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sqlite-optimizationSQLite 优化

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

364

周安装

15

GitHub Stars

1

下载量

119
CodexClaudeCursorGemini CLI

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

unknown

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:sqlite-optimization(SQLite 优化)
来源仓库:https://github.com/jrajasekera/claude-skills
仓库路径:skills/sqlite-optimization
安装命令:
npx skills add https://github.com/jrajasekera/claude-skills --skill sqlite-optimization
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/jrajasekera/claude-skills --skill sqlite-optimization

简介

用于 SQLite 数据库的性能优化与查询调优。

  • 适合分析慢查询、优化索引策略或调整 schema 设计。
  • 需提供数据库文件和目标表信息后调用。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 建议在非高峰时段执行耗时较长的优化任务。
  • sqlite-optimization 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

SQLite Performance Optimization

Quick Start: Baseline Configuration

Set these PRAGMAs on every connection for most applications:

PRAGMA journal_mode = WAL;          -- Non-blocking concurrent reads/writes
PRAGMA synchronous = NORMAL;        -- Fast commits, safe for WAL mode
PRAGMA foreign_keys = ON;           -- Data integrity
PRAGMA cache_size = -64000;         -- 64MB cache (negative = KB)
PRAGMA temp_store = MEMORY;         -- In-memory temp tables/sorting
PRAGMA busy_timeout = 5000;         -- 5s retry on lock contention
PRAGMA mmap_size = 2147483648;      -- 2GB memory-mapped I/O (benchmark first)

Run periodically:

PRAGMA optimize;  -- Update query planner statistics

Optimization Workflow

1. Diagnose with EXPLAIN QUERY PLAN

EXPLAIN QUERY PLAN SELECT ...;

Key indicators:

  • SCAN TABLE → Full table scan (O(N)) — needs index or query rewrite
  • SEARCH TABLE USING INDEX → Index lookup (O(log N)) — good
  • COVERING INDEX → Index-only scan, no table lookup — optimal
  • USE TEMP B-TREE → Sorting not covered by index — consider index with ORDER BY columns

2. Schema Design

Use INTEGER PRIMARY KEY for rowid alias:

CREATE TABLE users (
    id INTEGER PRIMARY KEY,  -- Alias for rowid, no extra index
    email TEXT NOT NULL,
    created_at TEXT
);

Choose appropriate types:

  • INTEGER for IDs, counters, booleans
  • REAL for floats
  • TEXT for strings
  • Avoid large BLOBs in hot tables; use separate table or external files

WITHOUT ROWID for junction tables:

CREATE TABLE user_roles (
    user_id INTEGER,
    role_id INTEGER,
    PRIMARY KEY (user_id, role_id)
) WITHOUT ROWID;  -- Clustered index, no rowid overhead

3. Indexing Strategy

Create indexes for real query patterns:

-- For: WHERE user_id = ? AND created_at >= ? ORDER BY created_at DESC
CREATE INDEX idx_orders_user_created
ON orders (user_id, created_at DESC);

Covering indexes eliminate table lookups:

-- For: SELECT user_id, total FROM orders WHERE user_id = ?
CREATE INDEX idx_orders_cover
ON orders (user_id, created_at DESC, total);

Partial indexes for subsets:

CREATE INDEX idx_active_users
ON users (email) WHERE status = 'active';

Expression indexes:

CREATE INDEX idx_users_lower_email
ON users (lower(email));

Avoid indexing:

  • Small tables (< hundreds of rows)
  • Low-cardinality columns alone (booleans)
  • Columns rarely in WHERE/JOIN/ORDER BY

4. Write Optimization

Always batch writes in transactions:

BEGIN TRANSACTION;
INSERT INTO logs (...) VALUES (...);
-- ... many more inserts
COMMIT;

Throughput jumps from ~50 inserts/sec (autocommit) to 50,000+ inserts/sec.

Use prepared statements:

stmt = conn.prepare("INSERT INTO logs (ts, level, msg) VALUES (?, ?, ?)")
for row in data:
    stmt.execute(row)

Optimal batch size: 1,000–10,000 rows balances throughput vs memory.

5. Concurrency Configuration

WAL mode enables concurrent readers + one writer:

  • Readers don't block writers
  • Writers don't block readers
  • Uses .wal and .shm files alongside main DB

Handle contention:

PRAGMA busy_timeout = 5000;  -- Retry for 5 seconds before SQLITE_BUSY

Architecture pattern: Single writer thread/process + many reader connections.

6. Maintenance

VACUUM after large deletes:

VACUUM;  -- Rebuilds and defragments DB file
  • Run during maintenance windows (blocking operation)
  • Requires free disk space ≈ DB size

Auto-vacuum for incremental reclaim:

PRAGMA auto_vacuum = INCREMENTAL;
VACUUM;  -- Apply change
-- Then periodically:
PRAGMA incremental_vacuum;

Keep statistics fresh:

PRAGMA optimize;  -- Smart, selective ANALYZE
-- Or full:
ANALYZE;

Reference Files

Common Anti-Patterns

ProblemSolution
SELECT *List only needed columns
Loop with single insertsBatch in transaction
Correlated subquery per rowRewrite as JOIN
Index on every columnIndex only queried columns
No busy_timeoutSet appropriate timeout
Rollback journal modeSwitch to WAL
Never running ANALYZERun PRAGMA optimize periodically

Environment-Specific Notes

Mobile (iOS/Android):

  • WAL + synchronous = NORMAL
  • Background maintenance jobs for VACUUM
  • Local storage only (not SD card)

Server/Desktop:

  • Centralize writes in single thread if possible
  • Set PRAGMAs immediately after connection open
  • Consider connection pooling with writer serialization

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

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

Codex

38.57%
按下载量换算46

Claude

29.08%
按下载量换算35

Cursor

20.43%
按下载量换算24

Gemini CLI

9.68%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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