itdar

AI Agency — AI skill for Claude Code

AI community

🌐 English 한국어 日本語 中文 Español.

How to install AI Agency

This entry records only its repository, not the path inside it, so there is no exact command to give. Open itdar/ai-agency and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What AI Agency does

🌐 English 한국어 日本語 中文 Español.

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README

🌐 [English](README.md) | [한국어](docs/README_ko.md) | [日本語](docs/README_ja.md) | [中文](docs/README_zh.md) | [Español](docs/README_es.md)

ai-agency

**Stop re-explaining your project to AI.**

A vendor-neutral CLI that builds a persistent context layer for your project — so every AI session (Claude Code, Codex, Cursor, Gemini, Copilot, Windsurf, Aider) starts already knowing your codebase, conventions, and business rules.

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux-lightgrey.svg)](#requirements) [![Made with Bash](https://img.shields.io/badge/made%20with-Bash-1f425f.svg)](src/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](#contributing) [![Sponsor](https://img.shields.io/badge/Sponsor-%E2%9D%A4-red?logo=github)](https://github.com/sponsors/itdar)

ai-agency init — scan, classify, scaffold, validate



Pick the PM, pick sub-agents — panes spawn and coordinate via task-board.md
Pick PM from the menu, pick which specialists to bring — panes spawn and coordinate through a shared task-board.md.

Try it in 60 seconds

brew install itdar/tap/ai-agency

cd ~/your-project
ai-agency init      # scans, generates AGENTS.md + .ai-agents/ context
ai-agency           # pick an agent — it's already briefed

No Homebrew? `curl -fsSL https://raw.githubusercontent.com/itdar/ai-agency/main/src/install.sh | bash -s -- --global`

Works on macOS and Linux. Don't like it? `ai-agency uninstall` removes the tool, and `ai-agency clear` removes everything it generated — [no residue](#updating--uninstalling).


Why

Every new AI session starts from zero. The model burns tokens figuring out:

  • "What framework is this?" — reads 20 files
  • "What are the team conventions?" — guesses w