Skip to content
FunCoding

Search

Search docs, Skills and MCP

golang-dependency-injection

Comprehensive guide for dependency injection (DI) in Golang. Covers why DI matters (testability, loose coupling, separation of concerns, lifecycle management), manual constructor injection, and DI library comparison (google/wire, uber-go/dig, uber-go/fx, samber/do). Use this skill when designing service architecture, setting up dependency injection, refactoring tightly coupled code, managing singletons or service factories, or when the user asks about inversion of control, service containers, or wiring dependencies in Go. For a specific DI library, → See `samber/cc-skills-golang@golang-google-wire`, `samber/cc-skills-golang@golang-uber-dig`, `samber/cc-skills-golang@golang-uber-fx`, or `samber/cc-skills-golang@golang-samber-do` skills.

代码质量与审查3.4kskills/golang-dependency-injection/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/samber/cc-skills-golang/golang-dependency-injection/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Persona: You are a Go software architect. You guide teams toward testable, loosely coupled designs — you choose the simplest DI approach that solves the problem, and you never over-engineer.

Orchestration mode: Fan out the three sub-agents described in Refactor mode (global/init discovery, concrete-dependency mapping, service-locator detection) when refactoring a large coupled codebase toward dependency injection, and consolidate into one migration plan. On Claude Code, use ultracode to opt into multi-agent orchestration explicitly.

Modes:

  • Design mode (new project, new service, or adding a service to an existing DI setup): assess the existing dependency graph and lifecycle needs; recommend manual injection or a library from the decision table; then generate the wiring code.
  • Refactor mode (existing coupled code): use up to 3 parallel sub-agents — Agent 1 identifies global variables and init() service setup, Agent 2 maps concrete type dependencies that should become interfaces, Agent 3 locates service-locator anti-patterns (container passed as argument) — then consolidate findings and propose a migration plan.

Community default. A company skill that explicitly supersedes samber/cc-skills-golang@golang-dependency-injection skill takes precedence.

Dependency Injection in Go

Dependency injection (DI) means passing dependencies to a component rather than having it create or find them. In Go, this is how you build testable, loosely coupled applications — your services declare what they need, and the caller (or container) provides it.

This skill is not exhaustive. When using a DI library (google/wire, uber-go/dig, uber-go/fx, samber/do), refer to the library's official documentation and code examples for current API signatures.

For interface-based design foundations (accept interfaces, return structs), see the samber/cc-skills-golang@golang-structs-interfaces skill.

Best Practices Summary

  1. Dependencies MUST be injected via constructors — NEVER use global variables or init() for service setup
  2. Small projects (< 10 services) SHOULD use manual constructor injection — no library needed
  3. Interfaces MUST be defined where consumed, not where implemented — accept interfaces, return structs
  4. NEVER use global registries or package-level service locators
  5. The DI container MUST only exist at the composition root (main() or app startup) — NEVER pass the container as a dependency
  6. Prefer lazy initialization — only create services when first requested
  7. Use singletons for stateful services (DB connections, caches) and transients for stateless ones
  8. Mock at the interface boundary — DI makes this trivial
  9. Keep the dependency graph shallow — deep chains signal design problems
  10. Choose the right DI library for your project size and team — see the decision table below

Why Dependency Injection?

Problem without DIHow DI solves it
Functions create their own dependenciesDependencies are injected — swap implementations freely
Testing requires real databases, APIsPass mock implementations in tests
Changing one component breaks othersLoose coupling via interfaces — components don't know each other's internals
Services initialized everywhereCentralized container manages lifecycle (singleton, factory, lazy)
All services loaded at startupLazy loading — services created only when first requested
Global state and init() functionsExplicit wiring at startup — predictable, debuggable

DI shines in applications with many interconnected services — HTTP servers, microservices, CLI tools with plugins. For a small script with 2-3 functions, manual wiring is fine. Don't over-engineer.

Manual Constructor Injection (No Library)

For small projects, pass dependencies through constructors. See Manual DI examples for a complete application example.

// ✓ Good — explicit dependencies, testable
type UserService struct {
    db     UserStore
    mailer Mailer
    logger *slog.Logger
}

func NewUserService(db UserStore, mailer Mailer, logger *slog.Logger) *UserService {
    return &UserService{db: db, mailer: mailer, logger: logger}
}

// main.go — manual wiring
func main() {
    logger := slog.Default()
    db := postgres.NewUserStore(connStr)
    mailer := smtp.NewMailer(smtpAddr)
    userSvc := NewUserService(db, mailer, logger)
    orderSvc := NewOrderService(db, logger)
    api := NewAPI(userSvc, orderSvc, logger)
    api.ListenAndServe(":8080")
}
// ✗ Bad — hardcoded dependencies, untestable
type UserService struct {
    db *sql.DB
}

func NewUserService() *UserService {
    db, _ := sql.Open("postgres", os.Getenv("DATABASE_URL")) // hidden dependency
    return &UserService{db: db}
}

Manual DI breaks down when:

  • You have 15+ services with cross-dependencies
  • You need lifecycle management (health checks, graceful shutdown)
  • You want lazy initialization or scoped containers
  • Wiring order becomes fragile and hard to maintain

DI Library Comparison

Go has three main approaches to DI libraries:

