This article focuses on Skill architecture design in AI-assisted development. It explains how Rules, DAO Skills, foundational Service Skills, and Workflow can form composable engineering capabilities to solve three common problems: unconstrained code generation, unstable requirement understanding, and risky automation. Keywords: Skill architecture, Rules governance, Git Workflow.
Technical Specifications at a Glance
| Parameter | Details |
|---|---|
| Domain | AI-assisted development engineering |
| Core Languages | Java, Python, Markdown |
| Primary Protocols / Standards | Conventional Commits, JPA Specification, MIT License |
| Typical Tech Stack | Spring Boot, Spring Data JPA, MySQL, Lombok |
| Repository Form | Knowledge packs / rule packs / workflow documentation |
| Star Count | Not provided in the source |
| Core Dependencies | BaseAuditEntity, Repository, QueryParam, gh CLI |
This design upgrades AI from “able to write code” to “able to deliver code under constraints”
Although the original title mentioned BitLocker, the actual body of the article centers on AI coding system design. The focus is not a specific encryption API, but how to package domain knowledge, code templates, constraint rules, and execution processes into capability units that AI can invoke.
This design targets three engineering pain points: inconsistent generation results, difficulty injecting project conventions, and poor decomposition of complex tasks. Instead of asking the model to infer everything from scratch every time, you provide structured Skills so the AI can operate within explicit boundaries.
The basic Skill directory structure should combine knowledge, templates, and scripts
/mnt/skills/dao-crud/
├── SKILL.md # Usage guide and applicable scenarios
├── templates/ # Code templates
├── examples/ # Example inputs and outputs
└── scripts/ # Automation scriptsThis directory reflects a key principle: a Skill is not a one-line prompt. It is a reusable engineering asset package.
Rules, Skills, and Workflow should form a layered system
The original article proposes a three-layer model: the Rules layer defines standards and constraints, the Skills layer provides capability modules, and the Workflow layer handles process orchestration. This structure is more stable than a single prompt because it separates how to do it, what to do, and when to execute it.
Rules provide unified coding standards and domain knowledge. Skills complete specific tasks such as DAO generation or employee queries. Workflow connects code review, commit message generation, and PR management into a single process.
Rules → Skills → Workflow
Standards Capability modules Process automationThis means AI no longer faces ambiguous requirements directly. It is first constrained by rules, then driven by skills, and finally bounded by workflow.
The value of a DAO Skill lies in binding database changes to code generation
The DAO Skill is the most complete capability example in the article. It covers three categories of tasks: generating DDL, generating Entity/Repository/QueryParam code, and applying field additions or removals to existing tables while synchronizing the related code updates.
Its key value is not simply generating a few Java classes. Its real value is turning table structure, index guidance, comment conventions, query rules, and audit fields into a unified executable specification. That is what makes AI-generated code consistent and maintainable.
A qualified DAO Skill must enforce strong built-in constraints
@Entity
@Table(name = "product")
public class Product extends BaseAuditEntity {
/** Product name */
private String name; // Text field used for fuzzy matching
/** Product price */
private BigDecimal price; // Monetary values must use BigDecimal to prevent precision loss
}This structure expresses several hard constraints: inherit from a base audit entity, use BigDecimal for monetary amounts, and provide complete field comments. The purpose of a code template is to turn these constraints into the default implementation path.
Query parameter modeling determines the upper bound of reusable query capability
The original naming rules for QueryParam are especially valuable. Query types are inferred automatically from field semantics: fields like name/title use fuzzy matching, status/type/code use IN queries, and time-related fields use range queries.
This is far more reasonable than letting AI generate Repository methods arbitrarily. With this approach, all queries converge on a unified Specification-building logic, which reduces API sprawl and duplicate implementations.
