go-performance

cxuu/golang-skills · updated Apr 8, 2026

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$npx skills add https://github.com/cxuu/golang-skills --skill go-performance
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summary

Performance-specific guidelines apply only to the hot path. Don't prematurely optimize—focus these patterns where they matter most.

skill.md

Go Performance Patterns

Available Scripts

  • scripts/bench-compare.sh — Runs Go benchmarks N times with optional baseline comparison via benchstat. Supports saving results for future comparison. Run bash scripts/bench-compare.sh --help for options.

Performance-specific guidelines apply only to the hot path. Don't prematurely optimize—focus these patterns where they matter most.


Prefer strconv over fmt

When converting primitives to/from strings, strconv is faster than fmt:

s := strconv.Itoa(rand.Int()) // ~2x faster than fmt.Sprint()
Approach Speed Allocations
fmt.Sprint 143 ns/op 2 allocs/op
strconv.Itoa 64.2 ns/op 1 allocs/op

Read references/STRING-OPTIMIZATION.md when choosing between strconv and fmt for type conversions, or for the full conversion table.


Avoid Repeated String-to-Byte Conversions

Convert a fixed string to []byte once outside the loop:

data := []byte("Hello world")
for i := 0; i < b.N; i++ {
    w.Write(data) // ~7x faster than []byte("...") each iteration
}

Read references/STRING-OPTIMIZATION.md when optimizing repeated byte conversions in hot loops.


Prefer Specifying Container Capacity

Specify container capacity where possible to allocate memory up front. This minimizes subsequent allocations from copying and resizing as elements are added.

Map Capacity Hints

Provide capacity hints when initializing maps with make():

m := make(map[string]os.DirEntry, len(files))

Note: Unlike slices, map capacity hints do not guarantee complete preemptive allocation—they approximate the number of hashmap buckets required.

Slice Capacity

Provide capacity hints when initializing slices with make(), particularly when appending:

data := make([]int, 0, size)

Unlike maps, slice capacity is not a hint—the compiler allocates exactly that much memory. Subsequent append() operations incur zero allocations until capacity is reached.

Approach Time (100M iterations)
No capacity 2.48s
With capacity 0.21s

The capacity version is ~12x faster due to zero reallocations during append.


Pass Values

Don't pass pointers as function arguments just to save a few bytes. If a function refers to its argument x only as *x throughout, then the argument shouldn't be a pointer.

func process(s string) { // not *string — strings are small fixed-size headers
    fmt.Println(s)
}

Common pass-by-value types: string, io.Reader, small structs.

Exceptions:

  • Large structs where copying is expensive
  • Small structs that might grow in the future

String Concatenation

Choose the right strategy based on complexity:

Method Best For
+ Few strings, simple concat
fmt.Sprintf Formatted output with mixed types
strings.Builder Loop/piecemeal construction
strings.Join Joining a slice
Backtick literal Constant multi-line text

Read references/STRING-OPTIMIZATION.md when choosing a string concatenation strategy, using strings.Builder in loops, or deciding between fmt.Sprintf and manual concatenation.


Benchmarking and Profiling

Always measure before and after optimizing. Use Go's built-in benchmark framework and profiling tools.

go test -bench=. -benchmem -count=10 ./...

Read references/BENCHMARKS.md when writing benchmarks, comparing results with benchstat, profiling with pprof, or interpreting benchmark output.

Validation: After applying optimizations, run bash scripts/bench-compare.sh to measure the actual impact. Only keep optimizations with measurable improvement.


Quick Reference

Pattern Bad Good Improvement
Int to string fmt.Sprint(n) strconv.Itoa(n) ~2x faster
Repeated []byte []byte("str") in loop Convert once outside ~7x faster
Map initialization make(map[K]V) make(map[K]V, size) Fewer allocs
Slice initialization make([]T, 0) make([]T, 0, cap) ~12x faster
Small fixed-size args *string, *io.Reader string, io.Reader No indirection
Simple string join s1 + " " + s2 (already good) Use + for few strings
Loop string build Repeated += strings.Builder O(n) vs O(n²)

Related Skills

  • Data structures: See go-data-structures when choosing between slices, maps, and arrays, or understanding allocation semantics
  • Declaration patterns: See go-declarations when using make with capacity hints or initializing maps and slices
  • Concurrency: See go-concurrency when parallelizing work across goroutines or using sync.Pool for buffer reuse
  • Style principles: See go-style-core when deciding whether an optimization is worth the readability cost
how to use go-performance

How to use go-performance on Cursor

AI-first code editor with Composer

1

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 go-performance
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/cxuu/golang-skills --skill go-performance

The skills CLI fetches go-performance from GitHub repository cxuu/golang-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/go-performance

Reload or restart Cursor to activate go-performance. Access the skill through slash commands (e.g., /go-performance) 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

GET_STARTED →

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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.847 reviews
  • Ganesh Mohane· Dec 24, 2024

    go-performance reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Carlos Martin· Dec 12, 2024

    We added go-performance from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Maya Rahman· Dec 12, 2024

    Useful defaults in go-performance — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Amelia Thompson· Dec 4, 2024

    I recommend go-performance for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Amelia Tandon· Nov 23, 2024

    go-performance reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sakshi Patil· Nov 15, 2024

    I recommend go-performance for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ira Zhang· Oct 22, 2024

    go-performance reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Dev Ramirez· Oct 14, 2024

    Registry listing for go-performance matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Chaitanya Patil· Oct 6, 2024

    Useful defaults in go-performance — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Piyush G· Sep 25, 2024

    go-performance is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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