How to Quickly Get Started in Any Field with Codex

Many people only treat Codex as a tool for writing code, but the more efficient use is to treat it as a domain learning engineer that can help you build a learning system, maintain a knowledge base, generate exercises, check results, and advance projects.

Codex’s advantage goes beyond answering questions. It can read and write files in the project directory, run commands, modify code, and simultaneously execute automated checks like tests, linters, and type checkers. The OpenAI official documentation also clearly states that Codex can answer questions in a codebase, edit files, and run related commands. More importantly, Codex supports writing project‑level long‑term instructions via AGENTS.md—it reads this file before starting work, and supports both global and project‑level instructions. Additionally, Codex Skills allow you to package a certain type of task into a reusable capability; a Skill is usually a directory containing a SKILL.md, and can also include scripts and reference materials.

Once you understand these capabilities, the right way to use Codex to learn a field is no longer:

Please tell me about AI Agent.

Instead, let it create for you:

A runnable, iterable, testable, and reviewable learning repository.

The One‑Sentence Method

Use Codex to create a learning project repository for a certain field, containing a knowledge map, core concepts, case studies, exercises, quizzes, projects, review records, and personal progress. Then let Codex dynamically adjust the learning path each day based on your mastery.

This is the dividing line between Codex and ordinary chat‑based learning:

ChatGPT is better at explaining knowledge clearly; Codex is better at engineering, file‑based, project‑based, and automating the learning process.

Step 1: Create a Domain Learning Repository

Take learning “AI Agent” as an example. In the terminal, go to the working directory and start Codex:

mkdir learn-ai-agent && cd learn-ai-agent && codex

Then enter:

I want to quickly master a new domain with Codex: AI Agent.

Please create a domain learning repository in the current directory with the following structure:

learn-ai-agent/
├─ AGENTS.md
├─ README.md
├─ 00_domain_map.md
├─ 01_core_concepts/
├─ 02_case_studies/
├─ 03_exercises/
├─ 04_projects/
├─ 05_flashcards/
├─ 06_quizzes/
├─ 07_daily_review/
├─ 08_glossary.md
└─ progress.md

Please complete the following tasks:

1. Create the above directories and files.
2. In README.md, explain how to use this learning repository.
3. In 00_domain_map.md, generate a knowledge map of AI Agent.
4. Break AI Agent into learning modules from beginner to advanced.
5. Each module must include: concept explanation, real‑life analogy, real‑world case, practice exercises, output task, acceptance criteria.
6. Each day’s learning must have a deliverable outcome.
7. Don’t just generate articles—generate exercises, quizzes, projects, and review tasks.
8. Create AGENTS.md, stipulating that from now on the teaching process must follow: "Explanation → Example → Practice → Check → Review".
9. Create progress.md to record learning progress, mistakes, weak points, and next steps.
10. Finally, generate a 30‑day learning roadmap.

The most critical file in the entire repository is **AGENTS.md**. It’s not ordinary notes; it’s the “long‑term rule file” for this learning repository—a project instruction manual written for Codex. It is recommended to have Codex write the following rules:

```plaintext
# AGENTS.md

You are my domain learning engineer.

The goal of this repository is to help me master AI Agent within 30 days and complete a small demonstrable project.

Your teaching process must follow:

1. First give the global map, then explain local details.
2. Every concept must include: one‑sentence explanation, real‑life analogy, technical explanation, real‑world case, and one exercise.
3. Don’t only teach theory; schedule a deliverable task every day.
4. Update progress.md after each learning session.
5. Schedule a phase test every week.
6. When I answer incorrectly, don’t just give the answer—diagnose the error type: unclear concept / cannot apply / unclear expression / confused knowledge.
7. Adjust the subsequent learning plan based on my weak points.
8. The ultimate goal is to help me complete a small demonstrable project.

With this file, Codex is no longer a one‑shot Q&A machine, but a teaching engine that runs according to rules.

Step 2: Build a Knowledge Map First, Don’t Dive into Details

The biggest risk when learning a new field is diving straight into a sea of materials. The correct order is:

See the map first → Learn concepts → Study cases → Do exercises → Finally do a project.

Input to Codex:

Please help me build a knowledge map of AI Agent using the Feynman technique.

Output:

1. What problem does the field of AI Agent actually solve?
2. Explain AI Agent in plain language that a primary school student can understand.
3. The 20 most important core concepts in this field.
4. The relationships between these concepts.
5. The 10 pairs of concepts that beginners most often confuse.
6. The 5 stages you need to go through from beginner to being able to do a project.
7. The artifact you must produce in each stage.
8. A minimal necessary knowledge list—only keep what you truly have to learn.
9. Content you don’t need to learn for now, to avoid distraction.
10. The recommended learning sequence.

What you want Codex to do is not “pile up materials” but “compress the domain.” You can ask it to break knowledge into three types:

- **The 20% you must know first**: Determines whether you can get started.
- **The 60% you can skip for now**: You won’t use it even if you learn it now.
- **The 20% to deepen later**: Come back after you’ve done the project.

The goal of this step is not to finish learning, but to build a clear sense of direction: where am I now, what do I need to learn, what can I ignore for now, and what artifact do I need to produce in the end.

## Step 3: Let Codex Generate a “Learning Skill”

When you find yourself repeatedly learning different domains, you can package this method into a reusable Codex Skill. Input:

