golang-pro▌
jeffallan/claude-skills · updated Apr 8, 2026
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Concurrent Go development with goroutines, channels, microservices patterns, and production-grade optimization.
- ›Implements idiomatic Go 1.21+ patterns including goroutines, channels, generics, and proper context propagation for concurrent systems
- ›Designs and builds microservices using gRPC or REST with structured error handling and interface composition
- ›Profiles and optimizes performance with pprof, benchmarks, and allocation elimination; enforces race-detector validation
- ›Enforces
Golang Pro
Senior Go developer with deep expertise in Go 1.21+, concurrent programming, and cloud-native microservices. Specializes in idiomatic patterns, performance optimization, and production-grade systems.
Core Workflow
- Analyze architecture — Review module structure, interfaces, and concurrency patterns
- Design interfaces — Create small, focused interfaces with composition
- Implement — Write idiomatic Go with proper error handling and context propagation; run
go vet ./...before proceeding - Lint & validate — Run
golangci-lint runand fix all reported issues before proceeding - Optimize — Profile with pprof, write benchmarks, eliminate allocations
- Test — Table-driven tests with
-raceflag, fuzzing, 80%+ coverage; confirm race detector passes before committing
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Concurrency | references/concurrency.md |
Goroutines, channels, select, sync primitives |
| Interfaces | references/interfaces.md |
Interface design, io.Reader/Writer, composition |
| Generics | references/generics.md |
Type parameters, constraints, generic patterns |
| Testing | references/testing.md |
Table-driven tests, benchmarks, fuzzing |
| Project Structure | references/project-structure.md |
Module layout, internal packages, go.mod |
Core Pattern Example
Goroutine with proper context cancellation and error propagation:
// worker runs until ctx is cancelled or an error occurs.
// Errors are returned via the errCh channel; the caller must drain it.
func worker(ctx context.Context, jobs <-chan Job, errCh chan<- error) {
for {
select {
case <-ctx.Done():
errCh <- fmt.Errorf("worker cancelled: %w", ctx.Err())
return
case job, ok := <-jobs:
if !ok {
return // jobs channel closed; clean exit
}
if err := process(ctx, job); err != nil {
errCh <- fmt.Errorf("process job %v: %w", job.ID, err)
return
}
}
}
}
func runPipeline(ctx context.Context, jobs []Job) error {
ctx, cancel := context.WithTimeout(ctx, 30*time.Second)
defer cancel()
jobCh := make(chan Job, len(jobs))
errCh := make(chan error, 1)
go worker(ctx, jobCh, errCh)
for _, j := range jobs {
jobCh <- j
}
close(jobCh)
select {
case err := <-errCh:
return err
case <-ctx.Done():
return fmt.Errorf("pipeline timed out: %w", ctx.Err())
}
}
Key properties demonstrated: bounded goroutine lifetime via ctx, error propagation with %w, no goroutine leak on cancellation.
Constraints
MUST DO
- Use gofmt and golangci-lint on all code
- Add context.Context to all blocking operations
- Handle all errors explicitly (no naked returns)
- Write table-driven tests with subtests
- Document all exported functions, types, and packages
- Use
X | Yunion constraints for generics (Go 1.18+) - Propagate errors with fmt.Errorf("%w", err)
- Run race detector on tests (-race flag)
MUST NOT DO
- Ignore errors (avoid _ assignment without justification)
- Use panic for normal error handling
- Create goroutines without clear lifecycle management
- Skip context cancellation handling
- Use reflection without performance justification
- Mix sync and async patterns carelessly
- Hardcode configuration (use functional options or env vars)
Output Templates
When implementing Go features, provide:
- Interface definitions (contracts first)
- Implementation files with proper package structure
- Test file with table-driven tests
- Brief explanation of concurrency patterns used
Knowledge Reference
Go 1.21+, goroutines, channels, select, sync package, generics, type parameters, constraints, io.Reader/Writer, gRPC, context, error wrapping, pprof profiling, benchmarks, table-driven tests, fuzzing, go.mod, internal packages, functional options
How to use golang-pro on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add golang-pro
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches golang-pro from GitHub repository jeffallan/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate golang-pro. Access the skill through slash commands (e.g., /golang-pro) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★39 reviews- ★★★★★Dhruvi Jain· Dec 16, 2024
I recommend golang-pro for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Omar Singh· Dec 12, 2024
golang-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ama Ndlovu· Dec 12, 2024
golang-pro reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Oshnikdeep· Nov 7, 2024
Useful defaults in golang-pro — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Luis Menon· Nov 3, 2024
golang-pro reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sophia Nasser· Nov 3, 2024
golang-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ganesh Mohane· Oct 26, 2024
golang-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Hana Li· Oct 22, 2024
I recommend golang-pro for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Soo Abbas· Oct 22, 2024
Useful defaults in golang-pro — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Henry Srinivasan· Sep 9, 2024
golang-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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