependencies, relying on Node.js built-ins and child processes. The codebase demonstrates strong engineering practices: extensive test coverage (unit, integration, golden snapshot tests), dependency injection for testability, atomic file writes for persistent state, and thorough sanitization of untrusted inputs to prevent terminal injection. It is actively maintained with frequent releases and CI/CD pipelines.

Paseo is a full-stack, multi-platform development environment for orchestrating AI coding agents (Claude Code, Codex, OpenCode, Pi, etc.) from a unified interface. It consists of a daemon server that manages agent sessions, workspaces, terminals, and git operations; a cross-platform client built with React Native/Expo (iOS, Android, web, Electron desktop); a CLI tool; and a plugin ecosystem. The architecture is provider-agnostic, supporting multiple AI backends through a common protocol layer. The codebase demonstrates high engineering craftsmanship with extensive test coverage, clean separation of concerns, and reusable patterns like the plugin SDK, protocol schemas, and platform abstraction layers. It is actively maintained with rapid release cycles and a large community.

CPA Manager Plus is a full-stack, self-hosted management panel for AI gateway proxies (CPA / CLIProxyAPI). It provides a rich React dashboard to monitor usage across multiple AI providers, manage credentials and API keys, configure provider-specific settings, automate quota cooldowns and account health actions, and inspect credential health via Codex/xAI probes. The Go backend persists all data in SQLite, runs background workers for data rollups, migrations, and collector polling, and exposes a comprehensive REST API. The codebase is well-structured with a clean layered architecture (HTTP handlers, services, repositories), extensive unit and integration tests in both Go and TypeScript, a demo mode with synthetic data, and i18n support. It is actively maintained with a rigorous release process and frequent updates.

A well-crafted, self-contained application for analyzing and visualizing Claude Code usage logs. The system reads local JSONL transcript files (written by Claude Code regardless of subscription plan), parses them into a local SQLite database, and surfaces token counts, cost estimates, session histories, and subagent attribution through both a terminal CLI (`python cli.py today` / `stats` / `week`) and a rich single-page web dashboard (`python cli.py dashboard`). The dashboard is served by a stdlib-only Python HTTP server and bundles all HTML/JS/CSS inline, using Chart.js from CDN for visualizations. A companion VS Code extension embeds the same dashboard as a sidebar webview, auto-discovering the Python runtime via a flexible install-mode resolution chain (bundled `.py` sources, Homebrew shim, workspace clone, or explicit path). Architecturally, the code is split into three top-level Python modules—`scanner.py` (JSONL parsing, SQLite schema management, incremental scanning with deduplication by message ID), `cli.py` (CLI dispatch with per-turn cost calculation using a tiered pricing lookup), and `dashboard.py` (HTTP server with an embedded SPA). There are no third-party Python runtime dependencies; the VS Code extension uses TypeScript with minimal Node.js dependencies (only `@vscode/vsce` for packaging). The test suite is comprehensive, covering unit, integration, and HTTP-level scenarios across all three Python modules plus the entire TypeScript extension. A strict version parity test (`test_version.py`) enforces lockstep between the Python constant, the CHANGELOG heading, and the extension's `package.json`. Notable craftsmanship includes: the incremental scan engine that tracks file mtimes and line counts, processes only new/changed lines, and deduplicates streaming events via a conditional unique index on `message_id`; the one-time topic backfill mechanism for schema upgrades that re-reads only title records without touching token totals; and the VS Code extension's server-manager with configurable readiness probes that distinguishes the dashboard's JSON response shape from arbitrary HTTP 200s. The project is actively maintained with frequent releases, a weekly triage automation, and a detailed AGENTS.md that documents non-obvious invariants (streaming dedup, session model priority, cost calculation per-turn).

