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golang-databaseGo 数据库

Agent Skill

用于辅助数据库表结构、查询语句、迁移脚本和数据维护任务。它适合让 Agent 分析 schema、编写 SQL、排查查询问题、整理索引或生成迁移建议。使用时需要明确数据库类型、连接环境和目标表,区分只读分析与写入变更;涉及删除、更新、迁移和批量导入时,应优先 dry-run、备份或事务保护,避免误操作。

总安装

8,387

周安装

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下载量

2,937
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:golang-database(Go 数据库)
来源仓库:https://github.com/samber/golang-database
安装命令:
openclaw skills install golang-database
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install golang-database

简介

golang-database 提供 Go 数据库访问的综合指南和最佳实践。

  • 适合在 OpenClaw 中编写参数化查询和处理事务时使用。
  • 涵盖结构扫描、错误模式和连接池配置。
  • 安装命令:openclaw skills install golang-database。
  • 建议确认权限范围、维护状态及是否会触发数据库连接或 SQL 执行。

SKILL.md

name
golang-database
description
Comprehensive guide for Go database access. Covers parameterized queries, struct scanning, NULLable column handling, error patterns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use this skill whenever writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite. Also triggers for database testing or any question about database/sql, sqlx, pgx, or SQL queries in Golang. This skill explicitly does NOT generate database schemas or migration SQL.
user-invocable
false
license
MIT
compatibility
Designed for Claude Code or similar AI coding agents, and for projects using Golang.
metadata
author
samber
version
1.1.2
openclaw
emoji
🗄️
homepage
https://github.com/samber/cc-skills-golang
requires
bins
install
[]
allowed-tools
Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent AskUserQuestion

Persona: You are a Go backend engineer who writes safe, explicit, and observable database code. You treat SQL as a first-class language — no ORMs, no magic — and you catch data integrity issues at the boundary, not deep in the application.

Modes:

  • Write mode — generating new repository functions, query helpers, or transaction wrappers: follow the skill's sequential instructions; launch a background agent to grep for existing query patterns and naming conventions in the codebase before generating new code.
  • Review/debug mode — auditing or debugging existing database code: use a sub-agent to scan for missing rows.Close(), un-parameterized queries, missing context propagation, and absent error checks in parallel with reading the business logic.
Community default. A company skill that explicitly supersedes samber/cc-skills-golang@golang-database skill takes precedence.

Go Database Best Practices

Go's database/sql provides a solid foundation for database access. Use sqlx or pgx on top of it for ergonomics — never an ORM.

When using sqlx or pgx, refer to the library's official documentation and code examples for current API signatures.

Best Practices Summary

  1. Use sqlx or pgx, not ORMs — ORMs hide SQL, generate unpredictable queries, and make debugging harder
  2. Queries MUST use parameterized placeholders — NEVER concatenate user input into SQL strings
  3. Context MUST be passed to all database operations — use *Context method variants (QueryContext, ExecContext, GetContext)
  4. sql.ErrNoRows MUST be handled explicitly — distinguish "not found" from real errors using errors.Is
  5. Rows MUST be closed after iteration — defer rows.Close() immediately after QueryContext calls
  6. NEVER use db.Query for statements that don't return rows — Query returns *Rows which must be closed; if you forget, the connection leaks back to the pool. Use db.Exec instead
  7. Use transactions for multi-statement operations — wrap related writes in BeginTxx/Commit
  8. Use SELECT ... FOR UPDATE when reading data you intend to modify — prevents race conditions
  9. Set custom isolation levels when default READ COMMITTED is insufficient (e.g., serializable for financial operations)
  10. Handle NULLable columns with pointer fields (*string, *int) or sql.NullXxx types
  11. Connection pool MUST be configured — SetMaxOpenConns, SetMaxIdleConns, SetConnMaxLifetime, SetConnMaxIdleTime
  12. Use external tools for migrations — golang-migrate or Flyway, never hand-rolled or AI-generated migration SQL
  13. Batch operations in reasonable sizes — not row-by-row (too many round trips), not millions at once (locks and memory)
  14. Never create or modify database schemas — a schema that looks correct on toy data can create hotspots, lock contention, or missing indexes under real production load. Schema design requires understanding of data volumes, access patterns, and production constraints that AI does not have
  15. Avoid hidden SQL features — do not rely on triggers, views, materialized views, stored procedures, or row-level security in application code

Library Choice

LibraryBest forStruct scanningPostgreSQL-specific
database/sqlPortability, minimal depsManual ScanNo
sqlxMulti-database projectsStructScanNo
pgxPostgreSQL (30-50% faster)pgx.RowToStructByNameYes (COPY, LISTEN, arrays)
GORM/entAvoidMagicAbstracted away

Why NOT ORMs:

  • Unpredictable query generation — N+1 problems you cannot see in code
  • Magic hooks and callbacks (BeforeCreate, AfterUpdate) make debugging harder
  • Schema migrations coupled to application code
  • Learning the ORM API is harder than learning SQL, and the abstraction leaks

Parameterized Queries

// ✗ VERY BAD — SQL injection vulnerability
query := fmt.Sprintf("SELECT * FROM users WHERE email = '%s'", email)

// ✓ Good — parameterized (PostgreSQL)
var user User
err := db.GetContext(ctx, &user, "SELECT id, name, email FROM users WHERE email = $1", email)

// ✓ Good — parameterized (MySQL)
err := db.GetContext(ctx, &user, "SELECT id, name, email FROM users WHERE email = ?", email)

Dynamic IN clauses

query, args, err := sqlx.In("SELECT * FROM users WHERE id IN (?)", ids)
if err != nil {
    return fmt.Errorf("building IN clause: %w", err)
}
query = db.Rebind(query) // adjust placeholders for your driver
err = db.SelectContext(ctx, &users, query, args...)

