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 skills add drtgryhf-svg/growth-compass --skill growth-compassgit clone --depth 1 https://github.com/drtgryhf-svg/growth-compassWrote 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/drtgryhf-svg/growth-compass/growth-compass)<a href="https://agentmods.dev/skills/drtgryhf-svg/growth-compass/growth-compass"><img src="https://agentmods.dev/badge/skills/drtgryhf-svg/growth-compass/growth-compass/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/drtgryhf-svg/growth-compass/growth-compass"><img src="https://agentmods.dev/badge/skills/drtgryhf-svg/growth-compass/growth-compass.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00198 | $0.03544 |
| Opus 5.5 | $0.00079 | $0.01418 |
| Sonnet 5.5 | $0.00040 | $0.00709 |
| Haiku 4.5 | $0.00020 | $0.00354 |
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 2mo 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GrowthCompass（增长罗盘）—— 业务数据分析（电商/增长）
帮用户从 MySQL 数据库或 CSV/Excel 文件中得出可信的业务结论：漏斗转化、用户留存、复购、RFM 分群、指标异动归因，最终交付一份"结论先行"的分析报告。
服务的很多用户是新手：他们可能不知道自己的数据长什么样、说不清指标口径、看不懂 SQL。你的职责不只是跑出数字，而是带他把问题拆开、把过程讲明白。
新手友好三原则
- 先问清，再动手。 每次分析从 Step 0 头脑风暴开始：一次性问完必要问题，答案确认后全自动执行到底。宁可多问一句，不要猜错方向白算一上午。
- 过程透明。 每条 SQL 先用一句话说"这条在算什么"再执行；贴出关键结果时解释数字的含义。用户能看懂，才敢信结论。
- 说人话。 结论用业务语言（"新客首单转化率只有 8%，主要卡在填地址这一步"），技术名词（窗口函数、cohort）第一次出现时用一句话解释。
安全红线（任何时候不可违反）
- 只读：分析场景一律不执行写操作。
scripts/db_query.py会硬性拦截 DROP/DELETE/UPDATE/INSERT/TRUNCATE/ALTER/CREATE/GRANT 等，绕过拦截必须用户在对话里明确确认，并在执行前复述将要做的事。 - 必加 LIMIT：探查性查询必须限制行数（脚本默认自动加 1000）。大表先在 SQL 里聚合，再拉取结果。
- 脱敏：手机号、邮箱、身份证、收货地址等个人信息不要原样输出。查询时只取统计结果，或用
LEFT(phone, 3)这类方式打码。
Step 0 头脑风暴：先问清，再全自动（每次分析前必做）
动手前的唯一一次提问环节。目的：把模糊的业务诉求变成明确的分析任务。要求一条消息里分组问完，不要挤牙膏式追问。必问三项 + 根据用户的问题类型挑 2~3 个场景问题。完整问题库和默认口径见 references/kickoff-questions.md。
| 组 | 问什么 | 示例问法 |
|---|---|---|
| 必问 · 数据 | 数据在哪、怎么访问 | "数据是 MySQL 还是文件？给我路径或连接方式" |
| 必问 · 范围 | 哪个时间段、哪条业务线 | "分析哪个时间段？全部渠道还是某几个？" |
| 必问 · 目标 | 这份分析服务于什么决策 | "分析结果给谁看、用来决定什么事？" |
| 场景 · 异动归因 | 指标口径 / 近期变动 / 对比基准 | "退款算不算 GMV？最近有没有发版、活动、改价？" |
| 场景 · 漏斗 | 步骤定义与时间窗口 | "漏斗有哪几步？多久内走完算通过？" |
| 场景 · 留存 | 留存口径 | "看活跃留存还是购买留存？按日还是按周？" |
| 场景 · 指标 | 有效订单口径 | "退款/未支付单算进 GMV 吗？" |
用户答不上来的口径，直接给行业默认定义让 ta 确认，不要反复追问（默认口径速查表在 references/kickoff-questions.md）。
全自动契约
头脑风暴的答案一旦确认，Step 1~6 全自动执行到底，中途不再提问：连库/读文件 → 体检 → 清洗 → EDA → 分析 → 出图 → 报告一气呵成。auto_clean.py 的"需人工确认"项按脚本默认规则处理并原样记录，不为此打断。仅有的两个例外：① 数据本身无法支撑需求（缺表、字段对不上、数据量异常）；② 触发安全红线需要用户确认。所有代替用户拍板的口径假设，集中写进报告的「口径与假设」一节，供事后复核修正。
标准工作流
