Borrowing it
Nothing to install: this file belongs to opsrobot-ai/opsrobot. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/opsrobot-ai/opsrobot/main/.cursor/skills/doris-datamodel-docgen/SKILL.mdgit clone --depth 1 https://github.com/opsrobot-ai/opsrobotWrote 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/opsrobot-ai/opsrobot/doris-datamodel-docgen)<a href="https://agentmods.dev/skills/opsrobot-ai/opsrobot/doris-datamodel-docgen"><img src="https://agentmods.dev/badge/skills/opsrobot-ai/opsrobot/doris-datamodel-docgen.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 | $0.00064 | $0.02367 |
| Opus 5 | $0.00032 | $0.01184 |
| Sonnet 5 | $0.00013 | $0.00473 |
| Haiku 4.5 | $0.00006 | $0.00237 |
Grade A, and why
数据模型文档生成 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 8d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doris 数据模型文档自动生成
前置条件
- 仓库根目录存在
.env，且可配置DORIS_HOST、DORIS_PORT、DORIS_USER、DORIS_PASSWORD；默认库见getCronDatabaseName()/getLogTablesDatabaseName()（--log-database）。 - 本机网络可连 Doris；已安装 Node（与项目一致）。
- 依赖：使用项目已有
mysql2，无需额外npm install。
表列 · 字段说明规则（重要）
- 数据库有 COMMENT：
SHOW FULL COLUMNS中的注释必须原样写入「说明」列，脚本不覆盖、不臆造。 - 数据库无 COMMENT：**由 Cursor Agent（大模型）**结合表数据，将「说明」补成准确业务描述（可整表重写该列，保持表格其它列不变）。
- Node 脚本角色（离线/CI 友好）：
- 默认
--description-source=hybrid：无 COMMENT 时，脚本用列名语义推断（高频表如agent_sessions有精确映射，其余由下划线词表组合中文）填「说明」；Agent 仍可在交付前按业务再润色。 --description-source=db-only：无 COMMENT 时「说明」固定为—，不写入启发式；适合「先出结构 + 样例，再全交给 Agent 写说明」的流程。
- 默认
推荐工作流：先 node scripts/generate-doris-datamodel-md.mjs …（hybrid 或 db-only）→ 在 Cursor 里 @ 生成的 md + 相关源码 → 请 Agent 仅优化「说明」列（有 COMMENT 的列勿改）。
自然语言 → 命令
| 意图 | 命令示例（均在仓库根执行） |
|---|---|
| 只生成一张表 | node scripts/generate-doris-datamodel-md.mjs --table=cron_runs 或 node scripts/generate-doris-datamodel-md.mjs cron_runs |
| 多张表 | node scripts/generate-doris-datamodel-md.mjs --tables=cron_jobs,cron_runs |
| 当前库全部表 | node scripts/generate-doris-datamodel-md.mjs --all-tables |
| 按表名前缀筛选 | node scripts/generate-doris-datamodel-md.mjs --all-tables --prefix=cron_ |
| 指定库名 | node scripts/generate-doris-datamodel-md.mjs --database=opsRobot --table=foo |
agent_sessions（日志库） |
node scripts/generate-doris-datamodel-md.mjs --log-database --table=agent_sessions --force |
提高采样上限（内置至少 100 行，与 --sample-rows 取较大值） |
node scripts/generate-doris-datamodel-md.mjs --table=cron_runs --sample-rows=200 --force |
| 仅表结构、不拉数据 | node scripts/generate-doris-datamodel-md.mjs --table=foo --no-json-sample |
| 说明列仅库注释 / 其余留「—」给 Agent | node scripts/generate-doris-datamodel-md.mjs --table=foo --description-source=db-only --force |
| 只打印不写盘（校对版式） | node scripts/generate-doris-datamodel-md.mjs --table=cron_runs --print 或 --dry-run |
| 覆盖已有文件 | 加上 --force |
| 大模型一键补概要 + 表列说明 + JSON 平铺说明 | node scripts/generate-doris-datamodel-md.mjs --table=agent_sessions --llm --force（需 API Key，见下） |
| 自定义 bundle 输出目录 | 同上并加 --llm-bundle-dir=D:\\tmp\\bundles |
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.
- 8d ago First seen · 89 lines · 64 tokens per session scan A 74454d514149
数据模型文档生成 is a skill published in the GitHub repository opsrobot-ai/opsrobot (135 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 2,367 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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