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Agent Skill · 开发

database-documentation-gen(数据库文档生成)

用于辅助数据库表结构、查询语句、迁移脚本和数据维护任务。它适合让 Agent 分析 schema、编写 SQL、排查查询问题、整理索引或生成迁移建议。使用时需要明确数据库类型、连接环境和目标表,区分只读分析与写入变更;涉及删除、更新、迁移和批量导入时,应优先 dry-run、备份或事务保护,避免误操作。

database-documentation-gen 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 14,730。

总安装 667周安装 27GitHub Stars 2,089下载 210核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:database-documentation-gen(数据库文档生成)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/database-documentation-gen
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill database-documentation-gen
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill database-documentation-gen

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

通过元数据探查自动生成结构化的数据库文档与数据字典。支持 PostgreSQL 和 MySQL 的 schema 解析及关系图谱展示。输出 Markdown 格式报告,包含表结构、约束、索引和存储过程信息。要求具备数据库只读权限并能执行 CLI 查询命令。

database-documentation-gen 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/database-documentation-gen

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skill…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

database-documentation-gen 怎么安装?

在终端执行 npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

database-documentation-gen 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

database-documentation-gen 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

database-documentation-gen 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

database-documentation-gen 用的人多吗?

公开数据是累计安装 667 次,近一周安装 27 次,下载 210 次,GitHub 2,089 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

database-documentation-gen 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 2,089 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
database-documentation-gen

数据库文档生成 · 本页

6672,089接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Database Documentation Generator

Overview

Generate comprehensive database documentation by introspecting live PostgreSQL or MySQL schemas, extracting table structures, column descriptions, relationships, indexes, constraints, stored procedures, and views. Produces human-readable documentation in Markdown format including entity-relationship descriptions, data dictionary, and column-level metadata.

Prerequisites

  • Database credentials with read access to information_schema, pg_catalog (PostgreSQL), or system tables (MySQL)
  • psql or mysql CLI for executing introspection queries
  • Target output directory for generated documentation files
  • Existing column comments (COMMENT ON COLUMN) enhance output quality significantly
  • Knowledge of the business domain for meaningful table/column descriptions

Instructions

  1. Extract the complete table inventory: SELECT table_name, obj_description((table_schema || '.' || table_name)::regclass) AS table_comment FROM information_schema.tables WHERE table_schema = 'public' AND table_type = 'BASE TABLE' ORDER BY table_name (PostgreSQL). For MySQL: SELECT TABLE_NAME, TABLE_COMMENT FROM information_schema.TABLES WHERE TABLE_SCHEMA = DATABASE().
  2. For each table, extract column details: SELECT c.column_name, c.data_type, c.character_maximum_length, c.is_nullable, c.column_default, pgd.description AS column_comment FROM information_schema.columns c LEFT JOIN pg_catalog.pg_description pgd ON pgd.objsubid = c.ordinal_position AND pgd.objoid = (c.table_schema || '.' || c.table_name)::regclass WHERE c.table_name = 'target_table' ORDER BY c.ordinal_position.
  3. Extract primary key and unique constraint definitions: SELECT tc.constraint_name, tc.constraint_type, kcu.column_name FROM information_schema.table_constraints tc JOIN information_schema.key_column_usage kcu ON tc.constraint_name = kcu.constraint_name WHERE tc.table_name = 'target_table' AND tc.constraint_type IN ('PRIMARY KEY', 'UNIQUE').
  4. Extract foreign key relationships to build the relationship map: SELECT tc.table_name AS child_table, kcu.column_name AS child_column, ccu.table_name AS parent_table, ccu.column_name AS parent_column, rc.delete_rule, rc.update_rule FROM information_schema.table_constraints tc JOIN information_schema.key_column_usage kcu ON tc.constraint_name = kcu.constraint_name JOIN information_schema.referential_constraints rc ON tc.constraint_name = rc.constraint_name JOIN information_schema.constraint_column_usage ccu ON rc.unique_constraint_name = ccu.constraint_name WHERE tc.constraint_type = 'FOREIGN KEY'.
  5. Extract index definitions: SELECT indexname, indexdef FROM pg_indexes WHERE schemaname = 'public' ORDER BY tablename, indexname (PostgreSQL). For MySQL: SELECT TABLE_NAME, INDEX_NAME, COLUMN_NAME, NON_UNIQUE, SEQ_IN_INDEX FROM information_schema.STATISTICS WHERE TABLE_SCHEMA = DATABASE() ORDER BY TABLE_NAME, INDEX_NAME, SEQ_IN_INDEX.
  6. Extract views and their definitions: SELECT viewname, definition FROM pg_views WHERE schemaname = 'public'. Document each view with its purpose, source tables, and any filtering logic.
  7. Extract functions and stored procedures: SELECT routine_name, routine_type, data_type AS return_type FROM information_schema.routines WHERE routine_schema = 'public'. Include function signatures and parameter descriptions.
  8. Generate the data dictionary in Markdown format with one section per table containing: table description, column table (name, type, nullable, default, description), primary key, foreign keys with referenced table, indexes, and any check constraints.
  9. Generate an entity-relationship summary listing all relationships: parent_table (parent_column) -> child_table (child_column) with cardinality (one-to-many, many-to-many via junction tables).
  10. Generate table statistics for context: SELECT relname, n_live_tup AS row_count, pg_size_pretty(pg_total_relation_size(relid)) AS total_size FROM pg_stat_user_tables ORDER BY n_live_tup DESC. Include approximate row counts and table sizes in the documentation.

Output

  • Data dictionary (Markdown) with complete column-level documentation for every table
  • Entity-relationship description listing all foreign key relationships with cardinality
  • Index catalog documenting all indexes with their columns and purpose
  • View definitions with source table references and business logic descriptions
  • Schema statistics including table sizes, row counts, and index sizes

Error Handling

ErrorCauseSolution
Missing column commentsCOMMENT ON COLUMN not used in the databaseGenerate inferred descriptions based on column name patterns; flag columns needing manual description
Permission denied on pg_catalogRestricted database user without catalog accessRequest pg_read_all_settings role; or use pg_dump --schema-only as an alternative schema source
Large schema with 500+ tablesDocumentation generation takes too long or produces unmanageable outputGenerate per-schema or per-module documentation; create a table-of-contents index; filter to specific table prefixes
Custom types not resolvedPostgreSQL domain types or composite types not in standard introspectionQuery pg_type for custom type definitions; include type documentation in a separate section
Stale documentation after schema changeDocumentation not regenerated after migrationIntegrate documentation generation into CI/CD pipeline; run after migration step

Examples

Generating documentation for a 50-table e-commerce database: Introspect all tables in the public schema, producing a 200-line Markdown data dictionary. Each table section includes column descriptions derived from COMMENT ON COLUMN annotations, foreign key relationship arrows, and index listings. Junction tables are identified and documented as many-to-many relationships.

Creating onboarding documentation for a new team member: Generate schema documentation with table sizes and row counts to help new developers understand which tables are central (large, many relationships) and which are auxiliary (small, few references). The relationship map shows the core entity graph: users -> orders -> order_items -> products.

Audit-ready documentation for compliance: Generate documentation including all constraints, check rules, and default values for each column. Flag columns containing PII (matching patterns like email, phone, ssn, address) and document their data protection controls. Output includes timestamp of generation and database version.

Resources

平台分布

Codex

36.02%
按下载量换算76

Claude

31.06%
按下载量换算65

Cursor

20.5%
按下载量换算43

Gemini CLI

10.28%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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