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/shanggqm/data-insight-agent/data-analysisnpx skills add shanggqm/data-insight-agent --skill data-analysisgit clone --depth 1 https://github.com/shanggqm/data-insight-agentWrote 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/shanggqm/data-insight-agent/data-analysis)<a href="https://agentmods.dev/skills/shanggqm/data-insight-agent/data-analysis"><img src="https://agentmods.dev/badge/skills/shanggqm/data-insight-agent/data-analysis.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 | $0.00056 | $0.01477 |
| Opus 5 | $0.00028 | $0.00739 |
| Sonnet 5 | $0.00011 | $0.00295 |
| Haiku 4.5 | $0.00006 | $0.00148 |
Grade A, and why
data-analysis 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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据分析技能
标准化数据分析工作流，确保分析高效、结果可靠、输出规范。
激活条件
- 用户需要查询数据库并分析数据
- 用户需要生成数据分析报告
- 涉及"数据分析"、"统计"、"报告"、"洞察"等关键词
核心约束
1. 时区处理
首次分析时确认数据库时区设定，常见情况：
| 存储时区 | 查询处理 | 展示处理 |
|---|---|---|
| UTC | 查询条件转 UTC | 结果转本地时区 |
| 本地时区 | 直接使用 | 直接展示 |
| 时间戳 | 按需转换 | 转本地时区 |
确认后记录到对应的 schema 文件中，后续分析直接参考。
2. 输出规范
{workspace}/analysis-output/{topic}_{YYYYMMDD_HHmmss}/
├── raw_data/ # 原始查询结果（仅复杂场景）
├── processed_data/ # 计算后的分析数据（JSON格式，供HTML读取）
│ └── charts_data.json
├── report.md # Markdown 报告
└── report.html # 可视化报告（幻灯片样式）
3. 数据处理原则
- 禁止将大量原始数据读入对话上下文
- 优先通过 SQL 聚合直接得出统计结果
- 仅当数据量大或查询复杂时，才落地本地用代码处理
工作流程
Step 1: 需求理解与 Schema 获取
- 识别分析主题、时间范围、关注维度
- 查看
schemas/是否有对应表结构，无则通过工具获取并保存 - 确定查询范围：哪些表、哪些字段、什么条件
Schema 文件: schemas/{source}_{name}.md
Step 2: 设计分析方案
根据数据特点选择分析维度（参考 references/analysis_perspectives.md）：
- 描述性分析：规模、分布、集中趋势、离散程度
- 时序分析：趋势、周期、同环比
- 对比分析：分群、分类、交叉对比
- 关联分析：相关性、因果推断
- 异常分析：离群值、突变点
输出简要分析方案后再执行查询。
Step 3: 数据查询与计算
可用工具参考 dbtool/README.md，根据用户指定或默认使用 MySQL。
策略选择
简单场景（优先）：直接用 SQL 聚合
-- 通过 GROUP BY、COUNT、SUM、AVG 等直接得出统计结果
-- 多个统计需求可并行发起多条查询
SELECT DATE(created_at) as date, COUNT(*) as count FROM table GROUP BY date;
复杂场景（降级）：当遇到以下情况时，才落地本地处理
- 查询超时
- 需要跨表复杂计算
- 数据量过大无法一次返回
- SQL 难以表达的统计逻辑
1. 查询原始数据保存到 raw_data/
2. 在输出目录编写 Node.js 脚本处理
3. 结果保存到 processed_data/charts_data.json
Step 4: 生成报告
Markdown 报告（参考 templates/report_template.md）：
- 执行摘要：核心发现 + 关键指标
- 详细分析：按维度展开
- 洞察建议：结论 + 行动项
- 附录：数据说明 + 计算口径
HTML 幻灯片报告（参考 templates/visualization_template.html）：
技术栈：
- Reveal.js：幻灯片框架，支持键盘翻页、过渡动画、演讲者模式（按 S 键）
- Tailwind CSS：原子化 CSS，直接用类名布局
- ECharts：图表引擎，暗金色主题
- Animate.css：元素入场动画（配合 fragment）
- Remix Icon：图标库
设计规范：
- 暗金色主题（#0f0f1a 背景 + #d4af37 金色）
- 每页一个观点 + 图表 + 洞察说明
- 数据必须从
processed_data/charts_data.json读取，禁止硬编码
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 172 lines · 56 tokens per session scan A fa70a3ab1580
data-analysis is a skill published in the GitHub repository shanggqm/data-insight-agent (5 stars, last pushed 8mo ago), licensed MIT. It adds 56 tokens to every session and 1,477 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 2026-08-31.
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