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.
This repository is the personal blog and knowledge base of digoal, a prominent PostgreSQL DBA and database expert. It contains over a decade of blog posts (2010–2026) covering PostgreSQL internals, performance tuning, database architecture, and related topics, along with a large collection of images, PDFs, and other attachments. The blog is organized by year and month, with each post as a markdown file. The repository also includes a 'skills' directory that defines reusable AI agent skill definitions (SKILL.md files) for various tasks such as fact-checking, stock prediction, bug hunting, and course writing. These skills are structured with agent prompts, reference materials, and supporting scripts, making them extractable and reusable in AI-assisted workflows. The repo is actively maintained and serves as both a reference for database professionals and a library of AI agent capabilities.
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 hosts a Jekyll-based GitHub Pages website that curates and distributes AI skills (SKILL.md files) for economists. The site presents a browsable catalog of 17 skills organized by research workflow stage (ideation, literature, theory, data, analysis, writing, communication, engineering). It offers user path personalization (beginner/intermediate/advanced) persisted via localStorage, a download-all-skills feature that bundles skills into a zip using JSZip, individual skill downloads, a submission form that opens a GitHub Issue, and an educational 'Learn' page explaining AI agent concepts. The repository is primarily a content distribution platform; the actual software consists of the Jekyll site structure, CSS design system, and JavaScript interactivity. The skills themselves are high-quality markdown documents with YAML frontmatter, containing detailed instructions for AI coding assistants like Claude Code, Cursor, and Gemini CLI.
This repository serves as a curated directory of GitHub projects focused on 'human distillation skills'—the practice of extracting and encoding human expression styles, decision-making frameworks, and interaction patterns from individuals (such as personal contacts, idols, or public figures) into AI-readable formats. It does not contain any executable code, data processing pipelines, or analytical models itself; instead, it functions as a community-maintained index linking to external repositories that implement specific skill sets. The project is organized into thematic categories (self, interpersonal relationships, idols, and niche communities), each containing a markdown-formatted table of linked repositories with concise descriptions. Contributions are accepted via pull requests following a strict format to ensure consistency and usability. While the repository lacks traditional software engineering artifacts, it provides value as a discovery tool for researchers, developers, and enthusiasts seeking pre-existing work in the niche domain of personality-driven AI skill development. From a data perspective, the repository's sole asset is its README.md file, which acts as a structured dataset of skill project metadata. However, it does not include any data transformation, validation, or analytical components, limiting its reusability as a data engineering asset. The project exhibits minimal technical drift due to its simplicity and absence of external dependencies, and its maintenance status appears maintained based on recent commit activity (last commit 2026-04-08) and zero open issues, indicating ongoing community stewardship without significant backlog.