Step 0 头脑风暴（一次问清，锁定口径）
↓ 此后全自动，不再打断
Step 1 取数 → Step 2 数据体检 → Step 3 清洗 → Step 4 业务分析 → Step 5 可视化 → Step 6 报告
不必步步死板执行，但顺序不可颠倒：没头脑风暴不开工，没体检过的数据不分析，没分析过的数据不画图。
Step 1 取数
MySQL：先读 references/mysql-connection.md。连接成功后先摸 schema，不急着分析：
SHOW TABLES; -- 有哪些表
SHOW CREATE TABLE orders\G -- 表结构、字段类型、索引
SELECT COUNT(*) FROM orders; -- 数据量级，决定后面怎么查
SELECT * FROM orders LIMIT 5; -- 每张表扫一眼真实数据长什么样
What ships with it
18 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.
- references/analysis-frameworks.md 5.2 KB
- references/data-cleaning.md 4.9 KB
- references/kickoff-questions.md 4.4 KB
- references/mysql-connection.md 3.7 KB
- references/report-template.md 2.9 KB
- references/sql-patterns.md 7.6 KB
- references/visualization.md 3.4 KB
- scripts/anomaly_detect.py 8.9 KB runs code
- scripts/auto_clean.py 15 KB runs code
- scripts/auto_eda.py 9.6 KB runs code
- scripts/auto_join.py 6.7 KB runs code
- scripts/cohort_retention.py 6.6 KB runs code
- scripts/data_profile.py 6.0 KB runs code
- scripts/db_query.py 7.8 KB runs code
- scripts/funnel.py 6.5 KB runs code
- scripts/make_demo_data.py 8.6 KB runs code
- scripts/metrics.py 7.2 KB runs code
- scripts/quick_plot.py 5.7 KB runs code
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.
- 2mo ago First seen · 185 lines · 198 tokens per session scan A 6298bb6bd781
data-analysis is a skill published in the GitHub repository drtgryhf-svg/growth-compass (1 stars, last pushed 2mo ago), licensed MIT. It adds 198 tokens to every session and 3,544 once invoked, about $0.0008 per session on Opus 5.5. 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.
data-analysis compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| data-analysisdrtgryhf-svg/growth-compass | Skill | 1 | 2mo ago | MIT | A | 198 | Claude Code, Codex |
| fetch-dataagentscope-ai/QwenPaw-Data | Skill | 190 | yesterday | Apache-2.0 | A | 276 | Claude Code, Codex |
| query-odpsagentscope-ai/QwenPaw-Data | Skill | 190 | yesterday | Apache-2.0 | A | 81 | Claude Code, Codex |
| setup-data-workspacedanielrosehill/Claude-Data-Analyst-plugin | Skill | 12 | 3d ago | MIT | A | 73 | Claude Code |
| type-consistency-sweepdanielrosehill/Claude-Data-Analyst-plugin | Skill | 12 | 3d ago | MIT | A | 81 | Claude Code |
| datasetsAryan-Jhaveri/mcp-statcan | Skill | 8 | 11d ago | MIT | A | 67 | Claude Code, Codex |
| alibabacloud-data-agent-skillaliyun/data-agent-skill | Skill | 57 | 5d ago | none | A | 159 | Claude Code, Codex |