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kv-database-designerkv 数据库设计器

Agent Skill

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。

总安装

2,611

周安装

111

GitHub Stars

公开资料未说明

下载量

915
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:kv-database-designer(kv 数据库设计器)
来源仓库:https://github.com/felix-antonio-sl/kv-database-designer
安装命令:
openclaw skills install kv-database-designer
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install kv-database-designer

简介

键值数据库模式设计与优化工具,支持Schema分析、索引建议与迁移规划。

  • 适合Redis、DynamoDB等NoSQL数据库的结构演进与性能调优。
  • 可生成DDL语句、TTL策略与分区方案,降低运维复杂度。
  • 需明确数据类型、访问模式与一致性要求,避免过度设计。
  • 生产环境变更前应做影子测试,确保不影响在线服务稳定性。

SKILL.md

name
database-designer
description
Design, analyze, optimize and evolve database schemas. Use when the user asks to design schemas from scratch, choose between SQL and NoSQL engines, analyze or fix an existing schema (normalization, naming, constraints), plan index strategy, generate migration scripts, or interpret EXPLAIN plans.
compatibility
Requires Python 3.8+ for scripts in scripts/.

Database Designer

Convert a design request or existing schema into a structured, validated, production-ready artifact.

Activation

Use this skill when the user asks to:

  • design or review a database schema (DDL, ERD, Prisma, JSON Schema)
  • choose a database engine or decide SQL vs NoSQL
  • analyze normalization, constraints, naming or data types
  • plan an indexing strategy or diagnose slow queries
  • generate migration scripts with rollback
  • interpret an EXPLAIN plan or diagnose N+1 patterns

Workflow

  1. Classify the request: design | analyze | index | migrate | query | select-engine.
  2. Load the relevant reference for that mode:

- design / analyze{baseDir}/references/database-design-reference.md - normalization questions → {baseDir}/references/normalization_guide.md - indexing / EXPLAIN → {baseDir}/references/index_strategy_patterns.md - engine selection → {baseDir}/references/database_selection_decision_tree.md

  1. Run the appropriate script when the user provides a schema or queries:
   # Analyze schema for issues (normalization, constraints, naming, types)
   python {baseDir}/scripts/schema_analyzer.py --schema=<ddl_file>

   # Suggest index improvements for a query workload
   python {baseDir}/scripts/index_optimizer.py --schema=<ddl_file> --queries=<queries_file>

   # Generate up/down migration scripts
   python {baseDir}/scripts/migration_generator.py --before=<old_schema> --after=<new_schema>
  1. Formalize the design: objects, relationships, constraints, normalization level, primary/foreign keys.
  2. Emit the artifact: DDL, Mermaid ERD, Prisma schema, or JSON Schema — one format unless the user asks for multiple.
  3. Declare trade-offs: note any denormalization choices, missing constraints, or engine-specific limitations.

Output Contract

  • Open with the dominant design decision or issue found.
  • Emit one primary artifact (DDL, ERD, or schema) per response.
  • Annotate non-obvious choices (e.g. why a partial index, why a surrogate key).
  • Declare Information Loss when the target format cannot express a constraint (e.g. CHECK logic in Prisma).
  • Close with indexing recommendations and next migration step if applicable.

Key Rules

  • Default to PostgreSQL unless the user specifies another engine or the decision tree points elsewhere.
  • Default to 3NF for new schemas; document any intentional deviation and the reason.
  • Every generated migration must include a down script.
  • For destructive changes (DROP COLUMN with data), always recommend a logical backup first.
  • Do not generate application-layer connection pool code unless explicitly asked — reference the patterns instead.

Guardrails

  • Do not skip normalization analysis when designing from scratch.
  • Do not recommend over-indexing — flag indexes that may hurt write throughput.
  • Flag N+1 patterns but do not generate ORM-specific code unless the user's stack is known.
  • Stay within schema, index, migration and query scope; for large-scale cross-engine migrations refer to migration-architect.

Self Check

Before emitting any artifact, verify:

  • all foreign keys are declared;
  • primary key strategy is explicit (surrogate vs natural);
  • normalization level is stated and intentional deviations justified;
  • migration script has a down counterpart;
  • no inline example code duplicates what a reference file already covers.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

72.18%
按下载量换算660

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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