Patina is a deterministic, pattern-based AI text detection and humanization tool, primarily targeting Korean but supporting English, Chinese, and Japanese. It operates as a Node.js CLI (`patina-cli`), a web API (Vercel serverless), and an agent skill (Claude Code, Codex, Cursor). The core workflow: analyze source text for AI-typical patterns (stylometry, lexicon, discourse tells), build a structured prompt, invoke an LLM backend (OpenAI, Claude, Gemini, Kimi, etc.), verify meaning preservation (MPS/fidelity), and output a humanized rewrite with a deterministic audit trail. It enforces strict meaning guards (number safety, polarity, causation) and provides scoring, diff, and verification modes. The architecture is modular: a shared deterministic analysis core (`src/features/`) runs identically in Node and browser; a prompt builder assembles context from pattern packs, document types, and personas; multiple backend adapters abstract LLM invocation; and a verification pipeline ensures fidelity. The project includes extensive research infrastructure (benchmarking, human evaluation panels, corpus management) and a commercial Pro tier with Polar license validation. It is well-tested with over 200 unit/e2e tests, CI/CD, and security hardening (SSRF guards, input fencing, atomic writes).

This repository is a meta-application: a curated, opinionated framework of prompts ('skills') designed to govern the behavior of AI coding agents like Claude Code and GitHub Copilot Codex. It does not contain traditional application code but instead defines a structured software development methodology executed by an AI agent. The system transforms an AI agent from a simple chat interface into a disciplined, stateful development partner that plans, implements, reviews, and maintains code according to explicit workflows. The architecture is a monolith of interdependent markdown files organized into a skill hierarchy. The `ask-matt` skill acts as a router, directing user requests to the appropriate workflow. Core primitives like `grilling` (structured interview) and `domain-modeling` (glossary/ADR creation) are reused by higher-level skills. Execution engines like `implement` and `code-review` drive the actual development cycle. A key innovation is the 'stateful workspace' pattern, where the agent writes decisions to files (GLOSSARY.md, ADRs, specs) to maintain context across sessions and manage token limits. The codebase demonstrates exceptionally high craftsmanship in prompt engineering, with clear patterns for context management, sub-agent delegation, and human-in-the-loop interaction. It is actively maintained with a detailed changelog. However, its complexity is a double-edged sword: the tight coupling between skills and the reliance on specific model behavior creates a fragile ecosystem. The documented open bugs and high cognitive load for users are significant risks, preventing it from achieving an 'exceptional' rating despite its innovative design. The repository is a brilliant but brittle system for professional AI-assisted development.

Caveman is a multi-agent AI coding skill/plugin that compresses LLM output tokens by ~65-75% using a 'caveman' terse communication style. It's not a library or service but a distributed configuration package that installs into 30+ AI coding agents (Claude Code, Codex, Gemini, Cursor, Windsurf, Cline, Copilot, opencode, OpenClaw, etc.) through their native plugin/extension/rule-file mechanisms. The core deliverable is a set of SKILL.md prompt files that instruct models to drop articles, filler, pleasantries, and hedging while preserving exact technical substance, code blocks, URLs, and paths. The architecture is a single Node.js installer (bin/install.js) that auto-detects installed agents and dispatches to per-provider installation paths. For Claude Code specifically, it adds SessionStart and UserPromptSubmit hooks plus a statusline badge. The repo includes several sub-skills: caveman-commit (terse conventional commits), caveman-review (one-line PR comments), caveman-compress (rewrites memory files like CLAUDE.md into caveman format to save input tokens), caveman-stats (reads session logs to report real token savings), and cavecrew (compressed subagent definitions for investigator/builder/reviewer patterns). There's also caveman-shrink, an MCP middleware proxy that compresses tool descriptions. The codebase demonstrates strong engineering practices: symlink-safe flag file writes with O_NOFOLLOW and ownership verification, atomic settings.json merges with JSONC tolerance, defensive hook validation before writes, idempotent install/uninstall cycles, and a three-arm eval harness (baseline/terse/skill) for honest token compression measurement. The Python compress tool has safety guards against exfiltrating sensitive files and verifies backup integrity before overwriting. Test coverage is substantial across installer e2e flows, hook symlink safety, opencode plugin lifecycle, and config resolution order.

