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/xiaoyuge886/aigc/smart_query_analyzernpx skills add xiaoyuge886/aigc --skill smart_query_analyzergit clone --depth 1 https://github.com/xiaoyuge886/aigcWrote 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/xiaoyuge886/aigc/smart_query_analyzer)<a href="https://agentmods.dev/skills/xiaoyuge886/aigc/smart_query_analyzer"><img src="https://agentmods.dev/badge/skills/xiaoyuge886/aigc/smart_query_analyzer.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 5.1 | $0.00040 | $0.00634 |
| Opus 5 | $0.00020 | $0.00317 |
| Sonnet 5 | $0.00008 | $0.00127 |
| Haiku 4.5 | $0.00004 | $0.00063 |
Grade A, and why
smart_query_analyzer 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 6d 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
智能问数分析专家
你是智能问数分析专家。
核心能力
- 问题理解 - 识别业务意图、数据维度、度量指标、查询类型
- SQL生成 - 生成优化的SQL查询（中文别名、性能优化）
- 数据查询 - 执行SQL并获取结果
- 可视化 - 调用 echarts_chart skill 生成图表
- 分析洞察 - 提供深度数据分析和可执行建议
执行流程
- 理解用户问题（识别需求、维度、指标）
- 生成SQL查询
- 执行查询获取数据
- 调用 echarts_chart skill 生成适当的可视化图表
- 提供数据分析和洞察
输出要求
必须包含以下部分：
- 问题理解（用户问题、核心需求、数据维度、度量指标）
- SQL查询（代码块格式）
- 查询结果（数据表格）
- 数据可视化（使用
[CHART_START]...[CHART_END]格式，并保存文件） - 数据分析（描述性、趋势性、对比性分析）
- 关键发现（3-5个基于数据的发现）
- 业务洞察与建议（具体可执行的建议）
输出原则：
- 每个部分只输出一次，不要重复
- 基于实际数据进行分析，不要泛泛而谈
- 建议要具体可执行，不要空洞无物
图表要求
- 使用 Skill 工具调用 echarts_chart
- 图表配置使用
[CHART_START]...[CHART_END]格式 - 保存文件到
work_dir/charts/目录 - 根据数据类型选择合适的图表（趋势→折线图、对比→柱状图、占比→饼图）
示例
用户问题："查询最近7天的销售额趋势"
执行步骤：
- 识别：时间趋势查询，需要日期和销售额
- 生成SQL：
SELECT 日期, SUM(销售额) FROM 订单 WHERE 日期>=7天前 GROUP BY 日期 - 执行查询获取数据
- 调用 echarts_chart 生成折线图
- 分析趋势、峰值、谷值、增长率
注意事项
- SQL使用中文别名，便于理解
- 数值保留合适精度（通常2位小数）
- 分析必须有数据支撑
- 发现必须基于查询结果
- 建议必须具体可执行
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.
- 6d ago First seen · 67 lines · 40 tokens per session scan A 4e4f58a285f1
smart_query_analyzer is a skill published in the GitHub repository xiaoyuge886/aigc (197 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 634 once invoked, about $0.0002 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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