go-data-structures

cxuu/golang-skills · updated Apr 8, 2026

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

When this skill does NOT apply: For concurrent access to data structures (mutexes, atomic operations), see go-concurrency. For defensive copying at API boundaries, see go-defensive. For pre-sizing capacity for performance, see go-performance.

skill.md

Go Data Structures


Choosing a Data Structure

What do you need?
├─ Ordered collection of items
│  ├─ Fixed size known at compile time → Array [N]T
│  └─ Dynamic size → Slice []T
│     ├─ Know approximate size? → make([]T, 0, capacity)
│     └─ Unknown size or nil-safe for JSON? → var s []T (nil)
├─ Key-value lookup
│  └─ Map map[K]V
│     ├─ Know approximate size? → make(map[K]V, capacity)
│     └─ Need a set? → map[T]struct{} (zero-size values)
└─ Need to pass to a function?
   └─ Copy at the boundary if the caller might mutate it

When this skill does NOT apply: For concurrent access to data structures (mutexes, atomic operations), see go-concurrency. For defensive copying at API boundaries, see go-defensive. For pre-sizing capacity for performance, see go-performance.


Slices

The append Function

Always assign the result — the underlying array may change:

x := []int{1, 2, 3}
x = append(x, 4, 5, 6)

// Append a slice to a slice
x = append(x, y...)  // Note the ...

Two-Dimensional Slices

Independent inner slices (can grow/shrink independently):

picture := make([][]uint8, YSize)
for i := range picture {
    picture[i] = make([]uint8, XSize)
}

Single allocation (more efficient for fixed sizes):

picture := make([][]uint8, YSize)
pixels := make([]uint8, XSize*YSize)
for i := range picture {
    picture[i], pixels = pixels[:XSize], pixels[XSize:]
}

Read references/SLICES.md when debugging unexpected slice behavior, sharing slices across goroutines, or working with slice headers.

Declaring Empty Slices

Prefer nil slices over empty literals:

// Good: nil slice
var t []string

// Avoid: non-nil but zero-length
t := []string{}

Both have len and cap of zero, but the nil slice is the preferred style.

Exception for JSON: A nil slice encodes to null, while []string{} encodes to []. Use non-nil when you need a JSON array.

When designing interfaces, avoid distinguishing between nil and non-nil zero-length slices.


Maps

Implementing a Set

Use map[T]bool — idiomatic and reads naturally:

attended := map[string]bool{"Ann": true, "Joe": true}
if attended[person] {  // false if not in map
    fmt.Println(person, "was at the meeting")
}

Copying

Be careful when copying a struct from another package. If the type has methods on its pointer type (*T), copying the value can cause aliasing bugs.

General rule: Do not copy a value of type T if its methods are associated with the pointer type *T. This applies to bytes.Buffer, sync.Mutex, sync.WaitGroup, and types containing them.

// Bad: copying a mutex
var mu sync.Mutex
mu2 := mu  // almost always a bug

// Good: pass by pointer
func increment(sc *SafeCounter) {
    sc.mu.Lock()
    sc.count++
    sc.mu.Unlock()
}

Quick Reference

Topic Key Point
Slices Always assign append result; nil slice preferred over []T{}
Sets map[T]bool is idiomatic
Copying Don't copy T if methods are on *T; beware aliasing

Related Skills

  • Defensive copying: See go-defensive when copying slices or maps at API boundaries to prevent mutation
  • Capacity hints: See go-performance when pre-sizing slices or maps for known workloads
  • Iteration patterns: See go-control-flow when using range loops over slices, maps, or channels
  • Declaration style: See go-declarations when choosing between new, make, var, and composite literals
how to use go-data-structures

How to use go-data-structures 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-data-structures
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-data-structures

The skills CLI fetches go-data-structures 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-data-structures

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

4.737 reviews
  • Shikha Mishra· Dec 24, 2024

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

  • Ganesh Mohane· Dec 20, 2024

    go-data-structures fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Amelia Robinson· Dec 16, 2024

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

  • James Chawla· Dec 4, 2024

    Keeps context tight: go-data-structures is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aanya Srinivasan· Nov 23, 2024

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

  • Yash Thakker· Nov 15, 2024

    go-data-structures has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Diya Kim· Nov 7, 2024

    Keeps context tight: go-data-structures is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aisha Nasser· Oct 26, 2024

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

  • Jin Brown· Oct 14, 2024

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

  • Dhruvi Jain· Oct 6, 2024

    Solid pick for teams standardizing on skills: go-data-structures is focused, and the summary matches what you get after install.

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