Agent Reach is a Python CLI tool that serves as a capability layer for AI agents, giving them one-click access to ~15 internet platforms (Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu, LinkedIn, etc.) without requiring manual setup. It does not wrap these platforms itself; instead, it selects, installs, health-checks, and routes to the most reliable upstream CLI tools (e.g., twitter-cli, yt-dlp, gh CLI, OpenCLI, mcporter) for each platform. The architecture is built around a multi-backend channel pattern: each platform has an ordered list of backends (primary + fallbacks), and the `doctor` command probes each candidate in order to determine the active backend. This design handles upstream tool instability gracefully by allowing fallback routing without code changes. The codebase demonstrates strong engineering practices: a clean Channel ABC with `check()` and `can_handle()` contracts, a robust probe system that distinguishes missing/broken/timeout/error by actual execution, security-hardened file operations (atomic private writes, symlink rejection, bounded reads), and SSRF protection in URL normalization. The CLI provides commands for installation (safe-by-default, read-only mode), diagnostics, configuration, and skill registration for agent environments (Claude Code, OpenClaw, Cursor, OpenCode). An optional MCP server exposes read-only status. Agent Reach is purely an application — there are no notebooks, data pipelines, or ML components. Its value lies in reducing the friction of giving agents internet access, with a focus on zero-config defaults for public platforms and cookie-based auth for login-required ones. The project is actively maintained, with documented responses to upstream API changes (e.g., Bilibili blocking yt-dlp, Twitter API pay-per-use).

Ruflo is a large, modular AI agent orchestration platform that extends Claude Code with a plugin ecosystem, swarm coordination, memory systems, neural learning, security enforcement, and federation. The core is a CLI tool (`npx ruflo`) that provides 26+ commands and 215+ MCP tools for managing agents, memory, security, hooks, and deployments. It includes a console UI inside Claude Code, a Svelte-based chat UI (ruvocal), and a React-based goal UI. The architecture is ADR-driven with clean separation into domain, application, and infrastructure layers across 35+ plugins and 20+ core packages. The system implements advanced patterns: CQRS for memory and swarm, circuit breakers for resilience, capability envelopes for security, Ed25519 signing for release integrity, and a harness loop for automated champion selection. It supports multi-model routing, hybrid vector search (HNSW + SQLite), watermarking (WASM-based Gumbel scheme), and neural learning (PPO, DQN, Decision Transformer). Federation enables cross-installation agent collaboration with zero-trust, HMAC-signed envelopes, and PII-gated data flow. While the codebase is well-structured and actively maintained, it has some maintenance risks: duplicate helper scripts across multiple directories, hardcoded version numbers in smoke tests, experimental alpha plugins, and a large regression test suite that requires ongoing upkeep. The plugin ecosystem is extensive but some plugins may be aspirational rather than production-ready.

OfficeCLI is a cross-platform CLI application written in C#/.NET that creates, reads, and mutates OOXML Office documents (Word .docx, Excel .xlsx, PowerPoint .pptx) without requiring Microsoft Office. It is explicitly engineered as an agent-facing tool: every document element is addressable through a uniform path syntax (e.g. /slide[1]/shape[2]), all commands emit structured JSON with error codes and suggestions, and it ships AI-skill files for Claude/Copilot/Cursor/Codex, an MCP server, plus Python and Node SDKs. The core operation model is a batch command language: documents are mutated by replayable JSON items (add/set/remove/get) executed atomically through the CLI or over a named pipe to a resident process. Architecturally the repo is a single .NET binary (embedded runtime) with a CommandBuilder layer, a DocumentHandlerFactory dispatching to three large partial-class handlers (WordHandler, ExcelHandler, PowerPointHandler), and a shared Core layer containing chart-to-SVG rendering, a formula evaluator implementing 350+ Excel functions with statistical distributions, pivot table construction, Mermaid diagram parsing, OOXML theme/table-style resolution, HTML/PNG/SVG preview rendering, and a plugin protocol for foreign-format dump-readers and exporters. Security is taken seriously: SsrfGuard validates remote fetch targets, HyperlinkUriValidator allowlists URI schemes, XmlTextValidator and DocumentLimits guard against decompression bombs and DOM sprawl, and AtomicPackageWriter prevents torn file writes. Quality is strong but carries real maintenance debt. The code demonstrates deep OOXML craftsmanship - schema-order-correct XML insertion, exact EMU unit round-trips, Lanczos-based gamma functions, and curated chart-style galleries. However, handlers are split across dozens of partial-class files that evolved from former god methods, the property API is stringly-typed (props dictionaries parsed per key), examples must be maintained in parallel shell and Python twins, and the tool deliberately tolerates 'UNSUPPORTED props' warnings (exit code 2), which can mask incomplete feature coverage. The best reuse targets are the self-contained Core utilities rather than the format handlers. The project is actively maintained with multi-platform CI publishing to npm and PyPI.