```plaintext
Please create a Codex Skill for me named `domain-learning-master`.

Purpose: When I enter “I want to learn a certain domain,” it automatically sets up a domain learning system.

Follow the Codex Skills directory specification:

domain-learning-master/
├─ SKILL.md
├─ references/
├─ scripts/
└─ templates/

This Skill needs to support:

1. Building a domain knowledge map.
2. Breaking down core concepts.
3. Generating a 30‑day study plan.
4. Arranging a learning task each day.
5. Automatically generating practice questions and quizzes.
6. Pointing out knowledge gaps based on answers.
7. Writing mistakes, weak points, and learning progress into progress.md.
8. Conducting a phase test every 7 days.
9. Finally guiding the completion of a small project.
10. Generating corresponding project repository structures for different domains.

The **SKILL.md** generated by Codex should contain a clear workflow and daily teaching format. In this way, Codex is no longer just a temporary answer machine—it has a reusable “learning operating system”: learn a new domain, start a complete learning project with one command.

## Step 4: Learn Daily with an “Input‑Output” Pattern

Don’t ask “Please teach me machine learning”—that’s too vague; Codex will easily give you a generic explanatory text.

You should ask like this:

```plaintext
Today is Day 3. Please arrange today’s study based on progress.md.

Requirements:

1. First review the 5 key points from yesterday.
2. Check my weak points from yesterday.
3. Teach me 3 core concepts today.
4. Each concept must include: one‑sentence explanation, real‑life analogy, technical explanation, small case.
5. Give me 5 test questions.
6. Give me a small task that can be completed within 60 minutes.
7. Provide acceptance criteria for the task.
8. After I finish, please check my answers.
9. Update progress.md based on my performance.
10. Generate tomorrow’s study suggestions.

At the end of each day, input a review instruction so Codex can summarize what you mastered, what you didn’t, which type of mistakes you made (unclear concept / cannot apply / unclear expression / confused knowledge), write incorrect questions into progress.md, update the glossary, generate a daily knowledge compression card, and arrange the next day’s tasks.

This way, learning is no longer a one‑time chat but an evolving project. Codex is responsible for maintaining progress.md, glossary, knowledge cards, quiz bank, daily review, and project outcomes—you just need to complete tasks, submit answers, review mistakes, and push the project forward each day.

## Step 5: Do a Small Project for Every Domain

Truly mastering a domain is not “understanding it” but “being able to build something.” Use the following template to let Codex design a minimum viable project based on your foundation:

```plaintext
I don’t want to just learn theory. Please help me design a minimum viable project to verify whether I have truly mastered this domain.

Domain: AI Agent
My background: Beginner in programming
Time: 7 days
Goal: Create a demonstrable artifact

Please output: project name, one‑sentence introduction, core features, required knowledge points, daily task schedule, daily deliverables, acceptance criteria, final presentation method. If I can’t code, give me a low‑code solution. If I want to advance further, give me upgrade directions.

