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Explore a repository without getting lost

Use Codex to build a verifiable map of the code before requesting changes.

Ask before changing

In an unfamiliar repository, the first task should reduce uncertainty. Ask for concrete paths, symbols, and commands so every claim can be checked.

Terminal
Analyze this repository without editing files. Identify:
1. entry point
2. main modules
3. one request flow
4. tests and validation commands
5. three technical risks
Cite files and lines.

Trace real behavior

A folder list does not explain a system. Pick visible behavior and ask Codex to trace it from the UI or endpoint through data, side effects, and tests.

  • Where is input validated?
  • Which contract connects the modules?
  • Where is data transformed or persisted?
  • Which test would fail if this behavior changed?

Large repositories, monorepos, and multi-repo work

In a monorepo, do not ask “understand everything.” First request the map of workspaces, packages, commands, and internal dependencies. Then narrow to one path and require Codex to say what it did not inspect.

Terminal
Analyze this monorepo without editing. Return:
- detected workspaces or packages
- install and test command for each relevant package
- internal dependencies of the package I will touch
- boundaries of what you did not read
Then focus only on packages/web.

If work crosses several repositories, work through contracts: API, events, schema, published package, or versioned documentation. Do not let Codex patch two repos at once without independent verification in each.

Require evidence

Ask Codex to separate observed facts from inferences and to state when evidence is missing. This reduces plausible but incorrect answers.

Exercise

Choose one small feature, obtain its map, and manually verify two references. Keep the summary in your notes unless the team explicitly wants new repository documentation.

Lesson deliverable

What you will build

Practical evidence for "Explore a repository without getting lost" applied to a real or training repository.

Why it matters

The goal is to turn the lesson into a verifiable action, not just reading.

Starter repository or files

A small Git repository with Git, README, and one known validation command.

Steps

  1. 1. Inspect Git status before starting.
  2. 2. Ask Codex to work with limited scope.
  3. 3. Run the verification command.
  4. 4. Review the diff or produced evidence.
  5. 5. Record what is verified and what remains open.

Copy-ready Codex request

Apply the lesson "Explore a repository without getting lost" in this repository. Work in small changes, cite concrete files, run one verification, and finish with evidence and risks.

Expected result

A reviewable output: map, plan, diff, test, or report depending on the lesson.

Verification command

git status --short && git diff --stat

Manual check

Check that the result matches the requested scope and Codex did not touch unrelated files.

Common error. Requesting too much at once. Fix: limit the folder, behavior, and expected verification.

Mini exercise

Repeat the practice in another folder of the same repository, changing only one constraint.

Show solution

Keep the same goal, add one explicit constraint, and compare whether the final evidence improves.

Evidence to save

Save the prompt, commands run, relevant output, and git diff --stat.

Official sources and tested version

OpenAI CodexTested: 2026-07-12
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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