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.
Design and packet work comes before an Intent, so oah call cannot judge it. In v0.3.0, the lead session runs oah recommend --kind design with the user's request file, then sends the work to the oah-<tier> subagent it prints. Design never runs below high, and with no usable judgement it runs at max: unknown difficulty is not treated as easy.
When you need it
A wrong design costs every step after it, yet design happens before there is an accepted Intent to score.
How it works
The lead session runs oah recommend --kind design --task <task> --request-file <file>, adding --scope <path> for paths it already knows.
With Jev on, the request text goes through the privacy check first; this is the only request oah recommend sends, and only with --request-file.
The work goes to the oah-<tier> subagent of the printed tier. Design never runs below high; with no key, Jev off, a secret, a failure or low confidence, it runs at max.
--kind packet keeps its rule tier when unjudged.
The outcome, Jev off included, is recorded under the task in the state folder without the request text, the Scope paths or the key; oah packet new warns OAH_DESIGN_UNJUDGED when no design judgement is recorded.
Example
The command the lead session runs before writing a design or a packet.