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Turn any repo into a governed AI workspace. Quality gates, security scanning, and risk management — enforced locally via git hooks. Works with Claude Code, GitHub Copilot, Cursor, Gemini & Codex.

ai-engineering 최신버젼 다운로드

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<p><strong>Turn AI-assisted delivery into a governed local workflow — in any repo, any IDE.</strong></p>

<p><code>54 skills · 9 agents · 6 surfaces · 1 governed flow</code></p>

<br>

`{ai} engineering` installs a deterministic governance layer into any repository — specs, decisions, skills, agents, hooks, and an audit trail, all as versioned local files. No hosted control plane. No provider lock-in. Every IDE follows the same rules.

## Quickstart

Get a governed repo in under a minute — `{ai} engineering` is uv-first:

```bash
uv tool install ai-engineering   # install the CLI
ai-eng install .                 # add governance to your repo
ai-eng doctor                    # [PASS] hooks, mirrors, manifest, required tools
```

<details>
<summary>Prefer <code>pip</code> or <code>pipx</code>?</summary>

```bash
pipx install ai-engineering
# or
python -m pip install --user ai-engineering
```

</details>

Then open your editor and type `/ai-start`. Prefer to ease in? Start in observe mode and enforce only what proves useful.

**What you get:** 54 skills and 9 agents you invoke with `/ai-<name>` · a spec-driven workflow · automatic checks on every change · versioned local files you own. Update any time with `ai-eng update`.

## The governed workflow

You drive the intent and approve each step; the gates catch the rest — no secrets, broken docs, or untested changes reach a merge.

<br>

<br>

The canonical chain is **`/ai-brainstorm → /ai-plan → /ai-build → /ai-pr`**. Use it whenever work changes product behavior, framework behavior, security posture, public docs, or release state. `/ai-commit` stays available for WIP checkpoints; it is not part of the chain.

## Your toolkit

Fifty-four skills and nine agents cover the whole delivery loop — and the same commands work in every supported editor.

<br>

<br>

Need evidence? `/ai-research` returns cited findings from local context, the web, and async deep research. And because plans carry ready-to-apply patches, mechanical work routes to a smaller model — routine edits stay cheap.

## Supported surfaces

One canonical payload is mirrored, byte-for-byte, into every enabled surface. Skills live in two trees: `.claude/skills` for Claude Code, whose search paths are compiled in, and `.agents/skills` for every other surface. Only agents, commands and hooks stay surface-local.

| Surface | Entry point | Skills | Surface-local |
|---------|-------------|--------|---------------|
| Claude Code | [CLAUDE.md](CLAUDE.md) | `.claude/skills/` | `.claude/agents/`, `.claude/commands/` |
| GitHub Copilot | [.github/copilot-instructions.md](.github/copilot-instructions.md) | `.agents/skills/` | `.github/agents/`, `.github/hooks/` |
| OpenAI Codex | [AGENTS.md](AGENTS.md) | `.agents/skills/` | `.codex/config.toml`, `.codex/hooks.json` |
| Antigravity | [AGENTS.md](AGENTS.md) | `.agents/skills/` | `.agents/agents/` |
| OpenCode | [AGENTS.md](AGENTS.md) | `.agents/skills/` | `.opencode/agents/`, `.opencode/commands/` |
| Cursor | `.cursor/rules/` | `.agents/skills/` | `.cursor/agents/` |

The ruleset lives in [AGENTS.md](AGENTS.md). Project identity and hard prohibitions live in [CONSTITUTION.md](CONSTITUTION.md). Release history lives in [CHANGELOG.md](CHANGELOG.md).

## Highlights

- **Ship a whole spec in one run** — `/ai-autopilot` decomposes it, builds a dependency DAG, runs parallel waves, and converges on a reviewed PR.
- **What you approved is what shipped** — a brainstorm hard-gate plus a spec-lifecycle state machine keep every change anchored to the approved spec (Rung 2 SDD — spec and code stay in sync, not just spec-first that drifts).
- **An audit trail you own** — every AI action lands in a hash-chained NDJSON log you can verify offline, with no telemetry by default.
- **Every bypass has an owner and an expiry** — no `# noqa` or `@ts-ignore`; findings are refactored or formally risk-accepted with a severity-based TTL.
- **Every tool call is screened before it runs** — a deterministic guard checks each edit, write, and shell command and stops risky ones.
- **AI quality is a tested property** — skills are measured with pass@k, and a regression beyond five points blocks the pull request.

## Documentation

- [ai-engineering.arcasiles.com](https://ai-engineering.arcasiles.com) — the website: what it is, why it's governed, and how to install
- [docs/](docs/) — architecture, getting started, and the diagram set
- [CONSTITUTION.md](CONSTITUTION.md) — mission, stakeholders, prohibitions
- [CHANGELOG.md](CHANGELOG.md) — release history and breakage notes

## Standing on the shoulders of

`{ai} engineering` builds on ideas and patterns from these projects:

| Project | What we learned |
|---------|----------------|
| [Superpowers](https://github.com/obra/superpowers) | Brainstorm hard-gate, TDD-for-skills patterns |
| review-code | Handler-as-workflow, parallel specialist agents |
| dotfiles/ai | Agent matrix, SDLC coverage |
| autoresearch | Radical simplicity as a design principle |
| [SpecKit](https://github.com/github/spec-kit) | Spec-driven workflow inspiration |
| [Anthropic Skills](https://github.com/anthropics/skills) | Frontend-design, skill-creator — absorbed and extended |

## Contributing

Contributions are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, code style, testing, and the pull request process. This project follows the Contributor Covenant [Code of Conduct](CODE_OF_CONDUCT.md).

## License

MIT. See [LICENSE](LICENSE).

## Star history

## Contributors

Made with [contrib.rocks](https://contrib.rocks).

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