thehardwarestore.ai

Symphony is an autonomous AI agent orchestration service written in Elixir/Phoenix, designed to manage coding agents (like OpenAI's Codex) that resolve issues from project trackers. In production, it polls Linear, GitHub, GitLab, Jira, or Asana for new work, creates isolated per-issue workspaces, launches Codex agents with provider-native tools, manages retries and backoff, and provides live observability via a terminal dashboard and optional web UI. The architecture follows OTP best practices: a supervisor tree with GenServers for the orchestrator state machine, polling loop, status dashboard, and tracker adapters. Key entry points include the CLI (`SymphonyElixir.CLI.main`), the Phoenix web endpoint, and Mix tasks for code quality checks. Data flows are event-driven: poll tracker -> normalize to Issue struct -> dispatch via AgentRunner -> create workspace -> run Codex turns -> cleanup. The system is explicitly experimental ("engineering preview") but demonstrates strong engineering in its adapter pattern, configuration design, and test infrastructure.

AIClient2API (A2) is a production-grade API proxy/gateway that converts proprietary AI provider APIs (Gemini, Claude/Kiro, Qwen, Codex, Grok, OpenAI, and more) into standard OpenAI-compatible endpoints (`/v1/chat/completions`, `/v1/messages`, `/v1beta/models`). It supports multi-account OAuth credential pools with automatic token refresh, health checks, and fallback routing, enabling clients to break free-tier restrictions. The server is built on Node.js with a master-worker architecture for process management, a plugin system for extensibility (authentication, monitoring, usage statistics, API key sharing), and a Go-based TLS sidecar for JA3/JA4 bypass. A full web management console (static HTML/CSS/JS) provides configuration, provider management, credential file upload, interactive playground, and usage monitoring. The codebase demonstrates strong patterns (strategy, factory, adapter, converter) but suffers from some monolithic utility files, hardcoded provider mappings, and high maintenance overhead from multiple OAuth implementations.

codex-universal is a Docker image that provides a universal development environment with multiple language runtimes and tools. It allows users to configure specific versions of Python, Node.js, Rust, Go, Swift, Ruby, PHP, and Java via environment variables at runtime. The image is built from Ubuntu 24.04 and includes a wide array of development tools. The architecture is monolithic but modular via environment variable-driven configuration. Key entry points are the Docker container's entrypoint script which runs setup_universal.sh and then starts a bash shell. The system relies on external package managers (apt, pyenv, nvm, rustup, etc.) and scripts from external sources for installation.

This repository implements an MCP (Model Context Protocol) server that provides semantic code search capabilities for AI assistants. It enables developers to search their codebases by meaning rather than just keywords, using local AI models to ensure privacy and avoid external API calls. The system features semantic code search using embeddings (nomic-embed-text-v1.5 with MRL), incremental indexing with file watching, efficient SQLite-based caching of embeddings, cross-platform package version checking across 20+ ecosystems, workspace switching capabilities, and resource throttling to prevent system overload during indexing. The codebase demonstrates strong engineering practices with proper error handling, modular design, and attention to user experience.

OysterWorkflow is an open-core desktop application (Electron + React) that records a user's computer activity via Screenpipe (screen, audio, UI events), then uses multi-stage LLM calls to extract reusable, structured 'skills' (workflow graphs) that AI agents (Codex, Claude, Hermes) can execute. The system provides a full lifecycle: capture → ingest → normalize → segment → LLM-based workflow discovery → skill extraction → workflow graph generation → quality evaluation → installation into agent skill directories → execution via MCP or Hermes CLI. It also includes a cloud sync layer (Supabase), a marketplace (ClawHub), and a local runtime HTTP server for the UI. Architecturally, it is a hybrid monolith with clear module boundaries: a desktop shell (Electron main process), a local runtime server (Express), a CLI (commander), a React UI (Vite + React Flow), and a product state layer (SQLite-backed with cloud sync). The codebase is TypeScript-first, ESM-only, and heavily tested (200+ test files). It demonstrates strong engineering practices: atomic file I/O, bounded child process management, immutable revision control for workflow graphs, and a sophisticated prompt registry with versioned inheritance. While primarily an application, it contains a significant data pipeline component (ingest → normalize → segment → LLM extraction) that could be considered a research-grade feature engineering system. The repo is actively maintained with frequent commits, CI, and issue triage. Notable risks include heavy sidecar bundling (Screenpipe, Hermes, ffmpeg), rapid prompt set version churn, and a mixed licensing model (Apache 2.0 open core + PolyForm Noncommercial for product code).

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.

