ai-engineering

Turn AI-assisted delivery into a governed local workflow — in any repo, any IDE.

54 skills · 9 agents · 6 surfaces · 1 governed flow

{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:

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

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

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.

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.

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.

The ruleset lives in AGENTS.md. Project identity and hard prohibitions live in CONSTITUTION.md. Release history lives in 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 — the website: what it is, why it's governed, and how to install
• docs/ — architecture, getting started, and the diagram set
• CONSTITUTION.md — mission, stakeholders, prohibitions
• CHANGELOG.md — release history and breakage notes

Standing on the shoulders of

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

Contributing

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

License

MIT. See LICENSE.

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Contributors

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