祝融说。

You open the list of 14 skills and see a bunch of unfamiliar names—Architect, Inspector, Orchestrator, Workflow. You don't know where to start. Don't worry. As a manager, you don't need to master the details of every skill, but you do need to know what problems they solve and which ones you need to drive into existence.

You receive a list of 14 skills, each with a professional-sounding name—Architect, Inspector, Orchestrator, Workflow. Your first reaction might be: "Do I need to learn all of them?"

The answer is: no. As a manager, your responsibility is not to master the operational details of each skill, but to know which skills you need to drive into existence, which skills the team can use autonomously, and which skills only need to be introduced for specific scenarios.

This is like managing a restaurant. You don't need to know how to make every dish, but you need to know whether the kitchen process is sound, whether the quality of the food coming out is stable, and whether the supply chain for ingredients is reliable. The 14 skills are the "kitchen process" of AI coding—you don't need to know how to make every dish, but you need to know whether the process is running smoothly.

2.1 Skill Classification from the Manager's Perspective

From a manager's perspective, the 14 skills can be divided into three categories:

Category One: Governance Skills (Managers need to drive them into existence)

These skills are part of the team's norms and require the manager to drive them into existence.

Category Two: Execution Skills (The team uses them autonomously; the manager needs to understand them)

These skills are used in the team's day-to-day development. The manager does not need to master the details, but needs to understand what they do.

Category Three: Specialized Skills (Used in specific scenarios; the manager needs to understand the applicable scenarios)

These skills are used in specific scenarios. The manager needs to know whether the team has the capabilities and tools to handle these scenarios.

There is a hidden logical relationship among the three categories: governance skills determine whether you can "do things right," execution skills determine whether you can "do things fast," and specialized skills address whether you can "do special things." As a manager, you first need to ensure the governance skills are in place—without them, the quality of AI coding is uncontrollable. Execution skills can be rolled out gradually once governance skills are established, and specialized skills can be introduced when specific scenarios arise.

2.2 The Correspondence Between Skills and Team Roles

2.3 The Skill Maturity Model

You can use this model to evaluate the team's level of mastery of each skill. Why is a maturity model needed? Because there is a wide gap between "knowing" and "doing." You may have established blueprint standards, but is the team actually following them? Without quantitative evaluation, you can only rely on intuition—and intuition is often unreliable.

The maturity model divides the level of mastery into five grades:

Evaluation example:

Team skill maturity assessment:

Architect (Architecture Design): L2 - Standardized Use
  - A blueprint is produced before every project begins ✓
  - The blueprint is updated regularly ✓
  - There is no unified standard for blueprint quality ✗

Inspector (Supervision & Acceptance): L1 - Trying It Out
  - Some members carry out acceptance, but it is not mandatory ✓
  - There is no unified acceptance criteria ✗
  - Acceptance results are not recorded ✗

2.4 Skill Investment Priorities

With limited resources, it is recommended to invest according to the following order of priority. Why this order? Because governance skills are the "foundation," and execution skills are the "superstructure." If you build the superstructure before the foundation is solid, the building will collapse.

First Priority (Must be established)

Architect — Without a blueprint, AI coding has no direction. The blueprint is the foundation of quality.

Inspector — Without acceptance, the quality of AI coding is uncontrollable. Acceptance is the guarantee of quality.

Recommendation: Before promoting AI coding across the team, first establish the standards for blueprints and acceptance.

Second Priority (Establish as soon as possible)

Workflow — Automated Workflow is key to efficiency gains. When requirements are clear, Workflow can significantly reduce human intervention.

Advisor — The Advisory skill helps the team resolve difficult technical decisions, reducing decision bottlenecks.

Recommendation: Once the team has mastered blueprints and acceptance, promote Workflow and Advisor.

Third Priority (Establish gradually)

Orchestrator — Project Orchestration suits multi-feature projects; introduce it after the team is familiar with Workflow.

QA — Quality Assurance is a complement to acceptance; establish it once the acceptance system has matured.

Requirements — Requirements Analysis is introduced when project complexity is high.

Recommendation: As team maturity improves, gradually introduce these skills.

Fourth Priority (Establish on demand)

Cloner, POC, Legacy Recon, Next, Job, Coach, Frontend Architect

These skills are used in specific scenarios and should be introduced on demand, without the need for widespread promotion.

Chapter Summary

From a manager's perspective, the 14 skills fall into three categories—governance, execution, and specialized—and this classification is itself a "manager's guide": governance skills require you to drive them into existence, execution skills can be used autonomously by the team, and specialized skills are introduced on demand. The skill maturity model helps you quantitatively evaluate the team's level of mastery, and the investment priorities tell you where to begin investing. Remember: governance skills are the foundation. If the foundation is not properly laid, no matter how beautiful the superstructure, it will collapse. In the next chapter, we will learn how to introduce this methodology to your team.