Why Does My AI Agent Keep Generating Unidiomatic Go Code?

The Problem: AI-Generated Go Code That Doesn't Feel Right

You've integrated an AI coding assistant into your workflow. It generates Go code that compiles and runs, but something feels off. The code works, but it's verbose, uses anti-patterns, or ignores Go's idiomatic style. You spend more time refactoring the AI's output than you would writing it yourself.

This is a common frustration. Large language models are trained on vast amounts of code, but that code isn't always high-quality or idiomatic. They might generate:

• Overly clever code that sacrifices readability for brevity.
• Poor error handling, like ignoring errors or using generic error messages.
• Inefficient concurrency patterns that risk goroutine leaks or deadlocks.
• Non-idiomatic package structures that make code harder to maintain.

The result is technical debt. Your team spends cycles in code reviews pointing out the same issues: "Please wrap this error with context," "Don't use a nil map here," or "This interface is too broad." The AI assistant, lacking persistent context about your project's standards, repeats these mistakes.

What a Good Solution Should Change

A useful tool for this problem wouldn't just generate code. It would act as a guardrail and a teacher, ensuring the generated code adheres to established Go conventions. It should:

1. Enforce Core Principles: Automatically apply rules like "make the zero value useful" and "accept interfaces, return structs."
2. Standardize Error Handling: Generate code that wraps errors with context and uses errors.Is/errors.As correctly.
3. Promote Safe Concurrency: Suggest patterns like worker pools, proper context usage, and graceful shutdowns, while avoiding goroutine leaks.
4. Guide Interface Design: Encourage small, focused interfaces defined in the consumer package.
5. Improve Over Time: The solution should be a reusable skill that can be integrated into your agent's workflow, providing consistent guidance without manual prompting every time.

The goal is to shift the AI from generating functional code to generating maintainable, idiomatic code that aligns with your team's standards and Go's philosophy.

Introducing the golang-patterns Skill

One potential solution to explore is the golang-patterns skill. It's a curated set of instructions and examples designed to be fed to an AI agent (like those powered by Claude) to guide its Go code generation.

Think of it as a style guide and best-practices handbook that your AI agent can consult before writing or reviewing code. It's not a magic fix, but a structured way to inject Go idioms into the AI's output.

How It Works in Practice

The skill is a Markdown file (SKILL.md) containing patterns, examples, and anti-patterns. When integrated into an agent's context (e.g., via an MCP integration), the agent references this document when:

• Writing new Go code: The agent checks the patterns before generating functions, structs, or interfaces.
• Reviewing code: The agent can use the patterns to suggest improvements.
• Refactoring: The agent can apply the documented refactoring techniques.

For example, if you ask your agent to "write a function to fetch a user from a database," a well-configured agent using this skill would likely produce code that:

• Returns (*User, error) instead of just *User.
• Wraps the database error with context: fmt.Errorf("get user %s: %w", id, err).
• Uses a small, focused interface for the database dependency if the function is part of a service layer.

// Example output guided by the skill's patterns
func GetUser(ctx context.Context, store UserStore, id string) (*User, error) {
    user, err := store.GetUser(ctx, id)
    if err != nil {
        return nil, fmt.Errorf("get user %s: %w", id, err)
    }
    return user, nil
}

Evaluating If This Skill Fits Your Workflow

Before integrating any skill, it's wise to inspect it. Here’s how to evaluate golang-patterns:

Capability Boundaries

• What it does well: It provides a strong foundation for core Go patterns—error handling, concurrency, interface design, and package structure. It's excellent for ensuring new code follows established conventions.
• What it doesn't do: It's not a linter (like golangci-lint), a compiler, or a full style guide like Uber's. It won't catch every possible issue, and it doesn't replace static analysis tools. It also doesn't cover domain-specific patterns (e.g., how to structure a gRPC service or a specific algorithm).
• Best use cases:
  • Teams adopting Go who want consistent AI-assisted code generation.
  • Solo developers who want their AI assistant to produce cleaner code.
  • Projects where code review is bottlenecked by style and idiomatic issues.
• When not to use it: If your primary need is fixing compilation errors, optimizing performance at a micro-level, or enforcing very project-specific business rules, this skill alone won't suffice. It's a style guide, not a debugger or optimizer.

Setup Context and Safety Signals

• Integration: This skill is designed for use with AI agents that support MCP (Model Context Protocol) integrations. You would typically add the skill's content to your agent's system prompt or context window. There are no specific installation commands; it's about providing the right context.
• Safety: The skill itself is a set of instructions and code examples. It doesn't execute code or access external systems. The security risk is low, but always review the content of any skill you integrate to ensure it aligns with your standards. The repository is under a "Sponsors" license, which is worth noting.
• Repository Signals: The skill is part of the everything-claude-code repository. With over 228k stars, it indicates significant community interest, though forks are listed as 0. The topics (productivity, mcp, developer-tools, ai-agents) suggest it's aimed at developer productivity within AI agent workflows.

What to Inspect Before Using

1. Read the Full Skill: Don't just skim. Review the SKILL.md file to see if the patterns align with your team's style. Do you agree with all the examples? Are there patterns you'd want to add or modify?
2. Test with a Sample Task: Give your AI agent a small, well-defined Go task (e.g., "write a function to parse a CSV file with error handling") with and without the skill active. Compare the output for idiomatic quality.
3. Check for Conflicts: Ensure the skill's patterns don't conflict with your project's existing linter rules or style guides. For example, if your project uses a specific error package, the skill's generic fmt.Errorf pattern might need adjustment.
4. Consider Maintenance: The skill is a static file. If Go best practices evolve or your team's conventions change, you'll need to update the skill file yourself. There's no auto-update mechanism.

A Practical Option, Not a Silver Bullet

The golang-patterns skill is a practical option to inspect if you're struggling with unidiomatic AI-generated Go code. It provides a structured way to teach your AI agent the "Go way" of doing things, potentially reducing code review friction and improving codebase consistency.

However, it's not a set-and-forget solution. It requires initial evaluation, possible customization, and should be used in conjunction with other tools like linters and your own code reviews. Its value lies in providing a shared, documented set of conventions that both humans and AI agents can reference, leading to more predictable and maintainable code generation.

If your workflow involves frequent AI-assisted Go development and you value idiomatic code, it's worth taking the time to review the skill's content and run a small pilot test.