schema

A command that reads a project's database structure and presents it as a table, an ASCII relationship diagram, or Markdown documentation. A database schema describes tables, columns, data types, rules, and links between records.

In plain words
What is it for?
Use it to review one table and its relationships, summarize columns and constraints, or create database documentation for a docs/ folder.
Why use it?
It makes an unfamiliar database easier to inspect and explain without manually piecing together schema files and settings.

Command

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.

agentmods
npx agentmods add commands/iwritec0de/app-dev/schema
Clone the repo
git clone --depth 1 https://github.com/iwritec0de/app-dev
Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 722 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00008 $0.00722
Opus 5 $0.00004 $0.00361
Sonnet 5 $0.00002 $0.00144
Haiku 4.5 $0.00001 $0.00072

Measured yesterday against content hash 155ec4e0cdae, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

schema 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 yesterday.

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.

commands/schema.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Database Schema

Inspect, visualize, and document the database schema.

Load the database-designer skill for schema documentation patterns.

Usage

/schema [table_name] [--format diagram|table|markdown]

Arguments

Parse $ARGUMENTS to extract:

  • table_name (optional) — Focus on a specific table and its relationships
  • --format (optional, default: table) — Output format
    • table — Summary tables of columns, types, constraints, indexes
    • diagram — ASCII entity-relationship diagram
    • markdown — Full documentation suitable for a docs/ directory

Instructions

1. Determine Database Type

Check the project for database indicators:

ls prisma/schema.prisma 2>/dev/null         # Prisma
ls knexfile.* 2>/dev/null                    # Knex
ls ormconfig.* typeorm.config.* 2>/dev/null  # TypeORM
ls alembic.ini 2>/dev/null                   # SQLAlchemy/Alembic
ls config/database.yml 2>/dev/null           # Rails

Also check for DATABASE_URL or MYSQL_* env patterns to determine PostgreSQL vs MySQL.

2. Extract Schema

If an ORM schema file exists (Prisma, TypeORM, etc.), read it directly — it's the source of truth.

If MCP database connection is available, use it to introspect:

  • List all tables
  • For each table: columns, types, nullable, defaults, constraints
  • Foreign key relationships
  • Indexes

If neither, search for migration files or SQL schema dumps.

3. Output by Format

table (default):

## Schema: my_database

### users
| Column | Type | Nullable | Default | Key |
|--------|------|----------|---------|-----|
| id | uuid | NO | gen_random_uuid() | PK |
| email | varchar(255) | NO | — | UQ |
| name | varchar(100) | YES | — | |
| created_at | timestamptz | NO | now() | |

Indexes: idx_users_email (email)
Relations: → posts.author_id, → comments.user_id

diagram:

┌─────────┐       ┌─────────┐
│  users  │───┐   │  posts  │
├─────────┤   │   ├─────────┤
│ id (PK) │   └──→│ author_id│
│ email   │       │ id (PK) │
│ name    │       │ title   │
└─────────┘       └─────────┘

Read the full file on GitHub · 93 lines

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.

  1. yesterday First seen · 93 lines · 8 tokens per session scan A 155ec4e0cdae

Subscribe to this mod's changes

schema is a command published in the GitHub repository iwritec0de/app-dev (3 stars, last pushed 4mo ago), licensed MIT. It adds 8 tokens to every session and 722 once invoked, about $0.0000 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 2026-08-31.