This repository implements an AI-agent skill that converts images, PDFs, and image-based PPT/PPTX files into fully editable PowerPoint (.pptx) documents. The system uses a deterministic CLI state machine (`editppt`) with a four-phase workflow: normalize input into per-page jobs, dispatch to subagents for page reconstruction, record page manifests, and finalize by rebuilding the PPTX from manifests. It integrates OCR (PaddleOCR or offline detector) for text bounding boxes, image generation/editing backends (built-in, OpenAI-compatible, or Codex OAuth), and LaTeX rendering for formulas. The output preserves native text boxes, shapes (including custom paths and polygons), tables, images, and speaker notes, with strict provenance tracking for all assets. The codebase is well-structured with clear separation of concerns, a manifest-driven assembly pattern, and comprehensive tests. However, the last commit date is anomalously in the future (2026-09-16), suggesting the repository may be abandoned or the timestamp is erroneous; combined with only 1 open issue and no recent activity, it is classified as stale. The technical drift is low as the code uses modern Python and standard libraries, but external dependencies (OCR, image APIs, LaTeX) may become incompatible over time.

Claude Delegator is a Claude Code plugin that provides GPT (via Codex CLI) and Gemini (via Gemini CLI) as specialized expert subagents. It offers five domain experts (Architect, Plan Reviewer, Scope Analyst, Code Reviewer, Security Analyst) that can advise or implement based on task requirements. The plugin uses MCP (Model Context Protocol) to connect to external CLIs, featuring sophisticated delegation logic with proactive triggers, session management (single-shot and multi-turn), and response synthesis. A key innovation is the zero-dependency Gemini MCP bridge that wraps the Gemini CLI in a standard MCP interface.

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 repository is a collection of configuration templates for Codex CLI, an AI coding assistant. It provides a structured set of configuration files (config.toml, AGENTS.md, sub-agent definitions, and skill files) to enable multi-agent parallel workflows, system-level contracts via developer_instructions, and a well-crafted terminal dialog style. The templates are designed to be copied into the user's ~/.codex directory to configure Codex CLI for efficient, visually structured AI interactions. The repository is actively maintained with frequent updates aligning with Codex CLI releases, and demonstrates high craftsmanship in prompt engineering and configuration design. It is not a traditional software application but a configuration-as-code repository that optimizes an existing tool.

This repository is a motion-graphics video production toolkit designed for AI coding assistants (Codex and Claude Code). It provides a structured pipeline for turning talking-head footage into edited videos with animated overlays, using HTML/CSS/JS compositions driven by GSAP timelines. The system includes reusable style libraries (Vox Explainer and Kallaway) with hundreds of parameterized card templates, transcription and cut-detection scripts, beat-planning heuristics, and project scaffolding. The architecture is a collection of standalone Node.js scripts, agent skill definitions, and HTML templates rather than a monolithic application. It relies on external services (ElevenLabs, Kie.ai, HyperFrames CLI) and CDN-hosted libraries (GSAP, Google Fonts). The repo is actively maintained with recent commits and a CI pipeline, though it exhibits some duplication between mirrored skill directories and draft-status cards that have not all been individually rendered.

Spawn is a multi-cloud VM provisioning CLI that deploys AI coding agents (Claude Code, Codex, OpenClaw, etc.) onto cloud VMs (Hetzner, DigitalOcean, AWS Lightsail, GCP, Daytona, Sprite) with a single command. It handles the full lifecycle: interactive agent/cloud selection via fuzzy matching, VM provisioning, SSH key injection, agent installation (npm binaries, pre-built tarballs, or Docker sidecars), environment configuration (OpenRouter API key, MCP servers via skill files), and interactive session handoff. The system supports headless mode, recursive spawning (agent launching sub-agents), auto-update systemd services, and a security scan cron. It is distributed as per-cloud JS bundles (Bun.build, minified) with curl|bash wrappers that download from GitHub Releases, and a Windows PowerShell installer. Architecturally, Spawn is a modular monolith: a shared orchestration pipeline (`shared/orchestrate.ts`) drives cloud-specific providers implementing a common `CloudRunner`/`CloudOrchestrator` interface (authenticate → promptSize → createServer → waitForReady → install → configure → launch → interactiveSession). Agents and clouds are defined in a `manifest.json` that validates compatibility via runtime type contracts. A shared `Result` monad (`@openrouter/spawn-shared`) enables functional error handling with retry semantics across SSH calls and cloud API interactions. The codebase is well-tested (~100+ test files) with fs-isolated sandboxes, mocked cloud prompts, and integration tests against real cloud providers via an E2E harness. The codebase demonstrates strong engineering practices: schema-validated input parsing (valibot), security-first validation (allowlists for every user-controlled input: instance names, IPs, SSH usernames, shell commands, remote paths), and atomic file operations with corruption recovery for history persistence. However, there is significant duplication across cloud providers (near-identical SSH execution patterns in each cloud module), large monolithic files (orchestrate.ts ~1000+ lines, agent-setup.ts ~2000 lines), and manual drift risk from hardcoded API slugs (DigitalOcean marketplace images, Hetzner server types, GCP machine types) and embedded OAuth client secrets (documented as acceptable public-client pattern).