NanoClaw is a self-hosted, multi-agent AI orchestration platform that runs containerized agent sessions, integrates with a wide range of chat channels (Slack, Discord, Telegram, WhatsApp, iMessage, etc.), and provides a CLI-based admin interface. It is architected as a host service (Node.js/TypeScript, SQLite) that manages per-session agent containers (Bun runtime) isolated via Docker. The system features a typed guard/approval system for privileged actions, a plugin-based skill system for extensibility, and a structured migration framework for database schema evolution. Key operational flows include inbound message routing from channel adapters, session lifecycle management with two-DB mailbox split (inbound/outbound SQLite), container orchestration with credential injection via OneCLI vault, and host sweep for periodic maintenance. The CLI (`ncl`) provides resource CRUD with hooks, command guard integration, and a socket-based transport protocol. The project emphasizes a small, auditable codebase, skill-based feature installation over configuration, and deterministic setup/upgrade workflows with Claude Code fallback for error recovery.

This is a monorepo of six AI agent skills (SKILL.md format) for creative content generation, targeting Claude Code, Cursor, Codex CLI, and other compatible agents. Each skill is a self-contained package with agent instructions, reference libraries, scaffold scripts, and optional companion websites. The skills enable agents to: generate beautiful single-file HTML articles (beautiful-article), create and edit images via GPT-4o (gpt-image-2), retrieve knowledge from local files (kb-retriever), produce deterministic motion-graphics videos with audio (motion-showreel), design polished web pages with anti-AI-cliché rules (web-design-engineer), and build click-driven video presentations with TTS (web-video-presentation). The repository also includes release automation (zero-dependency Node scripts, GitHub Actions), a Claude Code plugin marketplace entry, and two showcase websites (Vite+React+TypeScript). While primarily an application toolkit, it contains structured prompt template libraries and design reference catalogs that resemble curated data assets. The codebase demonstrates high engineering craftsmanship with deterministic rendering pipelines, a session-based audio synthesizer, design token systems, and comprehensive test coverage for the motion-showreel pipeline.

This repository is a Claude Code marketplace providing Language Server Protocol (LSP) plugins for enhanced code intelligence. It contains self-contained plugin directories for over 30 programming languages, each with plugin.json metadata and .lsp.json LSP configuration files. The repository includes validation and synchronization scripts that ensure consistency between plugin definitions and the marketplace manifest (.claude-plugin/marketplace.json). When installed through Claude Code's plugin system, these plugins enable features like go-to-definition, find-references, hover info, and symbol search for respective languages.

The agent-skills repository provides a framework for enhancing AI coding agents with structured engineering workflows called 'skills'. Each skill encodes senior engineer best practices across the software development lifecycle (Define → Plan → Build → Verify → Review → Ship) through opinionated, process-driven guidelines. Skills are invoked automatically based on context or via slash commands and integrate with multiple AI agent tools (Claude Code, Gemini CLI, Antigravity, Codex, etc.) through tool-specific adapters. The system includes a meta-skill for dynamic skill routing, reusable agent personas for specialized tasks, and validation infrastructure to ensure consistency and prevent drift. It is designed to prevent agents from skipping critical steps by mandating concrete evidence (tests, build output) rather than relying on subjective assessments.

This repository is a production-grade Agent Skill and CLI tool for generating geometry-safe technical diagrams from structured JSON or natural language descriptions. It produces SVG, PNG, animated GIF, and offline interactive HTML outputs across 12 visual styles (11 generator-driven, 1 AI-authored) and supports 14 UML diagram types with domain-specific semantic contracts for C4, cloud, event, and ops profiles. The system features a deterministic orthogonal routing engine, composition quality gates, a validated SVG-to-GIF motion pipeline with per-style animation contracts, and a strict validation framework. It is distributed as an npm package and a Claude Code skill, with a nested mirror for `npx skills add` installation. The codebase is well-tested with extensive regression and pixel-level gates, though it suffers from some duplication between the root and skill mirror directories and hardcoded contract constants.