Assess candidate Codex CLI skills in real codebases
Run Codex CLI take-homes in CodeSubmit IDE or over local Git, or pair live in CodePair, to see how engineers turn failing tests into clean, verified patches.
Fix the rounding bug in totals.py using the pytest traceback
Proposed a six-line patch in billing/totals.py
Reviewed patch and ran pytest
14 unit tests passed
What to evaluate
How to evaluate engineers using OpenAI Codex CLI
Codex CLI rewards engineers who feed the terminal agent failing test output and strict approval boundaries rather than pasting vague feature wishes.
Setting command approval boundaries
Strong candidates choose appropriate approval levels before allowing Codex CLI to modify source files or execute shell commands without asking.
Constraining prompts with test logs
Instead of open-ended requests, experienced engineers pipe failing test logs and compiler traces into the prompt to keep the patch narrowly scoped.
Inspecting unified diffs before committing
Candidates read each generated diff line by line in the terminal to catch unintended edits or regressions before committing changes.
Writing missing edge-case tests
When a generated patch only satisfies the happy path, deliberate developers write edge-case assertions by hand and commit in small, reversible steps.
How it runs
Codex CLI in CodeSubmit IDE, Git, or CodePair
Assign a take-home in CodeSubmit IDE or over local Git, or assess Codex CLI skills live in a CodePair browser workspace.
Assign a repository task
Pick an open-ended library task or upload your own repo, then choose CodeSubmit IDE with budgeted AI credits, local Git, or live CodePair.
Prompt, patch, and run tests
In CodeSubmit IDE, Codex CLI is ready in a recorded, budget-capped workspace, or candidates run it locally with Git or live in CodePair.
Inspect diffs and test results
Your team reviews the working code, commits, test output, notes, recorded session, usage summary, and AI review, or follows up in CodePair.
Codex CLI tasks
Repository tasks built for terminal patch workflows
Give candidates a realistic codebase with failing tests or missing behavior so you can observe how they direct Codex CLI under real constraints.
Fix invoice rounding in LedgerPulse
Line-item tax totals drift by one cent on tiered European invoices when discounts are applied before currency conversion in the billing service.
Preserve backward compatibility with existing stored invoice snapshots and webhook payloads.
Harden retry logic in DockRoute
Duplicate carrier shipment labels are generated when warehouse barcode scanners retry timed-out dispatch requests during peak afternoon fulfillment windows.
Keep dispatch endpoint latency flat and avoid adding external queue dependencies.
Add timezone conflict checks in CareSlot
Telehealth patients booking across daylight saving boundaries can reserve overlapping fifteen-minute intake appointments with the same on-call clinician.
Do not alter the database migration history or public booking schema.
What you review
Evidence from every candidate submission
Instead of grading a single final answer, your hiring team inspects the working software, commit progression, and verification choices behind the code.
Diffs and commits
Inspect the full diff, commit history, and passing test results to see how the candidate iterated on patches.
AI repo review
Read an automated review that summarises repository structure, highlights testing gaps, and suggests follow-up prompts for your debrief.
Candidate notes
Review the candidate's written notes on tradeoffs, then open any submitted repository in CodePair for a live follow-up.
Why real tasks beat algorithm brainteasers
Software development has changed, and hiring tests built around timed algorithm puzzles measure the wrong instincts. Any terminal agent can generate a binary tree traversal in seconds. When your team hires developers skilled with Codex CLI, you need to know whether they can diagnose a failing service, constrain a patch, and protect existing behavior.
Practical repository assessments show how an engineer works when the agent's first diff is incomplete. In CodeSubmit, candidates read existing modules, feed real compiler and test output into Codex CLI, and write missing edge-case checks by hand. Reviewers evaluate the shipped code, commit history, and engineering judgment that show up on the job.
Frequently asked questions
Can candidates use Codex CLI in a coding test on CodeSubmit?
Yes. For take-homes, teams choose CodeSubmit IDE, where Codex CLI runs in a recorded workspace with a per-candidate AI budget, or local Git. Codex CLI also runs live inside CodePair for collaborative interviews.
How do we run live pair programming with Codex CLI during interviews?
Open a CodePair session from a library challenge, a custom repository, or a candidate's submitted take-home project. Both the interviewer and candidate share the browser workspace where Codex CLI is available in the terminal to inspect code, propose patches, and run tests.
What does the hiring team see after a Codex CLI coding interview?
Reviewers see the working software, diffs, commits, test results, candidate notes, and an AI review. When candidates work in CodeSubmit IDE, your team also gets the recorded session and a usage summary showing how they steered Codex CLI.
Can we combine an OpenAI Codex CLI assessment with earlier screening steps?
Yes. Teams can start with AI phone screens or short coding screens called Bytes, then invite shortlisted candidates to a repository take-home project or a live CodePair session with Codex CLI to evaluate how they ship changes in a realistic codebase.
Assess Codex CLI skills on real repositories
Start a free trial to run CodeSubmit IDE, Git, or live CodePair assessments where candidates use Codex CLI to ship tested code.