Different domains can correspond to different minimal projects—for AI Agent you can build an automatic document‑organizing Agent, for prompt engineering you can build a personal prompt template library, for data analysis you can build a sales dashboard, for writing you can build a topic‑outline‑draft‑revision workflow. The smaller the project the better, but it must be complete: it can run, be demonstrated, be explained, be iterated, and prove that you have truly learned it.

## Step 6: Let Codex Play the Role of “Examiner”, Not Just Teacher

The fastest learners are not those who listen the most, but those who are tested the most. After finishing a phase, input:

```plaintext
Please stop teaching new knowledge. Now you are a strict examiner.

Based on the current content in the learning repository, conduct a phase test for me.

Test format: 10 multiple‑choice questions, 5 concept‑explanation questions, 3 scenario‑application questions, 1 comprehensive project question.

Requirement: First present the questions without giving answers; score after I answer. Provide a score for each question and a total score. Point out the reasons for mistakes and diagnose the error type (unclear concept / cannot apply / unclear expression / confused knowledge). Record weak points in progress.md, reorder the subsequent study plan, and give me 3 targeted remedial exercises.

This step is crucial. What you really need is not more knowledge, but to discover: which things did you think you understood but didn’t? Which can you understand but cannot apply? Which concepts can you name but cannot articulate? Which are the gaps you most need to fill?

## Step 7: Use “Domain Compression Cards” for Quick Review

After each day or module, let Codex generate a compression card:

```plaintext
Please compress what I learned today into a domain knowledge card.

Format: one‑sentence explanation, 5 keywords, 3 typical application scenarios, 2 common misconceptions, 1 classic case, 1 self‑test question, connection to previous knowledge, my easiest‑to‑forget points, suggested review time.

Save it to 05_flashcards/.

After 30 days, you will have your own set of domain card decks. These cards are not materials given to you by someone else, but personal knowledge assets generated from your learning process, mistakes, and project experience.

The Most Powerful General Prompt Template

For any future field, you can directly apply the following:

I want to quickly master a new domain with Codex.

Domain: {fill in domain}
My background: {fill in foundation}
Daily time: {fill in time}
Goal: {exam / work / project / article / product / investment research}
Final artifact: {what you want to produce, or let me recommend if unsure}

Please act as my domain learning architect and complete the following tasks:

1. Create a learning repository.
2. Generate a knowledge map of the domain.
3. Extract the 20 most important core concepts.
4. Design a 30‑day learning roadmap.
5. Arrange “study + practice + output + quiz” each day.
6. Create a glossary, error book, case library, project directory.
7. Use AGENTS.md to solidify learning rules.
8. Update progress.md after each learning session.
9. Conduct a phase test every 7 days.
10. Finally guide me to complete a small demonstrable project.

Learning principles:
- Less empty talk, more tasks.
- Global first, then details.
- Usable first, then deep.
- Output every day.
- All knowledge must be verified through exercises.
- Every phase must have a deliverable.
- Don’t pursue volume of materials—only pursue understanding, ability to restate, ability to apply, and ability to build a project.

Please first create the repository structure, then generate Day 1 tasks.

Recommended 30‑Day Learning Rhythm

Start each day by asking: “Please arrange today’s study tasks based on progress.md.” At the end of each day, do a complete review—summarize what you mastered, point out what you didn’t, diagnose weak‑point types, update progress.md and glossary, generate today’s knowledge card, and arrange tomorrow’s tasks.

The Most Important Mindset

Using Codex to learn any domain is not about “feeding you knowledge”—it’s about letting it help you build five systems:

Knowledge Map — solves “I don’t know what’s in this domain.”
Glossary — solves “I can’t understand the technical terms.”
Practice System — solves “I thought I understood, but I didn’t.”
Project System — solves “I learned a lot, but can’t use it.”
Review System — solves “I forget after learning, and don’t correct mistakes.”

The truly efficient way to learn is: ChatGPT is responsible for explaining knowledge clearly, Codex is responsible for engineering the learning process, and you are responsible for completing tasks, submitting answers, reviewing mistakes, and advancing the project.

In the end, what you get is not a pile of fragmented notes, but a personal learning system that can be continuously iterated.