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 skills/killvxk/pm-skills-zh/sql-queriesnpx skills add killvxk/pm-skills-zh --skill sql-queriesgit clone --depth 1 https://github.com/killvxk/pm-skills-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/skills/killvxk/pm-skills-zh/sql-queries)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/sql-queries"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/sql-queries.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 | $0.00073 | $0.00885 |
| Opus 5 | $0.00036 | $0.00443 |
| Sonnet 5 | $0.00015 | $0.00177 |
| Haiku 4.5 | $0.00007 | $0.00089 |
Grade A, and why
sql-queries 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 5d ago.
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
SQL 查询生成器
用途
将自然语言需求转化为经过优化的 SQL 查询，支持多种数据库平台。帮助产品经理、分析师和工程师准确生成查询语句，无需手动处理语法细节。
工作原理
第一步：理解数据库结构
- 如果你提供了结构文件（SQL、文档或结构图描述），我将读取并分析
- 提取表名、字段定义、数据类型及关联关系
- 识别主键、外键和索引策略
第二步：处理查询需求
- 明确你需要检索或分析的具体数据
- 确认 SQL 方言（BigQuery、PostgreSQL、MySQL、Snowflake 等）
- 询问额外要求（过滤条件、聚合方式、排序规则）
第三步：生成优化查询
- 编写充分利用数据库结构的高效 SQL
- 添加注释，解释复杂逻辑
- 针对大数据集提供性能优化建议
- 如有适用，提供替代实现方案
第四步：解释与测试
- 用通俗语言解释查询逻辑
- 建议如何测试或验证结果
- 提供性能优化技巧
- 如有需要，生成测试脚本或示例数据
使用示例
示例一：基于结构文件生成查询
上传你的 database_schema.sql 文件，并输入：
"生成一个查询，找出过去 30 天内注册且至少有 5 次活跃会话的用户"
示例二：基于结构描述生成查询
"我的数据库结构如下：Users 表（id, email, created_at），Sessions 表
（id, user_id, timestamp, duration）。生成一个查询，
统计 2026 年 1 月每位用户的平均会话时长。"
示例三：复杂分析查询
"创建一个 BigQuery 查询，按地区和客户等级分析营收，
包含同比增长率。"
核心能力
- 多方言支持：兼容 BigQuery、PostgreSQL、MySQL、Snowflake、SQL Server
- 文件读取：读取结构文件、SQL dump 及数据文档
- 查询优化：建议索引、分区及性能改进方案
- 解释说明：拆解查询逻辑，便于学习和文档化
- 测试支持：可生成测试查询和示例数据脚本
- 脚本执行：为你的数据库创建可执行的 SQL 脚本
最佳实践建议
- 提供背景信息：分享你的数据库结构或表结构
- 描述清晰：明确说明你需要的数据及过滤条件
- 注明数据库类型：指定你使用的 SQL 方言
- 说明限制条件：提及数据量、时间范围和性能要求
- 明确输出格式：如需特定格式的查询结果，请提前说明
输出格式
你将收到：
- SQL 查询：带注释的生产可用 SQL 代码
- 说明：查询的功能和运行逻辑
- 性能备注：优化建议和注意事项
- 测试脚本（如有需要）：示例数据和验证查询
延伸阅读
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.
- 5d ago First seen · 87 lines · 73 tokens per session scan A 2635dfeeb3e8
sql-queries is a skill published in the GitHub repository killvxk/pm-skills-zh (151 stars, last pushed 5mo ago), licensed MIT. It adds 73 tokens to every session and 885 once invoked, about $0.0004 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 2026-08-30.
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