Dynamic queries should be unified through a Specification builder
public Specification
<Product> buildSpecification(ProductQueryParam param) {
return (root, query, cb) -> {
List
<Predicate> predicates = new ArrayList<>();
if (param.getName() != null) {
predicates.add(cb.like(root.get("name"), "%" + param.getName() + "%")); // Use fuzzy matching for the name field
}
if (param.getStatuses() != null && !param.getStatuses().isEmpty()) {
predicates.add(root.get("status").in(param.getStatuses())); // Use exact set-based matching for status values
}
return cb.and(predicates.toArray(new Predicate[0]));
};
}This code combines multiple query conditions into a reusable JPA Specification.
A foundational Service Skill is closer to a capability interface specification
Unlike a DAO Skill, a foundational Service Skill does not emphasize code generation. Instead, it emphasizes capability boundaries. Using EmployeeService in an EHR scenario as an example, the key is to tell the AI which methods exist, which scenarios each method fits, and what level of result detail each one returns.
This kind of Skill is ideal for stable foundational capabilities such as employee, organization, and position master data services. These services usually change slowly, but many business modules reuse them.
A foundational Service Skill should define method semantics before implementation details
public interface EmployeeService {
List
<EmployeeDTO> queryEmployeeRoster(QueryParam param); // Return the full employee roster
List
<EmployeeChangeDTO> queryEmployeeChangesTimeline(Long employeeId, LocalDate start, LocalDate end); // Return the employee change timeline
List
<EmployeeLiteDTO> listEmployees(QueryParam param); // Return a lightweight base list
}This interface standardizes query capabilities into three scenarios: full, historical, and lightweight.
A Git Workflow Skill must prioritize safety constraints
Compared with DAO and Service Skills, the biggest difference in a Git Workflow Skill is that it can trigger real operations. The original article explicitly lists high-risk commands that must not run, such as force push, hard reset, branch deletion, and rewriting already-pushed history.
A Skill in this category cannot focus only on whether it can do something. It must also account for the cost of doing the wrong thing. For that reason, this type of Skill is better designed to require human confirmation instead of running fully automatically.
AI Visual Insight: This image is an entry badge for a platform-level AI reading assistant. It does not communicate business data. Instead, it signals that article consumption has shifted from traditional reading to three intelligent interaction modes: summarization, follow-up questioning, and code explanation. As a result, content design should favor structured knowledge units.
A safety-oriented Workflow should follow a whitelist model
git status # Read-only check of the working tree state
git diff --staged # Analyze staged changes to generate a commit message
git push origin feature/x # Safely push the current feature branch
# Forbidden: git push --force # Avoid rewriting remote historyThis command set restricts AI to non-destructive Git operations and prioritizes the safety of code assets.
The real difficulty is never Skill authoring. It is problem definition.
The final judgment in the original article is critical: Skills can continue to expand, but if requirements are not defined correctly, even the strongest AI will only make mistakes more consistently. The essence of engineering is not stacking more templates. It is improving your ability to model the problem.
A more mature direction, then, is not to pursue stronger generation in isolation. It is to establish a complete chain from requirement understanding, task decomposition, and rule binding to execution validation. In the future, developers will look more like orchestrators of AI capabilities.
FAQ
1. Can the Rules layer also be designed as a Skill?
Yes, but only if the rules themselves are modular enough to be referenced and composed. If the project is still in an exploratory stage, it is usually clearer to manage Rules independently first. After the system matures, turning them into Skills is the safer path.
2. Why should a DAO Skill prohibit foreign keys and JPA relationship mappings?
The goal is to reduce generation complexity and runtime risk. This avoids AI-generated deep object graphs, N+1 query problems, and tightly coupled migrations. Using redundant ID fields instead better supports service-oriented evolution.
3. Why is full automation not recommended for Git Workflow?
Because the difference between a dangerous command and a safe command is often just one parameter. Command names alone are not enough to eliminate risk. Any operation involving commit history, branch deletion, or force push must retain human confirmation.
Core Summary: This article reconstructs the original discussion of Skill system design for AI-assisted development. It focuses on the responsibility boundaries, constraint models, and composition patterns of Rules, DAO Skills, foundational Service Skills, and Git Workflow, and summarizes practical engineering principles for handling complex requirements.