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 is a monorepo for an AI coding assistant called T1 Code (a fork of T3 Code). It provides a unified chat-based interface that integrates multiple AI providers (Codex, Claude, Cursor, OpenCode) via a server-side orchestration engine. The server uses Effect-TS for functional effects, SQLite for persistence, and WebSocket for real-time communication. Clients include a web app (React with TanStack Router), a terminal UI (Ink/React), and a desktop app (Electron). The system supports git operations, terminal integration, source control providers (GitHub, GitLab, Bitbucket, Azure DevOps), checkpointing/diff tracking, and text generation for commit messages, PRs, and branch names. The architecture follows an event-sourcing/CQRS pattern with a projection pipeline for read models. The codebase is well-structured with extensive test coverage and reusable Effect-based service layers, but relies on beta versions of Effect-TS and other libraries, which introduces some technical drift risk.

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.

Caclawphony is an Elixir/OTP application that automates pull request triage, review, and merging by orchestrating AI agents (Codex). It polls a Linear project board for issues in configured states, creates isolated workspaces per issue, and runs phase-specific agent prompts (triage, review, prepare, merge, closure). A real-time terminal dashboard and optional Phoenix LiveView web UI provide observability into running agents, token usage, and rate limits. The system is driven by a single WORKFLOW.md file that serves as both configuration (YAML front matter) and per-issue prompt template. The architecture follows OTP GenServer patterns: an orchestration loop polls the tracker, dispatches to AgentRunner instances that manage Codex subprocesses via stdin/stdout JSON-RPC, and reconciles state transitions. External integrations include the Linear GraphQL API, GitHub CLI for PR operations, and Telegram for notifications. The repository includes extensive test coverage, CI/CD workflows, and reusable patterns such as a tracker behaviour, snapshot testing support, and a spec coverage linting mix task. While designed for the openclaw/openclaw repository with hardcoded Linear identifiers and defaults, the plugin architecture (tracker adapters, workspace hooks, prompt templates) supports customization. The codebase is actively maintained with modern Elixir tooling (Phoenix, Bandit, NimbleOptions) and demonstrates solid software engineering practices, though its tight coupling to specific Linear states and the prototype nature noted in README limit immediate reuse outside the originating project.

This repository is an open-source terminal-based AI coding assistant built in Rust, clean-room reimplementing Anthropic's Claude Code. It provides an interactive TUI (ratatui) for multi-provider LLM conversation (Anthropic, OpenAI, Google, and 30+ OpenAI-compatible) with a rich tool ecosystem for file editing, bash execution, MCP integration, code analysis, web fetching, and agent orchestration. The architecture is modular: a provider abstraction layer (`LlmProvider` trait), plugin system, slash commands, session management, and an Agent Client Protocol (ACP) for external IDE integration. The codebase is organized into 12+ workspace crates with clear separation of concerns, but many modules are still in active refactoring (issue #232) with stubs, TODOs, and incomplete feature gates (36 experimental features). The permission system, tool budget enforcement, and streaming decoders demonstrate strong engineering, while dual feature flag systems and global mutable state introduce maintenance risk. Overall, it is a feature-rich, well-architected application with some rough edges from ongoing migration.

The Kickbacks extension is a VS Code extension that monetizes IDE 'thinking' spinners in Claude Code and Codex by rendering clickable sponsored ads and sharing up to 50% of ad revenue with developers. It operates as a modular TypeScript application with a complex activation orchestrator managing subsystems for authentication, ad rotation, metrics, earnings, kill switch, consent, self-update, and UI components. The extension uses a local loopback server for secure communication between the extension and injected code in webviews/CLI, implements feature flags via sentinel files, and employs OS-native credential storage. Its architecture centers around adapters for Claude Code (webview and CLI) and Codex (webview), with centralized state management and serving gates controlling ad display based on kill switches, user preferences, and canary states. The code demonstrates strong engineering practices in areas like secure token handling, deduplication, and resilience patterns, though it remains tightly coupled to its specific ad-serving backend and third-party tool integrations.

This repository is a comprehensive, structured library of over 50 marketing 'skills' designed for AI coding agents (Claude Code, Codex, Cursor, etc.). Each skill is a self-contained directory with a SKILL.md (frontmatter + workflow), a references/ folder with deep-dive documentation and benchmarks, and evals/evals.json for behavioral testing. The skills cover the full marketing lifecycle: copywriting, SEO, paid ads (Meta, Google, LinkedIn), email, social, CRO, pricing, offers, attribution, market research, and more. Additionally, the repository includes 40+ zero-dependency Node.js CLI scripts that act as API clients for marketing tools like Ahrefs, Semrush, GA4, Meta Ads, Mailchimp, etc., each following a consistent pattern with dry-run support and environment variable authentication. Several integration guides and a central tool registry (REGISTRY.md) complete the ecosystem. While primarily a content library, the CLI scripts and validation/sync tooling demonstrate strong engineering patterns. The skills are designed to be consumed by AI agents to execute complex, multi-step marketing tasks, with cross-skill references and a shared context document (product-marketing.md) enabling orchestration.