oah helps you build an AI development workflow that fits you and keeps changing as AI improves. It is a working reference and a guide you can adapt.
A useful result needs more than generated code: the scope must be clear, the changes checked, and decisions kept for the next session. oah connects those steps so one request can carry through the work with a record of what happened.
002 / Why it keeps changing
Older rules and skills can hold a newer model back or make its results worse. Defaults should be easy to understand and change; a rule that gets in your way is a design problem to fix.
Keep the parts that help, remove what no longer helps, and revise the workflow as your tools improve. You can start with the full setup or take only the impact map, wiki, leases, evidence gates or review loop that your project needs.
003 / Why Claude builds and Codex reviews
This split comes from the author's own experience, not a measured comparison: Claude fills in details a beginner might miss in a first version; Codex checks and corrects within a fixed scope.
The current commands run Claude Code and Codex CLI. The ideas can also be adapted to another tool or harness, with one AI or several. Give each stage to the AI that suits your work.
004 / From one request to a checked change
Describe the result you want in plain language. The AI records the goal and scope, checks what the change touches, reserves its paths and chooses the tools for the task. Claude builds; Codex reviews, corrects and reviews again.
Gates record the checks and their results. The project wiki keeps the decisions and reasons beside the code. In v0.3.0, optional TypeSafe Jev can judge difficulty for model selection; rules remain the fallback when it is unavailable or uncertain.
005 / The decisions stay with you
You ask, answer when the AI needs a decision, and confirm before the change lands. If the same problem repeats or the work reaches a design choice, the loop stops to ask you.
Ask “Is the review done?” or “Did all the gates pass?” and inspect the evidence. The final confirmation belongs to you; writing code, checking it and approving its landing are separate steps.
006 / Free, open source, yours to adapt
oah is free, open-source software under the MIT license. Read the source, change it and bring the useful pieces into your own workflow. Your AI services and any optional services use their own accounts and billing.
The current v0.3.0 release supports macOS and native Windows, and expects familiarity with git and pull requests. Install it, run oah init in your project, then try the small hello-oah example. The installation and documentation sections explain each step.
For people changing oah itself: run the unit tests with Python 3.11 and again with the newest Python you have, with TMPDIR inside the checkout. CI tests macOS and Windows on every pull request to main, a separate workflow runs gitleaks and the privacy scan, and commits record their author engine with an Agent-Engine trailer.
When you need it
A tool that runs on two operating systems and guards secrets needs its own checks to run the same way for every contributor.
How it works
Create .tmp/test-tmp and point TMPDIR at it so every test write stays inside the checkout.
CI runs the tests on macOS and Windows for every pull request to main; pull requests that change only documentation skip the tests.
A separate workflow runs gitleaks over the full history and the privacy scan on every pull request and push, documentation included.
Before every push, python3 tools/privacy_scan.py --gitleaks require must print PRIVACY_SCAN_CLEAN.
Commits carry an Agent-Engine: claude|codex|human trailer.
Example
Run oah's own test suite (from How it works › Development).