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bobmatnyc
golang-grpc

Production gRPC in Go: protobuf layout, codegen, interceptors, deadlines, error codes, streaming, health checks, TLS, and testing with bufconn

Overview

Publisherbobmatnyc
Repositoryclaude-mpm-skills
Skill namegolang-grpc
Stars
75
Forks
19
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bobmatnyc on GitHub. Read the source before you install it.

Installation

Install the Golang Grpc AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bobmatnyc/claude-mpm-skills.git /tmp/claude-mpm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-mpm-skills/toolchains/golang/golang-grpc .claude/skills/golang-grpc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Golang Grpc in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Golang Grpc on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Golang Grpc is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Go gRPC (Production)

Overview

gRPC provides strongly-typed RPC APIs backed by Protocol Buffers, with first-class streaming support and excellent performance for service-to-service communication. This skill focuses on production defaults: versioned protos, deadlines, error codes, interceptors, health checks, TLS, and testability.

Quick Start

1) Define a versioned protobuf API

Correct: versioned package

proto
// proto/users/v1/users.proto
syntax = "proto3";

package users.v1;
option go_package = "example.com/myapp/gen/users/v1;usersv1";

service UsersService {
  rpc GetUser(GetUserRequest) returns (GetUserResponse);
  rpc ListUsers(ListUsersRequest) returns (stream User);
}

message GetUserRequest { string id = 1; }
message GetUserResponse { User user = 1; }
message ListUsersRequest { int32 page_size = 1; string page_token = 2; }

message User {
  string id = 1;
  string email = 2;
  string display_name = 3;
}

Wrong: unversioned package (hard to evolve)

proto
package users;

2) Generate Go code

Install generators:

bash
go install google.golang.org/protobuf/cmd/protoc-gen-go@latest
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@latest

Generate:

bash
protoc -I proto \
  --go_out=./gen --go_opt=paths=source_relative \
  --go-grpc_out=./gen --go-grpc_opt=paths=source_relative \
  proto/users/v1/users.proto

3) Implement server with deadlines and status codes

Correct: validate + map errors to gRPC codes

go
package usersvc

import (
    "context"

    "google.golang.org/grpc/codes"
    "google.golang.org/grpc/status"

    usersv1 "example.com/myapp/gen/users/v1"
)

type Service struct {
    usersv1.UnimplementedUsersServiceServer
    Repo Repo
}

type Repo interface {
    GetUser(ctx context.Context, id string) (User, error)
}

type User struct {
    ID, Email, DisplayName string
}

func (s *Service) GetUser(ctx context.Context, req *usersv1.GetUserRequest) (*usersv1.GetUserResponse, error) {
    if req.GetId() == "" {
        return nil, status.Error(codes.InvalidArgument, "id is required")
    }

    u, err := s.Repo.GetUser(ctx, req.GetId())
    if err != nil {
        if err == ErrNotFound {
            return nil, status.Error(codes.NotFound, "user not found")
        }
        return nil, status.Error(codes.Internal, "internal error")
    }

    return &usersv1.GetUserResponse{
        User: &usersv1.User{
            Id:          u.ID,
            Email:       u.Email,
            DisplayName: u.DisplayName,
        },
    }, nil
}

Wrong: return raw errors (clients lose code semantics)

go
return nil, errors.New("user not found")

Core Concepts

Deadlines and cancellation

Make every call bounded; enforce server-side timeouts for expensive handlers.

Correct: require deadline

go
if _, ok := ctx.Deadline(); !ok {
    return nil, status.Error(codes.InvalidArgument, "deadline required")
}

Metadata

Use metadata for auth/session correlation, not for primary request data.

Correct: read auth token from metadata

go
md, _ := metadata.FromIncomingContext(ctx)
auth := ""
if vals := md.Get("authorization"); len(vals) > 0 {
    auth = vals[0]
}

Interceptors (Middleware)

Use interceptors for cross-cutting concerns: auth, logging, metrics, tracing, request IDs.

