A command for designing a database schema, the structure that defines tables or collections, fields, relationships, and indexes. It also plans how that structure can change safely over time.

Use it to plan entities, fields, keys, relationships, indexes, partitioning, deletion rules, and migration strategies for a new or changing database.

It helps match the database design to transaction processing, analytics, or both. This reduces problems caused by poor query performance, inconsistent data, or unsafe schema changes.

data-model — Command for Claude Code by sairam0424

Install

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.

Clone the repo

git clone --depth 1 https://github.com/sairam0424/MindForge

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/sairam0424/mindforge/data-model/github.svg)](https://agentmods.dev/commands/sairam0424/mindforge/data-model)

Your own site

<a href="https://agentmods.dev/commands/sairam0424/mindforge/data-model"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/data-model/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/sairam0424/mindforge/data-model"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/data-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 33 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 838 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00033	$0.00838
Opus 5	$0.00016	$0.00419
Sonnet 5	$0.00007	$0.00168
Haiku 4.5	$0.00003	$0.00084

Measured 5d ago against content hash eff8e695cca0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

data-model 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 5d 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.

.claude/commands/mindforge/data-model.md · 76 lines

What it actually says

<execution_context> @.mindforge/skills/data-modeling/SKILL.md </execution_context>

Choose modeling approach: Based on --style flag or access patterns:
- OLTP (normalized): 3NF for transactional workloads, minimize write anomalies
- OLAP (dimensional): Star/snowflake schema for analytical queries, optimize reads
- Hybrid: Normalized for writes + materialized views for reads
- Consider: read/write ratio, query patterns, data volume, consistency requirements
- Document trade-offs of chosen approach vs alternatives
Define schema: For each entity, specify:
- Table/collection name (plural, snake_case)
- Columns with types, constraints, and descriptions
- Primary key strategy (UUID v7 for distributed, SERIAL for simple)
- Foreign keys and referential integrity rules
- Indexes (covering indexes for hot queries, partial indexes for filtered queries)
- Partitioning strategy if data volume warrants it (by time, by tenant)
- Soft delete vs hard delete decision per entity
Plan evolution strategy: Design for safe schema changes:
- Additive-only migrations (add columns, add tables — never remove in-place)
- Expand-contract pattern for breaking changes (3-phase: add new → migrate data → remove old)
- Backward compatibility window (N-1 app version must work with N schema)
- Migration testing: run against production-size dataset before deploy
- Rollback plan for each migration (reverse migration script)
- Version tracking: migration numbering, applied_at timestamps
Define data contracts: When --contracts flag is set:
- Schema contract: exact field types, nullability, enum values
- SLA contract: freshness (max age), completeness (% populated), volume (expected range)
- Quality contract: uniqueness constraints, valid value ranges, referential integrity
- Breaking change policy: versioned contracts, deprecation notices, consumer notification
- Contract validation: automated checks in CI pipeline
- Owner and consumer registry: who produces, who consumes each dataset
Document lineage: Trace data flow end-to-end:
- Source systems (where data originates)
- Transformation steps (ETL/ELT pipeline stages)
- Storage layers (raw → cleaned → aggregated → served)
- Consumers (which services/dashboards read this data)
- Freshness chain (latency from source to each consumer)
- Data classification (PII, sensitive, public) per field
Output data model: Deliver:
- Entity-relationship diagram (ASCII or description)
- DDL scripts (CREATE TABLE with all constraints)
- Migration scripts for initial setup
- Index strategy document
- Data contract definitions (JSON Schema or Avro)
- Lineage diagram
- Capacity planning estimates (storage growth per month)

Changes

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.

5d ago First seen · 76 lines · 33 tokens per session scan A eff8e695cca0

Subscribe to this mod's changes

data-model is a command published in the GitHub repository sairam0424/MindForge (0 stars, last pushed 5d ago), licensed MIT. It adds 33 tokens to every session and 838 once invoked, about $0.0002 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 2026-09-03.

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data-model

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

What it actually says

What this file has done since we first saw it

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