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/mateaix/mateclaw/sql_querynpx skills add mateaix/mateclaw --skill sql_querygit clone --depth 1 https://github.com/mateaix/mateclawWhat 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 | $0.00054 | $0.00807 |
| Opus 5 | $0.00027 | $0.00404 |
| Sonnet 5 | $0.00011 | $0.00161 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
sql_query 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
SQL 查询技能
当用户提出与数据查询相关的问题时，按照以下工作流程操作。
工作流程
第一步：发现数据源
调用 query_datasource(action='list_datasources') 查看所有可用的外部数据源。
- 如果只有一个数据源，直接使用它
- 如果有多个数据源，根据用户问题推断最相关的数据源；不确定时询问用户
第二步：发现表结构
调用 query_datasource(action='list_tables', datasourceId=<id>) 查看数据源中的表列表。
- 根据表名和注释判断与用户问题相关的表
- 不要一次查看所有表的结构，只查看相关的 2-3 张表
第三步：查看列详情
调用 query_datasource(action='describe_table', datasourceId=<id>, tableName='<表名>') 查看列名、类型和注释。
- 记住列名和类型，生成 SQL 时必须使用正确的列名
第四步：生成 SQL
根据表结构和用户问题，生成 SELECT SQL。遵循以下规则：
SQL 生成规则：
- 仅生成 SELECT 语句，绝不生成 INSERT/UPDATE/DELETE/DROP 等写操作
- 多表查询时，所有字段必须用表别名限定（如
t1.name） - 聚合查询使用 COUNT/SUM/AVG/MAX/MIN 等函数
- 时间过滤使用数据库对应的日期函数
- 字符串匹配使用 LIKE 并注意大小写
- 结果默认按合理的顺序排序（如时间倒序）
- 系统会自动注入 LIMIT 500，无需手动添加（除非用户指定了数量）
不同数据库的注意事项：
- MySQL：日期用
DATE_FORMAT()、NOW()，字符串连接用CONCAT() - PostgreSQL：日期用
TO_CHAR()、NOW()，字符串连接用|| - ClickHouse：日期用
toDate()、today()，注意不支持部分标准 SQL 语法
第五步：执行查询
调用 execute_sql(datasourceId=<id>, sql='<SQL>') 执行查询。
第六步：解读结果
- 用自然语言总结查询结果的要点
- 如果结果为空，分析可能的原因（表名/列名/条件有误等）
- 如果需要，可以调整 SQL 重新查询
- 如果查询结果包含数值列，系统会自动生成 ECharts 图表，无需你手动生成图表代码
- 重要：不要使用 write_file 工具生成 HTML 图表文件，系统已内置图表渲染能力
错误处理
如果 SQL 执行失败：
- 仔细阅读错误信息
- 常见原因：列名拼写错误、类型不匹配、语法错误
- 根据错误修正 SQL 并重试一次
- 如果仍然失败，向用户说明错误原因
安全须知
- 本工具仅支持只读查询（SELECT），写操作会被系统拒绝
- 查询结果默认限制为 500 行
- 查询超时为 30 秒
- 如果用户要求执行写操作，应礼貌拒绝并说明这是安全限制
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 · 72 lines · 54 tokens per session scan A 0311a56b075d
sql_query is a skill published in the GitHub repository mateaix/mateclaw (1,061 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 807 once invoked, about $0.0003 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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