AI-Native Workshop

Rival Workshop is a downloadable kit of 150 editable instruction files, or skills, for AI coding tools including Codex, Claude Code, and Cursor. The skills cover work across 22 disciplines, including product strategy, accessibility, design, frontend and backend engineering, data, AI engineering, security, testing, delivery, marketing, and operations, and are supplied with working examples and an installer for adding them to a project. Its companion app lists the skills, supports search and filtering, shows which skills an agent loads, and lets users enable, disable, rewrite, or add skills from the web. The included Brief tool turns a code change into a visual report with source references for review before merging. The download also includes an optional image helper; the app requires Node 20 or newer, while the image helper requires Python 3.10 or newer and a compatible image-provider key. The product is sold as a one-time purchase with future Workshop editions included, and the skills can be used and edited in personal or client projects but not redistributed as a competing collection.

TeamAI is an open-source CLI for managing a team's skills, rules, documentation, agents, hooks, MCP configuration, environment settings, and knowledge across AI coding tools such as Claude Code, Codex, Cursor, CodeBuddy, WorkBuddy, and OpenCode. It is installed with npm as `teamai-cli` and uses a shared Git repository as the team's source of truth. The CLI distributes resources through an administrative `push` → review and merge → `pull` workflow. Session-start hooks can run `teamai pull` to synchronize the shared harness into project- or user-scoped local AI-tool directories; roles, tags, and subscribed source repositories control which resources members receive. TeamAI also provides a knowledge layer: `teamai import` and `teamai codebase` build a searchable team knowledge base and codebase graph, while `teamai recall` uses BM25 search with graph-boosted reranking and can deploy a recall subagent into supported AI tools. Code relationships are extracted with a WebAssembly tree-sitter parser for TypeScript/JavaScript, Python, and Go, with regex-based heuristic extraction as a fallback and for other languages. Additional commands support friction-based learning suggestions, privacy-scrubbed session summaries, maintenance of stale or low-confidence knowledge, usage digests, a web dashboard, member and role management, package and plugin installation, diagnostics, and uninstallation. The repository is maintained by Tencent and contributors and is licensed under MIT.

AI Engineering from Scratch is a free, open-source curriculum developed by rohitg00 for learning to build and ship AI systems end to end. Its 20-phase sequence covers development tooling, mathematical and machine-learning foundations, deep learning, NLP, computer vision, generative AI, LLM applications, agent engineering, Model Context Protocol (MCP), agent skills, reinforcement learning, and related topics, with implementations in Python, TypeScript, Rust, and Julia. The repository describes 511 lessons and approximately 329 hours of material; each lesson produces a reusable artifact such as a prompt, skill, agent, or MCP server. Learners are instructed to read the lesson documentation, type and run the code from the repository root, record command evidence and outputs, and make a small change before continuing. It provides routes for complete foundations, mathematics and machine learning, production LLM applications, agent engineering, MCP, agent skills, and Claude certification preparation. The repository is distributed under the MIT license and is accompanied by a website containing the same lesson content.

AI Manager is a local-first desktop application for installing, updating, configuring, and repairing AI coding command-line tools on a user's computer. It provides a single interface for checking installed tools, managing versions, connecting tools to reviewed or custom HTTPS AI-service endpoints, and viewing the effective endpoint and environment-variable overrides without editing shell profiles or PATH files. Its sidebar includes software management, API endpoints, skills, MCP connections, global prompts, sessions, and settings. It installs skills from trusted GitHub catalogues or local ZIP files, supports stdio, HTTP, and SSE MCP servers, and searches local coding-tool sessions that can be resumed in a terminal. Tool behavior is controlled by a capability registry, and the application uses local signals for checks rather than claiming a process is healthy without evidence. The application is built with Tauri 2 and React 18 and is released under the GNU AGPL-3.0-or-later. It has no analytics or telemetry SDK, stores product data locally, and offers signed update verification and platform-specific installers. Provider credentials are currently stored in plaintext in the local database, backups, and SQL configuration exports. AI Manager began as a fork of CC Switch, with inherited portions retaining their original MIT licensing.

The Mastering AI Observability Workshop repository is a hands-on workshop resource for building a trace-backed support-agent improvement workflow with Braintrust. It provides a local Python workshop app, setup instructions, a support agent using tool calls, seeded evaluation cases, production-like trace generation, Braintrust datasets and evaluations, hosted scorers, Topics facets, sample-trace imports, and optional draft evaluation cases derived from traces. The workflow connects tracing and online analysis to offline evaluations, custom scoring, failure investigation, regression cases, and reviewable code changes. The repository includes a public WORKSHOP.md guide, environment setup through uv and Make commands, local database seeding, a web interface, and configuration for Braintrust Gateway model calls.