go-service-template-rest: A Production-Grade Go REST Service Template for AI-Assisted Development

The go-service-template-rest project analyzed in this article is a Go REST service template designed specifically for AI-assisted programming workflows. It corely provides an orchestrator-first workflow, project-level agents, a portable skill library, and a complete production-grade tech stack. Its aim is to fill the gaps in AI-native development repositories regarding the details of the Go language ecosystem, ensuring that the output from AI assistants adheres to Go idioms and meets production-grade code standards that can pass code reviews.

With the popularity of AI programming assistants like Codex and Claude Code, the work style of individual developers has changed. However, most AI-native repositories excel at requirement analysis and task planning but struggle with specific programming language ecosystems—especially Go. This project addresses the unique characteristics of Go backends by providing complete workflow guidance from architectural design to code review, solving this shortcoming.

Orchestrator-First Workflow

Adopts an explicit multi-stage workflow (requirement intake → planning → research → decision-making → specification definition → coding, etc.), with clear inputs/outputs and session wrappers for each stage, making AI-assisted development traceable and auditable.

Go-Native Guidance

Dives into Go ecosystem details: proper handling of goroutines/channels, Go idiomatic error handling, sqlc-generated type-safe database code, and chi router for building HTTP processing chains.

Project-Level Agents and Portable Skills

Defines professional agents (architecture, API, etc.) to provide analytical suggestions; skills are reusable workflow manuals stored in the .agents/skills directory for cross-project reuse.

OpenAPI-First HTTP Layer

Takes OpenAPI specification as the starting point for API design, paired with the chi router to ensure clear contracts and alignment with Go idiomatic processing chain patterns.

PostgreSQL and sqlc Data Layer

Chooses PostgreSQL + sqlc; sqlc generates Go code from SQL, balancing SQL expressiveness with compile-time type checking.

Built-in Observability

Preconfigures integration points for logging, metrics, and tracing, supporting troubleshooting and performance optimization in production environments.

CI/CD Gates

Includes testing, code style checks, and build validation to ensure code quality.

From Requirement to Specification

Requirement intake → plan execution strategy → parallel invocation of domain agents for research → comprehensive decision-making → pre-specification challenges → specification defined as spec.md and clarified.

From Design to Implementation

Technical design transformed into task plan → decomposed into tasks.md (including ID, dependencies, verification evidence) → coding strictly according to tasks.md.

Verification and Closure

Requires providing command evidence to prove completion; creation of new planning artifacts is not allowed to prevent scope creep.

The project defines 14 professional agents covering various dimensions of backend development:

• Architecture Agent: Module boundaries, ownership division
• API Agent: Contract behavior and HTTP semantics
• Concurrency Agent: Goroutine/channel correctness
• Data Agent: Data sources, schema evolution
• Domain Agent: Business invariants
• Reliability Agent: Timeout, retry strategies
• Security Agent: Authentication, tenant isolation
• Observability Agent: Logging, metrics design
• Performance Agent: Bottleneck analysis
• Quality Agent: Code review
• QA Agent: Test obligation definition Each agent has clear responsibilities to avoid overlap.

Applicable Scenarios:

1. Individual developers (who need to maintain code quality while using AI tools)
2. Small teams (who need reproducible processes and review standards)
3. Go learners (who want to understand production-grade service organization)
4. AI workflow explorers (who want to integrate AI into engineering processes) Usage Recommendations: Start by understanding workflow-plan.md, spec.md, and tasks.md, then participate in the full feature lifecycle to master the collaboration model.

This project represents a direction for AI-assisted development: amplifying developers' capabilities through structured workflows and domain guidance, rather than replacing human judgment. It provides the Go community with an entry point for discussing software engineering practices in the AI era, and will continue to evolve as AI tools advance.