Dynamic column names

Never interpolate column names from user input. Use an allowlist:

allowed := map[string]bool{"name": true, "email": true, "created_at": true}
if !allowed[sortCol] {
    return fmt.Errorf("invalid sort column: %s", sortCol)
}
query := fmt.Sprintf("SELECT id, name, email FROM users ORDER BY %s", sortCol)

For more injection prevention patterns, see the samber/cc-skills-golang@golang-security skill.

Struct Scanning and NULLable Columns

Use db:"column_name" tags for sqlx, pgx.CollectRows with pgx.RowToStructByName for pgx. Handle NULLable columns with pointer fields (*string, *time.Time) — they work cleanly with both scanning and JSON marshaling. See Scanning Reference for examples of all approaches.

Error Handling

func GetUser(id string) (*User, error) {
    var user User

    err := db.GetContext(ctx, &user, "SELECT id, name FROM users WHERE id = $1", id)
    if err != nil {
        if errors.Is(err, sql.ErrNoRows) {
            return nil, ErrUserNotFound // translate to domain error
        }
        return nil, fmt.Errorf("querying user %s: %w", id, err)
    }

    return &user, nil
}

or:

func GetUser(id string) (u *User, exists bool, err error) {
    var user User

    err := db.GetContext(ctx, &user, "SELECT id, name FROM users WHERE id = $1", id)
    if err != nil {
        if errors.Is(err, sql.ErrNoRows) {
            return nil, false, nil // "no user" is not a technical error, but a domain error
        }
        return nil, false, fmt.Errorf("querying user %s: %w", id, err)
    }

    return &user, true, nil
}

Always close rows

rows, err := db.QueryContext(ctx, "SELECT id, name FROM users")
if err != nil {
    return fmt.Errorf("querying users: %w", err)
}
defer rows.Close() // prevents connection leaks

for rows.Next() {
    // ...
}
if err := rows.Err(); err != nil { // always check after iteration
    return fmt.Errorf("iterating users: %w", err)
}

Common database error patterns

ErrorHow to detectAction
Row not founderrors.Is(err, sql.ErrNoRows)Return domain error
Unique constraintCheck driver-specific error codeReturn conflict error
Connection refusederr != nil on db.PingContextFail fast, log, retry with backoff
Serialization failurePostgreSQL error code 40001Retry the entire transaction
Context cancelederrors.Is(err, context.Canceled)Stop processing, propagate

Context Propagation

Always use the *Context method variants to propagate deadlines and cancellation:

// ✗ Bad — no context, query runs until completion even if client disconnects
db.Query("SELECT ...")

// ✓ Good — respects context cancellation and timeouts
db.QueryContext(ctx, "SELECT ...")

For context patterns in depth, see the samber/cc-skills-golang@golang-context skill.

Transactions, Isolation Levels, and Locking

For transaction patterns, isolation levels, SELECT FOR UPDATE, and locking variants, see Transactions.

Connection Pool

db.SetMaxOpenConns(25)              // limit total connections
db.SetMaxIdleConns(10)              // keep warm connections ready
db.SetConnMaxLifetime(5 * time.Minute)  // recycle stale connections
db.SetConnMaxIdleTime(1 * time.Minute)  // close idle connections faster

For sizing guidance and formulas, see Database Performance.

Migrations

Use an external migration tool. Schema changes require human review with understanding of data volumes, existing indexes, foreign keys, and production constraints.

Recommended tools:

  • golang-migrate — CLI + Go library, supports all major databases
  • Flyway — JVM-based, widely used in enterprise environments
  • Atlas — modern, declarative schema management

Migration SQL should be written and reviewed by humans, versioned in source control, and applied through CI/CD pipelines.

Avoid Hidden SQL Features

Do not rely on triggers, views, materialized views, stored procedures, or row-level security in application code — they create invisible side effects and make debugging impossible. Keep SQL explicit and visible in Go where it can be tested and version-controlled.

Schema Creation

This skill does NOT cover schema creation. AI-generated schemas are often subtly wrong — missing indexes, incorrect column types, bad normalization, or missing constraints. Schema design requires understanding data volumes, access patterns, query profiles, and business constraints. Use dedicated database tooling and human review.

Deep Dives

  • Transactions — Transaction boundaries, isolation levels, deadlock prevention, SELECT FOR UPDATE
  • Testing Database Code — Mock connections, integration tests with containers, fixtures, schema setup/teardown
  • Database Performance — Connection pool sizing, batch processing, indexing strategy, query optimization
  • Struct Scanning — Struct tags, NULLable column handling, JSON marshaling patterns

Cross-References

  • → See samber/cc-skills-golang@golang-security skill for SQL injection prevention patterns
  • → See samber/cc-skills-golang@golang-context skill for context propagation to database operations
  • → See samber/cc-skills-golang@golang-error-handling skill for database error wrapping patterns
  • → See samber/cc-skills-golang@golang-testing skill for database integration test patterns

References

适合场景

01

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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