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.
git clone --depth 1 https://github.com/Justdvp/claude-code-templatesWrote 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.
[](https://agentmods.dev/commands/justdvp/claude-code-templates/database)<a href="https://agentmods.dev/commands/justdvp/claude-code-templates/database"><img src="https://agentmods.dev/badge/commands/justdvp/claude-code-templates/database/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.
<a href="https://agentmods.dev/commands/justdvp/claude-code-templates/database"><img src="https://agentmods.dev/badge/commands/justdvp/claude-code-templates/database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00504 |
| Opus 5.5 | $0.00000 | $0.00202 |
| Sonnet 5.5 | $0.00000 | $0.00101 |
| Haiku 4.5 | $0.00000 | $0.00050 |
Grade A, and why
database 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- database — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Operations
Set up database operations for $ARGUMENTS following project conventions.
Task
Create or optimize database operations based on the requirements:
- Analyze existing database setup: Check current database configuration, ORM/ODM, and connection patterns
- Identify database type: Determine if using MongoDB, PostgreSQL, MySQL, or other database
- Examine ORM/ODM: Check for Prisma, TypeORM, Mongoose, Sequelize, or raw SQL patterns
- Review existing models: Understand current schema patterns and relationships
- Check migration system: Identify migration tools and patterns in use
- Implement operations: Create models, repositories, or services following project architecture
- Add validation: Include proper schema validation and constraints
- Create tests: Write database operation tests following project patterns
- Update migrations: Add necessary database migrations if schema changes required
Implementation Requirements
- Follow project's database architecture patterns
- Use existing ORM/ODM configuration and connection setup
- Include proper TypeScript types for all database operations
- Add comprehensive error handling and transaction management
- Implement proper indexing for performance
- Follow project's naming conventions for tables/collections and fields
- Consider data validation at both application and database levels
Database Patterns to Consider
Based on your project setup:
- Repository Pattern: Separate data access logic from business logic
- Active Record: Models with built-in database operations
- Data Mapper: Separate domain models from database schema
- Query Builder: Fluent interface for building database queries
- Raw SQL: Direct database queries for complex operations
Operation Types
Common database operations to implement:
- CRUD operations: Create, Read, Update, Delete
- Bulk operations: Batch inserts, updates, deletes
- Aggregation: Complex queries with grouping and calculations
- Relationships: Managing foreign keys and joins
- Transactions: Ensuring data consistency
- Migrations: Schema changes and data transformations
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.
- 1mo ago First seen · 56 lines · 0 tokens per session scan A b66a49effbe8
database is a command published in the GitHub repository Justdvp/claude-code-templates (7 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 504 tokens. 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.
Other commands, from other repositories
db-migrate
Generate a database migration file for schema changes.
add-index
Add database indexes to improve query performance with migration safety.
vd-scaffold
Transforma a intenção já aprovada em /vd-plan em fundação técnica real: schema + migrations, matriz RBAC, integração de pagamento BR-first com webhook idempotente, e tokens de white-label. Não fixa stack — decide por contexto via Triagem Cynefin-lite.
sql-review
Review SQL code for security, performance, correctness, and best practices across all major databases.
performance-optimization
Optimize code, queries, or systems for better performance with a structured profiling and analysis approach.
data
Invoke the Data Engineer for ETL/ELT, dbt, warehouses, streaming, orchestration, and data quality.