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.
npx agentmods add commands/swesmith/davila7__claude-code-templates.734b8a50/databasegit clone --depth 1 https://github.com/swesmith/davila7__claude-code-templates.734b8a50Wrote 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/swesmith/davila7__claude-code-templates.734b8a50/database)<a href="https://agentmods.dev/commands/swesmith/davila7__claude-code-templates.734b8a50/database"><img src="https://agentmods.dev/badge/commands/swesmith/davila7__claude-code-templates.734b8a50/database.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00504 |
| Opus 5 | $0.00000 | $0.00252 |
| Sonnet 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 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.
This is a copy
100% identical to database — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 5d ago First seen · 56 lines · 0 tokens per session scan A b66a49effbe8
database is a command published in the GitHub repository swesmith/davila7__claude-code-templates.734b8a50 (2 stars, last pushed 8mo 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. It is 100% identical to database, differing in 0 lines, and is treated as a copy.
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Plan regression tests for existing code with it.skip statements.
init
Initialize configurations for Supabase local development.
volume-admin-set-status
Changes the operational status of a named volume.
notebook-query
Query the notebook knowledge base (SQLite) built by /agy:notebook — precise, grounded, cited. Ask in natural language ("sum the amounts by category", "which docs mention 'Acme Corp'", "build a project timeline") or pass raw SQL. Read-only. Use this when you need exact aggregates/lookups across a document corpus…