Go's database ecosystem provides multiple layers of abstraction for SQL database integration. From the standard library's database/sql to enhanced libraries like sqlx and PostgreSQL-optimized pgx, developers can choose the right tool for their performance and ergonomics needs.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongolang-database-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches golang-database-patterns from bobmatnyc/claude-mpm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate golang-database-patterns. Access via /golang-database-patterns in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
29
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
29
stars
Go's database ecosystem provides multiple layers of abstraction for SQL database integration. From the standard library's database/sql to enhanced libraries like sqlx and PostgreSQL-optimized pgx, developers can choose the right tool for their performance and ergonomics needs.
Key Features:
Activate this skill when:
┌─────────────────────────────────────┐
│ What database are you using? │
└──────────────┬──────────────────────┘
│
┌──────────┴──────────┐
│ │
PostgreSQL Other SQL DB
│ │
▼ ▼
┌─────────────────┐ Use database/sql
│ Need max perf? │ + sqlx for convenience
└─────┬───────────┘
│
┌──┴──┐
Yes No
│ │
pgx sqlx + pq driver
Use database/sql when:
Use sqlx when:
Use pgx when:
Core Concepts:
package main
import (
"context"
"database/sql"
"time"
_ "github.com/lib/pq" // PostgreSQL driver
)
func setupDB(dsn string) (*sql.DB, error) {
db, err := sql.Open("postgres", dsn)
if err != nil {
return nil, err
}
// Connection pooling configuration
db.SetMaxOpenConns(25) // Max open connections
db.SetMaxIdleConns(5) // Max idle connections
db.SetConnMaxLifetime(5 * time.Minute) // Max connection lifetime
db.SetConnMaxIdleTime(1 * time.Minute) // Max idle time
// Verify connection
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()
if err := db.PingContext(ctx); err != nil {
return nil, err
}
return db, nil
}
Key Patterns:
// Query single row
func GetUserByID(ctx context.Context, db *sql.DB, id int) (*User, error) {
var user User
query := `SELECT id, name, email, created_at FROM users WHERE id = $1`
err := db.QueryRowContext(ctx, query, id).Scan(
&user.ID, &user.Name, &user.Email, &user.CreatedAt,
)
if err == sql.ErrNoRows {
return nil, ErrUserNotFound // Custom error
}
if err != nil {
return nil, fmt.Errorf("query user: %w", err)
}
return &user, nil
}
// Query multiple rows
func ListActiveUsers(ctx context.Context, db *sql.DB) ([]User, error) {
query := `SELECT id, name, email, created_at FROM users WHERE active = true`
rows, err := db.QueryContext(ctx, query)
if err != nil {
return nil, fmt.Errorf("query users: %w", err)
}
defer rows.Close() // CRITICAL: Always close rows
var users []User
for rows.Next() {
var user User
if err := rows.Scan(&user.ID, &user.Name, &user.Email, &user.CreatedAt); err != nil {
return nil, fmt.Errorf("scan user: %w", err)
}
users = append(users, user)
}
// Check for errors during iteration
if err := rows.Err(); err != nil {
return nil, fmt.Errorf("iterate users: %w", err)
}
return users, nil
}
Installation:
go get github.com/jmoiron/sqlx
Core Features:
package main
import (
"context"
"github.com/jmoiron/sqlx"
_ "github.com/lib/pq"
)
type User struct {
ID int `db:"id"`
Name string `db:"name"`
Email string `db:"email"`
CreatedAt time.Time `db:"created_at"`
}
// Get single struct
func GetUserByID(ctx context.Context, db *sqlx.DB, id int) (*User, error) {
var user User
query := `SELECT id, name, email, created_at FROM users WHERE id = $1`
err := db.GetContext(ctx, &user, query, id)
if err == sql.ErrNoRows {
return nil, ErrUserNotFound
}
return &user, err
}
// Select multiple structs
func ListUsers(ctx context.Context, db *sqlx.DB, limit int) ([]User, error)Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
tomlord1122/tomtom-skill
samber/cc-skills-golang
giuseppe-trisciuoglio/developer-kit
jwynia/agent-skills
mindrally/skills
github/awesome-copilot
Registry listing for golang-database-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend golang-database-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: golang-database-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
golang-database-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
golang-database-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added golang-database-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
golang-database-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for golang-database-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in golang-database-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend golang-database-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 26