🗄️
DataPython

Data Modeling

by nimadorostkar

Data Modeling is a Data skill for Claude Code, published by nimadorostkar in Claude-Skills-collection.

25 stars3 forkson nimadorostkar/Claude-Skills-collectionAdded 2026/08/12Repository updated 2026/07/26
View source on GitHubDownload repo ZIP
aiclaudeclaude-skillsskills
Install in seconds
Install Data Modeling
Copy Data Modeling into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/data/data-modeling ~/.claude/skills/data-modeling

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/nimadorostkar/Claude-Skills-collection.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/data/data-modeling/SKILL.md in nimadorostkar/Claude-Skills-collection
Installs to
~/.claude/skills/data-modeling
Collection
One of 50 skills cataloged from this repository
Category
Data — 668 skills

What Data Modeling does

Data Modeling helps design database schemas for new tables or major changes. It covers normalization, keys, temporal history, multi-tenancy, soft deletes, and constraints that are hard to reverse later.

Data Modeling is cataloged under Data on DirSkills. Data Modeling comes from a repository tagged ai, claude, claude-skills and skills.

Documentation

README

Data Modeling

Purpose

Design a schema that supports the queries you need and the changes you will want. Schema mistakes are the most expensive category of technical debt because the data outlives every application that touched it.

When to Use

  • Designing a new schema or a significant new table.
  • Adding multi-tenancy to an existing model.
  • Modeling history, versioning, or audit requirements.
  • Reviewing a schema before it becomes hard to change.

Capabilities

  • Normalization, and deliberate denormalization.
  • Key selection: natural, surrogate, UUID, ULID.
  • Temporal modeling: valid time, transaction time, event history.
  • Multi-tenancy strategies and their isolation guarantees.
  • Constraint design: the invariants the database should enforce.

Inputs

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Data Modeling

  • What else does nimadorostkar publish alongside Data Modeling?

    Data Modeling is one of 50 skills that DirSkills catalogs from nimadorostkar/Claude-Skills-collection, the repository it ships in. Its siblings there include API Design, Agent Design and Agent Instructions. Each one is a separate skill with its own page in this directory, installs the same way Data Modeling does, and is maintained by nimadorostkar in that same repository. The rest of the collection is listed on the nimadorostkar/Claude-Skills-collection page.

  • How does Data Modeling compare to other Data skills?

    Data Modeling ranks #644 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Data Modeling against them. Open each page to compare what they document and how they install.

More from nimadorostkar/Claude-Skills-collection

Data Modeling is one of 50 skills cataloged on DirSkills from nimadorostkar/Claude-Skills-collection.

See all 50 skills →
🔗
2026/07/16

API Design

Use when designing or reviewing an HTTP API. Covers resource modeling, status codes, pagination, idempotency, versioning, error formats, and the contract details that break clients when you get them wrong.
Quality
182
🤖
2026/07/16

Agent Design

Provides patterns for designing reliable LLM agents with tool use, including loop termination, error handling, human checkpoints, and recognizing when an agent isn't needed.
AI Engineering
182
🧭
2026/07/16

Agent Instructions

Write short, accurate instruction files for coding agents, including commands, conventions, and hard constraints. Use it when setting up or refreshing CLAUDE.md, AGENTS.md, or similar files.
AI Engineering
182
🧠
2026/07/16

Agent Memory

A method for agents to persist critical state—facts, failures, conclusions—in a file that survives context compaction and session ends, with strategies for retrieval and preventing stale data.
AI Engineering
182
🧾
3w ago

Architecture Decisions

Architecture Decisions records technical choices as ADRs with context, options, the decision, and consequences. Use it when a significant or hard-to-reverse decision needs to be documented for future maintainers.
Writing
253
⏳
2026/07/16

Background Jobs

Use when designing asynchronous job processing. Covers queue selection, idempotency, retry and backoff policy, scheduling, poison messages, and observability for work that happens outside the request.
DevOps
182

More Data skills

Data Modeling is one of 668 Data skills cataloged on DirSkills, ranked #644 by stars among them.

Browse all Data skills →
📊
3w ago

Benchmark Methodology

Benchmark Methodology scores competitors across nine weighted dimensions using explicit 1–5 rubrics and a tension plot. Use it after competitive-platform-analysis to produce comparable, evidence-anchored profile cards for a competitive report.
Data
239.8K36.4K
📓
3w ago

Jupyter Notebook

Jupyter Notebook provides a stateful Python REPL via a live Jupyter kernel (hamelnb) so variables persist across executions. Use it for iterative data exploration, API inspection, and complex code development.
Data
229.7K45.3K
🪙
3w ago

Solana

Solana queries wallets, token holdings, transactions, NFTs, whale transfers, and network stats on the Solana blockchain with USD pricing via CoinGecko. Use it when you need to inspect an address, verify a transaction, check token metadata or prices, or monitor network health.
Data
229.7K45.3K