Content Overview
Here is a content map of the entire site. The articles follow a practical learning sequence and match the four stages in the navigation: beginner, intermediate, advanced, and mastery.
Status description:
- Published: The article has been completed and can be clicked to read.
- Under planning: The article structure has been established, the main text is still being written, and no reading link is provided yet.
A total of 16 topic articles are currently planned, of which 8 have been published and 8 are in planning.
Beginner stage — complete an AI Coding feedback loop first
| Sequence | Tasks | Articles | Main Focus | Status |
|---|---|---|---|---|
| 1 | CXZ-61 | How to choose AI Coding tools: A popular guide for beginners | Execution, model management, verification | Published |
| 2 | CXZ-60 | AI Coding Quick Start: Develop a web application in 10 minutes | Input, execution, output | Published |
| 3 | CXZ-94 | From Localhost to a Global Website: Deploy with Codex, GitHub, and Cloudflare | Execution, deployment, verification | Published |
Stage goal: Understand mainstream AI Coding workflows, complete the loop from requirements to manual acceptance, and deploy the result as a globally reachable website.
Intermediate stage - establishing a stable delivery closed loop
| Sequence | Tasks | Articles | Main Focus | Status |
|---|---|---|---|---|
| 1 | CXZ-87 | Let AI Coding Agent truly understand the project: from Rules to AGENTS.md | Input, execution, verification | Published |
| 2 | CXZ-86 | Why does AI Coding get messier with every change? From requirements to Spec-Driven Development | Input, execution, verification | Published |
| 3 | CXZ-92 | AI Coding How to ensure code quality? From code generation to verification engineering | Output, quality assessment | Planning |
| 4 | CXZ-65 | MCP Panorama Guide: How AI can securely connect the real world | Execution, permission boundaries | Released |
| 5 | — | Let AI Coding Agent open the browser to troubleshoot by itself: Chrome DevTools MCP practice | Execution, verification | Published |
Main line of the stage: Project rules → Requirement specifications → Result verification → External capability expansion → Browser debugging closed loop.
Master stage - accumulation of reusable abilities
| Sequence | Tasks | Articles | Main Focus | Status |
|---|---|---|---|---|
| 1 | CXZ-93 | Context Engineering: Let AI Coding tools understand large code bases | Input | Planning |
| 2 | CXZ-64 | From Prompt to Agent Skill: Package Expert Knowledge as a Reusable Capability | Execution, capability reuse, quality evaluation | Published |
| 3 | CXZ-89 | From Rules, Skills to Plugin: How to engineer the combination of Agent capabilities | Execution | Under planning |
| 4 | TBD | How to disassemble and collaborate on complex tasks: AI Coding’s multi-agent workflow | Execution | Planning |
Main line of the stage: Context Engineering → Single Capability Encapsulation → Capability Combination → Complex Task Collaboration.
Master stage - Promoting large-scale team management
| Sequence | Tasks | Articles | Main Focus | Status |
|---|---|---|---|---|
| 1 | CXZ-88 | AI Coding Token and Cost Analysis: What exactly is consumed in a mission | Cost Analysis | Under Planning |
| 2 | CXZ-90 | AI Coding costs out of control? Understanding model call governance from AI Gateway | Gateway management, model management | Planning |
| 3 | CXZ-91 | How to measure the capabilities of AI Coding models? From Benchmark to real engineering evaluation | Model management, quality assessment | Planning |
| 4 | TBD | How to implement AI Coding in the team? Standards, asset library and evaluation mechanism | Global governance | Under planning |
Main line of the stage: Visible costs → Controllable calls → Evaluable capabilities → Sustainable team.
Supporting navigation
- Growth Route: Understand the progressive relationship between the four stages.
- Capability Map: View the capability structure from seven dimensions: input, execution, output, model, gateway, cost and quality.
Unified writing requirements
Every formal article should contain:
- A real engineering problem, rather than starting from a conceptual definition.
- A set of reusable judgment framework or operating procedures.
- A minimal example or project case.
- Risks, applicable boundaries and common misunderstandings.
- Verifiable reader learning outcomes.