ECC

ai-engineering-from-scratch

rohitg00/ai-engineering-from-scratch

Huia Project Score 79

AI Engineering from Scratch is a free, MIT-licensed course on GitHub that teaches how AI systems work by having you write each piece yourself, starting with linear algebra and ending with multi-agent systems and production infrastructure. The material is split into 20 phases and 523 lessons, with code in Python, TypeScript, Rust and Julia. Each lesson folder holds a written explanation, runnable code and an outputs directory. The pattern is to build an algorithm such as backpropagation, a tokenizer, attention or an agent loop from plain math first, and only then run the same idea through a library like PyTorch or scikit-learn. Lessons end with something you keep: a prompt, a SKILL.md file, an agent or an MCP server. The repository holds 396 skills and 99 prompts produced this way, plus a script that copies them into an agent's skills folder. There are three ways to follow it. You can read lessons on the website with no setup, clone the repository and run the code locally, or install the course's tutor skills into a coding agent such as Claude Code or Codex. Those skills run a placement quiz, write a study plan to LEARNING.md and teach one lesson per session. Separate tracks prepare for the four Claude certification exams and the MCP Associate exam; the README says these are independent and not affiliated with the exam bodies.

Stars

68K

Language

Python

License

MIT

Momentum

+8.2% 7dEstablished

ui-ux-pro-max-skill

nextlevelbuilder/ui-ux-pro-max-skill

Huia Project Score 71

UI UX Pro Max is an agent skill that hands coding assistants a searchable library of design guidance, so a request such as a landing page for a spa comes back with a matching style, color palette, font pairing and page structure. It installs into Claude Code, Cursor, Windsurf, GitHub Copilot, Codex, Gemini CLI, Kiro and about a dozen more assistants, through the Claude Code plugin marketplace or the uipro command from the ui-ux-pro-max-cli npm package. Under the hood it is a set of CSV datasets and a Python search script that ranks entries with BM25. The README counts 79 searchable UI styles (50 active), 192 palettes tied to 192 product types, 74 font pairings, 25 chart types, 119 UX guidelines and advice for 22 stacks, from React and Next.js to SwiftUI, Flutter and WPF. Version 2.0 added a design system generator that merges those searches into one recommendation, with anti-patterns to avoid and a pre-delivery checklist covering contrast, focus states and reduced motion. You can save the result as a master file with per-page overrides that the agent reads in later sessions. In most supported assistants the skill switches on by itself for UI requests. Kiro, Copilot, Roo Code and KiloCode use the /ui-ux-pro-max slash command instead. A paid premium edition with brand and asset features is sold separately on uupm.cc.

Stars

135K

Language

Python

License

MIT

Momentum

+1.9% 7dEstablished

ponytail

DietrichGebert/ponytail

Huia Project Score 76

Ponytail is a rule set and skill collection that pushes an AI coding agent to write as little code as a task needs. It ships as a plugin for Claude Code, Codex, GitHub Copilot CLI, Gemini CLI and several other agent hosts, and as plain rules files for editors such as Cursor, Windsurf and Cline. Before writing anything, the agent works down a fixed ladder: drop the feature if it does not need to exist, reuse what the codebase already has, then try the standard library, a native platform feature, an installed dependency and a one-line answer. New code comes only after all of those fail. The README is firm that validation, error handling, security and accessibility are off limits for cutting. Its example: asked for a date picker, the agent reaches for a native <input type="date"> instead of adding a library and a wrapper component. The plugin adds commands to set the intensity (lite, full, ultra or off), to review a diff or a whole repository for over-engineering, and to collect deferred shortcuts into a ledger. The maintainer publishes a benchmark: across twelve feature tasks on a FastAPI and React template with Haiku 4.5, the README reports about 54% fewer lines of code than the same agent without the skill, plus smaller drops in tokens, cost and time.

Stars

162K

Language

JavaScript

License

MIT

Momentum

+5.7% 7dEstablished

agent-skills

addyosmani/agent-skills

Huia Project Score 75

Agent Skills is a repository of 25 Markdown workflows for AI coding agents, covering software work from specification and planning through testing, review, performance, security, deployment and documentation. Each skill is a SKILL.md process with triggers, steps, red flags, verification criteria and common rationalizations to counter. The pack can be installed with the skills CLI or integrated with tools including Claude Code, Cursor, Gemini CLI, OpenCode, GitHub Copilot and Codex. Nine slash commands map development tasks to skills. /build auto can generate a plan and implement its tasks after one approval, while stopping on failures or risky steps. This suits teams that want an agent to follow a defined engineering process instead of relying on isolated prompts. The repository also contains four review personas, shared checklists, evaluation cases and host-specific adapters. One installation limitation matters: installing a single skill through npx does not copy the repository-level references/ directory, so links to shared checklists are unavailable unless those files are supplied another way.

Stars

106K

Language

JavaScript

License

MIT

Momentum

+4.9% 7dEstablished

A test runbook for web and mobile apps: an AI assistant drafts the plan, and testers or agents record results over MCP.

Video reference library that turns launch films and explainers into blueprints and prompt recipes Claude and other agents can build from.

AI video generator that turns a prompt or screen recording into onboarding, training, demo and explainer videos for teams.

MCP server that grounds AI agents in full-text computer science papers from top-tier conferences, for students and researchers.

Watches production logs, metrics and traces, investigates issues and opens pull requests with fixes for your team to review.