Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/holtwood/awesome-cursorrules-zh/database-algorithm-rulesgit clone --depth 1 https://github.com/holtwood/awesome-cursorrules-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/holtwood/awesome-cursorrules-zh/database-algorithm-rules)<a href="https://agentmods.dev/rules/holtwood/awesome-cursorrules-zh/database-algorithm-rules"><img src="https://agentmods.dev/badge/rules/holtwood/awesome-cursorrules-zh/database-algorithm-rules.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00003 | $0.00738 |
| Opus 5 | $0.00002 | $0.00369 |
| Sonnet 5 | $0.00001 | $0.00148 |
| Haiku 4.5 | $0.00000 | $0.00074 |
Grade A, and why
database-algorithm-rules scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
数据库算法规则
本规则集定义了在 Python 容器化应用中与数据库交互时应遵循的算法和设计原则，旨在确保数据操作的效率、可靠性和可扩展性。
1. 数据模型设计
- 范式化与反范式化: 根据业务需求和查询模式，权衡数据库范式化（减少数据冗余）和反范式化（优化读取性能）的程度。
- 索引策略: 为频繁查询的列创建合适的索引，包括单列索引、复合索引和全文索引。避免过度索引。
- 数据类型选择: 选择最合适的数据类型，以优化存储空间和查询性能。
2. 查询优化
- 避免 N+1 查询: 在 ORM 中使用
select_related或prefetch_related来减少数据库查询次数。 - 批量操作: 对于大量数据的插入、更新或删除，使用批量操作而非逐条操作，减少数据库往返次数。
- 分页查询: 对于大数据集，始终使用分页查询，避免一次性加载所有数据导致内存溢出和性能下降。
- 避免全表扫描: 优化查询语句，确保能够利用索引，避免不必要的全表扫描。
3. 事务管理
- 原子性: 确保一组相关的数据库操作作为一个原子单元执行，要么全部成功，要么全部失败。
- 隔离级别: 根据业务需求选择合适的事务隔离级别，以平衡数据一致性和并发性能。
- 短事务: 尽量保持事务的简短，减少锁的持有时间，提高并发性。
4. 连接管理
- 连接池: 使用数据库连接池来管理数据库连接，避免频繁地建立和关闭连接，提高效率。
- 连接复用: 尽可能复用现有连接，减少资源消耗。
5. 缓存策略
- 读写分离: 对于读多写少的应用，可以考虑读写分离，将读请求分发到只读副本。
- 应用层缓存: 缓存频繁访问的、不经常变动的数据到应用内存或 Redis 等缓存系统中，减少数据库负载。
- 缓存失效策略: 设计合理的缓存失效策略（如 LRU、TTL、写穿透、写回），确保数据一致性。
6. 错误处理与重试
- 数据库连接错误: 妥善处理数据库连接中断、超时等错误。
- 幂等性: 设计数据库操作为幂等的，以便在网络波动或服务重启时可以安全地重试。
- 指数退避: 对于可重试的错误，采用指数退避策略进行重试。
7. 数据库迁移
- 版本控制: 使用数据库迁移工具（如 Alembic, Django Migrations）来管理数据库 schema 的版本控制和变更。
- 自动化: 自动化数据库迁移过程，确保开发、测试和生产环境的数据库结构一致。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 51 lines · 3 tokens per session scan A a324b5662210
database-algorithm-rules is a cursor rule published in the GitHub repository holtwood/awesome-cursorrules-zh (232 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 738 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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