Founders and operators
Use Codex to turn messy goals into plans, implementation tasks, review evidence and a clean handoff without pretending every task is a coding task.
Free OpenAI Codex tutorial
Make Codex
reliably useful
Turn OpenAI Codex from an impressive coding assistant into a repeatable operating layer for product, engineering and operations work.
CLI and cloud surface decisions
Durable repo context, skills and MCP
Proof gates for safe delivery
Get the full tutorial
A detailed, practical guide you can use while Codex is open beside your repo.
- Prompts for planning, context and verification
- AGENTS.md, skills, MCP and subagent patterns
- Team and enterprise governance checkpoints
No spam. Just the tutorial and useful follow-ups.
Who this is for
A Codex workflow for the work around the code.
Developers and technical PMs
Learn the context hierarchy, isolated work, skills, MCP and subagent patterns that make a repo legible to an agent.
Teams and enterprise
Map Codex-only seats, workspace controls, groups, app permissions and human review into a safe pilot and rollout path.
Inside the tutorial
The table of contents is a working system.
Choose the right Codex surface
Chat, Work and Codex serve different contexts. Start with the surface that matches the task and data boundary.
Write the outcome contract
Define the user, constraints, acceptance criteria, risks and evidence before asking for implementation.
Give the repo durable context
Use AGENTS.md and layered instructions so the same hard-won decisions survive the next session.
Add skills and MCP
Turn repeatable behavior into skills and connect tools only where permissions, schemas and failure modes are understood.
Parallelize with control
Use plans, isolated worktrees and subagents for independent work, then consolidate with explicit checks.
Ship evidence, not narration
Run type, unit, build, security and browser checks; report what changed, what passed and what remains uncertain.
The operating loop
From vague request to trustworthy result.
Frame
State the outcome, scope, constraints and definition of done.
Context
Load the relevant docs, repo rules, tools and current system facts.
Work
Let Codex make the smallest coherent change in an isolated surface.
Prove
Verify behavior, inspect the diff and leave a handoff another person can trust.
Teams and enterprise
The governance layer is part of the tutorial.
The guide includes an explicit team path: start with a bounded pilot, separate ChatGPT and Codex seat needs, and make permissions visible before connecting company data.
Workspace and seats
Understand Business and Enterprise workspaces, ChatGPT seats versus Codex-only seats, owners, admins, members and analytics viewers before rollout.
Groups, apps and permissions
Review group-based access, app/plugin controls, custom MCP boundaries, read/write actions and least-privilege defaults with an admin.
Review and rollout
Use a small pilot, approved repos, human review and evidence-based exit criteria. Never let a successful demo become an ungoverned production process.
Questions the tutorial answers
Is Codex only for software engineers?
No. The same framing and verification loop works for technical PMs, founders and operators working with a repo or a structured deliverable.
How is this different from a prompt list?
It teaches the surrounding system: context, permissions, persistent instructions, tools, isolation and proof.
Does it cover current team features?
Yes. The team chapter flags the parts that must be checked against your current OpenAI workspace plan and admin settings.
What should I bring?
One real task, the relevant repo or documents, and a willingness to inspect the result instead of accepting confident prose.
Created by Iwo Szapar
Built from real agent workflows.
Iwo builds AI operating systems for knowledge work and maintains production workflows where context, permissions and verification matter as much as the model.
Give Codex a system to work inside.
Get the tutorial, choose one real task and build your first evidence-gated Codex loop.
Open the full tutorialWant the broader memory layer? Explore AI Second Brain