Recommended Free Tools
iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
To make Codex follow the same testing and code-review expectations across tasks, put repository-wide defaults in AGENTS.md and package repeatable task workflows as Skills. Then specify the evidence Codex should collect—such as named tests, policy checks, or human approval—and evaluate the instructions against representative changes. The right setup depends on whether a rule should apply across repository work or only when a particular workflow is used.
How do I make Codex follow the same testing and code-review instructions every time?
Start by separating standing project conventions from specialized workflows. AGENTS.md is for instructions that should guide work in a repository or directory. A Skill is a packaged workflow for a task that recurs, potentially with supporting templates or resources. They can work together: repository guidance can set local defaults while a Skill supplies a focused review-and-validation process.
The Codex CLI guide describes instruction files being collected from the user’s Codex configuration and from directories between the repository root and the current working directory, with more specific directory guidance taking precedence. Keep each instruction scoped to the work it governs, and avoid duplicated rules that conflict. See the Codex Prompting Guide for the CLI guidance.
Should testing and review rules go in AGENTS.md or a Skill?
| Decision | AGENTS.md | Skill |
|---|---|---|
| Best fit | Repository or directory defaults useful across relevant work | A reusable task workflow, especially one that benefits from examples or helper resources |
| Packaging | Project instruction file | A directory containing SKILL.md and optionally supporting files |
| How it is discovered or used | Codex CLI guidance describes discovery through configuration and the directory tree | Loading depends on the host and API; the official documentation distinguishes supported runtime contexts |
| Maintenance focus | Revisit rules for relevance and scope | Maintain the workflow and its supporting resources |
OpenAI’s Skills documentation describes the Skill structure and explains that loading behavior depends on the environment. Do not assume every Codex host loads Skills in exactly the same way.
Repository rules should stay lean. OpenAI’s September 11, 2026 guidance says: “Because AGENTS.md applies whenever the model works in your repository, you should frequently revisit each instruction and ask yourself whether it’s still needed.” See Rethinking skills and prompts for GPT-6 Astra. Avoid blanket requirements such as reading unrelated documentation before every edit; keep instructions tied to likely tasks and authorize only specific safe local workflows where appropriate.
What should a reusable code-review instruction say?
Define the scope and the expected report, not just “review this.” OpenAI’s Codex Prompting Guide recommends prioritizing bugs, risks, behavioral regressions, and missing tests. Ask for findings tied to concrete evidence in the diff or affected behavior. If no issue is identified, require Codex to say so plainly and identify any residual risks or test gaps.
- State which changes or components are in scope.
- Ask for bugs, relevant security or operational risks, behavioral regressions, and missing tests.
- Require findings to include evidence and severity.
- Require a no-findings response to distinguish “no issue found” from “no risk remains.”
- Ask for unresolved risks and checks that were unavailable or inconclusive.
What should a testing instruction require?
Give Codex a verification surface it can act on: the appropriate command or test class, important scenarios, expected behavior, and what to report if a check cannot run. Asking for tests is not itself proof of correctness. Report which checks actually ran and their outcomes, and separate confirmed results from unavailable or inconclusive checks.
For broader work, use a loop rather than treating one pass as final. OpenAI’s iterative repair-loop example separates review, repair, and validation, then repeats based on results. Depending on the task, validation can include tests, policy checks, simulations, or human approval; the source does not rank these methods universally.
- Review: inspect the current change against its scope and acceptance criteria.
- Repair: make focused corrections for identified issues.
- Validate: run the agreed checks and record results, including any blocker.
- Iterate: review again and repeat until the agreed evidence is met or a concrete blocker remains.
For safety-sensitive work, specify where human approval is required. A passing automated check is not a substitute for approval when the task’s validation boundary calls for a person.
A practical instruction template
This is an adaptable starting point, not an official OpenAI template. Replace the scope and checks with ones that are meaningful for your project:
Rank #4
For changes in
[scope], review for bugs, relevant risks, behavioral regressions, and missing tests. Run[specific validation commands]for[key scenarios]. Report findings with evidence and severity. If no findings are identified, state that and list residual risks or testing gaps. If a check cannot run, say why and what evidence is still needed.Recommended: Crashes or Glitches? A Free Driver Scan Usually Finds the Culprit →Recommended: PC Feels Slow? A Free Scan Shows What's Dragging Windows Down →Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
Put the parts that should govern all relevant repository work in AGENTS.md. Put the reusable, task-specific sequence in a Skill’s SKILL.md, adding support files only when they make the workflow easier to apply consistently.
Best Value
How can a team tell whether its instructions work?
Try them on a small set of representative real or safely constructed tasks instead of assuming a well-written template guarantees better results. Include a straightforward change, a behavioral edge case, and a case with a known test gap. Check whether Codex stays in scope, runs the named validation, identifies known or deliberately seeded issues, supports findings with evidence, and reports limitations.
If a requirement is missed or misunderstood, revise the instruction and repeat the exercise. This adapts the review-repair-validate-iterate pattern; it is a practical evaluation approach, not a promised improvement in review quality, coverage, or time saved.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