Decision Table

CriteriaManualgoogle/wireuber-go/dig + fxsamber/do
Project sizeSmall (< 10 services)Medium-LargeLargeAny size
Type safetyCompile-timeCompile-time (codegen)Runtime (reflection)Compile-time (generics)
Code generationNoneRequired (wire_gen.go)NoneNone
ReflectionNoneNoneYesNone
API styleN/AProvider sets + build tagsStruct tags + decoratorsSimple, generic functions
Lazy loadingManualN/A (all eager)Built-in (fx)Built-in
SingletonsManualBuilt-inBuilt-inBuilt-in
Transient/factoryManualManualBuilt-inBuilt-in
Scopes/modulesManualProvider setsModule system (fx)Built-in (hierarchical)
Health checksManualManualManualBuilt-in interface
Graceful shutdownManualManualBuilt-in (fx)Built-in interface
Container cloningN/AN/AN/ABuilt-in
DebuggingPrint statementsCompile errorsfx.Visualize()ExplainInjector(), web interface
Go versionAnyAnyAny1.18+ (generics)
Learning curveNoneMediumHighLow

Quick Comparison: Wiring Style

The same graph — Config -> Database -> UserStore -> UserService -> API — wired by hand and by a container. The contrast is what the wiring code encodes: an ordered call sequence you maintain, versus a set of providers the container orders for you.

// Manual — you own the order; adding a dependency means editing every call site downstream
cfg := NewConfig()
db := NewDatabase(cfg)
store := NewUserStore(db)
svc := NewUserService(store)
api := NewAPI(svc)
api.Run()
// No shutdown hooks, health checks, or lazy loading — add them yourself

// Container (samber/do) — order is derived from the constructor signatures
i := do.New()
do.Provide(i, NewConfig)
do.Provide(i, NewDatabase)
do.Provide(i, NewUserStore)
do.Provide(i, NewUserService)
api := do.MustInvoke[*API](i)
api.Run()
defer i.Shutdown() // shutdown and health checks come from the container

google/wire and uber-go/fx express the same graph differently: wire generates the manual sequence above at build time from a wire.Build provider list (cleanup via func() returned by providers, no lifecycle hooks), while fx registers providers with fx.Provide and resolves them by reflection at runtime with OnStart/OnStop hooks. Full wiring examples for each: google/wire, uber-go/dig + fx, samber/do.

Testing with DI

DI makes testing straightforward — inject mocks instead of real implementations:

// Define a mock
type MockUserStore struct {
    users map[string]*User
}

func (m *MockUserStore) FindByID(ctx context.Context, id string) (*User, error) {
    u, ok := m.users[id]
    if !ok {
        return nil, ErrNotFound
    }
    return u, nil
}

// Test with manual injection
func TestUserService_GetUser(t *testing.T) {
    mock := &MockUserStore{
        users: map[string]*User{"1": {ID: "1", Name: "Alice"}},
    }
    svc := NewUserService(mock, nil, slog.Default())

    user, err := svc.GetUser(context.Background(), "1")
    if err != nil {
        t.Fatalf("unexpected error: %v", err)
    }
    if user.Name != "Alice" {
        t.Errorf("got %q, want %q", user.Name, "Alice")
    }
}

Testing with samber/do — Clone and Override

Container cloning creates an isolated copy where you override only the services you need to mock:

func TestUserService_WithDo(t *testing.T) {
    // Create a test injector with mock implementation
    testInjector := do.New()

    // Provide the mock UserStore interface
    do.OverrideValue[UserStore](testInjector, &MockUserStore{
        users: map[string]*User{"1": {ID: "1", Name: "Alice"}},
    })

    // Provide other real services as needed
    do.Provide[*slog.Logger](testInjector, func(i *do.Injector) (*slog.Logger, error) {
        return slog.Default(), nil
    })

    svc := do.MustInvoke[*UserService](testInjector)
    user, err := svc.GetUser(context.Background(), "1")
    // ... assertions
}

This is particularly useful for integration tests where you want most services to be real but need to mock a specific boundary (database, external API, mailer).

When to Adopt a DI Library

SignalAction
< 10 services, simple dependenciesStay with manual constructor injection
10-20 services, some cross-cutting concernsConsider a DI library
20+ services, lifecycle management neededStrongly recommended
Need health checks, graceful shutdownUse a library with built-in lifecycle support
Team unfamiliar with DI conceptsStart manual, migrate incrementally

Common Mistakes

MistakeFix
Global variables as dependenciesPass through constructors or DI container
init() for service setupExplicit initialization in main() or container
Depending on concrete typesAccept interfaces at consumption boundaries
Passing the container everywhere (service locator)Inject specific dependencies, not the container
Deep dependency chains (A->B->C->D->E)Flatten — most services should depend on repositories and config directly
Creating a new container per requestOne container per application; use scopes for request-level isolation

Cross-References

  • → See samber/cc-skills-golang@golang-samber-do skill for detailed samber/do usage patterns
  • → See samber/cc-skills-golang@golang-structs-interfaces skill for interface design and composition
  • → See samber/cc-skills-golang@golang-testing skill for testing with dependency injection
  • → See samber/cc-skills-golang@golang-project-layout skill for DI initialization placement

References

Similar Skills

claude-api
anthropics/skills180k

claude-api

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

Code quality & review

ponytail-review
DietrichGebert/ponytail158k

ponytail-review

Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-review.

Code quality & review

code-review-and-quality
addyosmani/agent-skills103k

code-review-and-quality

Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch. Use when asked to review a diff or a pull request, even when the diff is pasted inline.

Code quality & review

documentation-and-adrs
addyosmani/agent-skills103k

documentation-and-adrs

Records decisions and documentation. Use when you need to document an architecture decision (ADR) or the reasoning behind a design choice, when changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.

Code quality & review

code-simplification
addyosmani/agent-skills103k

code-simplification

Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.

Code quality & review

understand
Egonex-AI/Understand-Anything86k

understand

Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships

Code quality & review