Correct: unary interceptor with request ID

go
func unaryRequestID() grpc.UnaryServerInterceptor {
    return func(ctx context.Context, req any, info *grpc.UnaryServerInfo, handler grpc.UnaryHandler) (any, error) {
        id := uuid.NewString()
        ctx = context.WithValue(ctx, requestIDKey{}, id)
        resp, err := handler(ctx, req)
        return resp, err
    }
}

Streaming patterns

Server streaming (paginate or stream results)

Correct: stop on ctx.Done()

go
func (s *Service) ListUsers(req *usersv1.ListUsersRequest, stream usersv1.UsersService_ListUsersServer) error {
    users, err := s.Repo.ListUsers(stream.Context(), int(req.GetPageSize()))
    if err != nil {
        return status.Error(codes.Internal, "internal error")
    }

    for _, u := range users {
        select {
        case <-stream.Context().Done():
            return stream.Context().Err()
        default:
        }

        if err := stream.Send(&usersv1.User{
            Id:          u.ID,
            Email:       u.Email,
            DisplayName: u.DisplayName,
        }); err != nil {
            return err
        }
    }
    return nil
}

Unary vs streaming decision

  • Use unary for single request/response and simple retries.
  • Use server streaming for large result sets or continuous updates.
  • Use client streaming for bulk uploads with one final response.
  • Use bidirectional streaming for interactive protocols.

Production Hardening

Health checks and reflection

Add health service; enable reflection only in non-production environments.

Correct: health + conditional reflection

go
hs := health.NewServer()
grpc_health_v1.RegisterHealthServer(s, hs)

if env != "production" {
    reflection.Register(s)
}

Graceful shutdown

Prefer GracefulStop with a deadline.

Correct: graceful stop

go
stopped := make(chan struct{})
go func() {
    grpcServer.GracefulStop()
    close(stopped)
}()

select {
case <-stopped:
case <-time.After(10 * time.Second):
    grpcServer.Stop()
}

TLS

Use TLS (or mTLS) in production; avoid insecure credentials outside local dev.

Correct: server TLS

go
creds, err := credentials.NewServerTLSFromFile("server.crt", "server.key")
if err != nil { return err }

grpcServer := grpc.NewServer(grpc.Creds(creds))

Testing (bufconn)

Test gRPC handlers without opening real sockets using bufconn.

Correct: in-memory gRPC test server

go
const bufSize = 1024 * 1024

lis := bufconn.Listen(bufSize)
srv := grpc.NewServer()
usersv1.RegisterUsersServiceServer(srv, &Service{Repo: repo})

go func() { _ = srv.Serve(lis) }()

ctx := context.Background()
conn, err := grpc.DialContext(
    ctx,
    "bufnet",
    grpc.WithContextDialer(func(context.Context, string) (net.Conn, error) { return lis.Dial() }),
    grpc.WithTransportCredentials(insecure.NewCredentials()),
)
if err != nil { t.Fatal(err) }
defer conn.Close()

client := usersv1.NewUsersServiceClient(conn)
resp, err := client.GetUser(ctx, &usersv1.GetUserRequest{Id: "1"})
_ = resp
_ = err

Anti-Patterns

  • Ignore deadlines: unbounded handlers cause tail latency and resource exhaustion.

  • Return string errors: map domain errors to codes.* with status.Error or status.Errorf.

  • Stream without backpressure: stop on ctx.Done() and handle Send errors.

  • Expose reflection in production: treat reflection as a discovery surface.

Troubleshooting

Symptom: clients see UNKNOWN errors

Actions:

  • Return status.Error(codes.X, "...") instead of raw errors.
  • Wrap domain errors into typed errors, then map to gRPC codes.

Symptom: slow/hanging requests

Actions:

  • Require deadlines and propagate ctx to downstream calls.
  • Add server-side timeouts and bounded concurrency in repositories.

Symptom: flaky streaming

Actions:

  • Stop streaming on ctx.Done() and handle stream.Send errors.
  • Avoid buffering entire result sets before sending.

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Golang Grpc AI skill do?

Production gRPC in Go: protobuf layout, codegen, interceptors, deadlines, error codes, streaming, health checks, TLS, and testing with bufconn

Why use Golang Grpc on TypingMind?

Because you install it once and use it with any model. Golang Grpc is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Golang Grpc in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bobmatnyc/claude-mpm-skills/tree/main/toolchains/golang/golang-grpc. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Golang Grpc?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Golang Grpc?

As many as you like. As long as a model supports skills, you can use Golang Grpc with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Golang Grpc AI skill free?

Yes. It is published on GitHub by bobmatnyc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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