Structured Autonomy Generate

An AI skill that generates code and project artifacts following structured autonomy principles, producing well organized outputs with clear boundaries, consistent patterns, and documented decision points so teams can review and extend generated work confidently.

What Is This?

Overview

This skill generates code, configuration files, and project scaffolding using a structured autonomy framework. Every generated artifact follows explicit conventions, includes inline documentation of decisions made during generation, and provides clear extension points. The output is organized into logical modules with defined interfaces between them, making it easy to understand what was generated and why each choice was made.

Who Should Use This

Designed for teams that need AI generated code they can trust and maintain. Ideal for organizations with strict review requirements, regulated industries needing audit trails, and teams building on generated scaffolding.

Why Use It?

Problems It Solves

AI generated code often feels like a black box. Developers receive working code but have no insight into why specific patterns were chosen, what alternatives were considered, or where the code expects human customization. This makes review difficult and maintenance risky.

Core Highlights

• Decision Documentation every significant choice includes a comment explaining the reasoning
• Clear Boundaries generated code is separated from areas intended for customization
• Consistent Patterns applies the same structural conventions across all generated files
• Extension Points marks specific locations where custom logic should be added
• Audit Friendly produces output that satisfies code review and compliance requirements

How to Use It?

Basic Usage

Describe what you need and the skill generates structured, well documented output.

// Generated: src/services/user-service.ts
// [Decision] Using repository pattern to separate data access from business logic.
// [Extension Point] Add custom validation in validateUser method.

export class UserService {
  constructor(private readonly userRepo: UserRepository) {}

  async createUser(input: CreateUserInput): Promise<User> {
    // [Generated] Input validation with schema check
    const validated = this.validateUser(input);
    const user = await this.userRepo.create(validated);
    return user;
  }

  // [Extension Point] Customize validation rules below
  private validateUser(input: CreateUserInput): CreateUserInput {
    if (!input.email.includes("@")) {
      throw new ValidationError("Invalid email format");
    }
    return input;
  }
}

Real-World Examples

Microservice Scaffold Generation

A fintech team used this skill to generate a payment processing microservice. The output included 14 files organized across service, repository, controller, and middleware layers. Each file contained decision annotations explaining pattern choices and where to add custom business logic.

project: payment-service
generated_at: "2025-03-04"
artifacts:
  - path: src/controllers/payment.ts
    purpose: HTTP request handling and response formatting
    decisions: ["REST over GraphQL for simplicity"]
    extension_points: ["Custom payment validation"]
  - path: src/services/payment.ts
    purpose: Business logic and orchestration
    decisions: ["Saga pattern for multi-step transactions"]
    extension_points: ["Fee calculation", "Fraud checks"]
  - path: src/repositories/payment.ts
    purpose: Database access layer
    decisions: ["Prisma ORM for type safety"]

Advanced Tips

Use the generated manifest file as a checklist during code review. Each artifact lists its decisions and extension points, making it easy to verify that reviewers understand every component. Customize the generation template to match your team conventions before running.

When to Use It?

Use Cases

• Microservice Scaffolding generate new services with consistent structure
• CRUD Module Generation produce data access layers with documented patterns
• API Endpoint Creation scaffold controllers with validation and error handling
• Configuration Generation produce environment configs with annotated defaults
• Regulated Development satisfy audit requirements with documented decisions

Related Topics

When working with structured code generation, these prompts activate the skill:

• "Generate a service with structured autonomy"
• "Create a documented scaffold for this feature"
• "Generate code with decision annotations"
• "Build a new module with clear extension points"

Important Notes

Requirements

• Works with TypeScript, JavaScript, Python, and Go project structures
• Benefits from a project conventions document to align generated patterns
• Requires clear input describing the desired functionality and constraints
• Version control recommended for tracking changes to generated artifacts

Usage Recommendations

Do:

• Review the manifest file first to understand the full scope of generated output
• Customize extension points before building on the generated scaffold
• Keep decision annotations updated as you modify generated code
• Use consistent templates across your team for uniform output

Don't:

• Delete decision comments as they provide valuable context for future maintainers
• Modify generated boundaries without updating the manifest documentation
• Skip code review just because the code was generated with structure
• Ignore extension points as they mark where custom logic is expected

Limitations

• Generated code reflects patterns at the time of generation and may not track evolving practices
• Complex domain logic requires human implementation at extension points
• Decision documentation adds lines to files which some teams may find verbose
• Template customization requires understanding the generation framework