governed-engineering-runtime

# Governed Engineering Runtime

Operate the repository through the `ai` CLI. Treat this skill as a governance workflow, not as extra
programming knowledge.

## Prepare

1. Confirm `codex login status` succeeds.
2. Check whether `ai --help` succeeds.
3. If `ai` is missing, ask before installing executable code. Resolve the current skill directory
   from this loaded `SKILL.md`, inspect the bundled `runtime/` source when required, then run:

   ```bash
   python3 <skill-directory>/scripts/bootstrap_runtime.py
   ```

   The bootstrap installs only the runtime bundled inside this skill. It does not download code from
   a remote package source.

4. Run `ai init .` when `.ai/project.yaml` is absent. Do not replace an existing configuration.
5. Read [references/runtime-checklist.md](references/runtime-checklist.md) before the first run.

## Execute

Run an end-to-end governed change with:

```bash
ai build "<engineering objective>"
```

Use narrower workflows when appropriate:

```bash
ai architect "<objective>"
ai review "Review the current working tree"
ai implement "<approved implementation objective>"
```

Allow the runtime to complete these stages in order:

1. specification;
2. architecture and specialist delegation;
3. required human approval;
4. implementation by a write-authorized agent;
5. independent correctness and security review;
6. testing against acceptance criteria;
7. optional Git commit.

Do not bypass an approval gate, grant write access to the controller, or reinterpret an infrastructure
failure as incomplete engineering work. Preserve existing repository changes.

## Report

At completion, report the run status, branch, commit when created, affected files, validation results,
remaining risks, and the location of persisted `.ai/summaries` and `.ai/tasks` records.