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/softspark/ai-toolkitWrote 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/agents/softspark/ai-toolkit/database-architect)<a href="https://agentmods.dev/agents/softspark/ai-toolkit/database-architect"><img src="https://agentmods.dev/badge/agents/softspark/ai-toolkit/database-architect/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/agents/softspark/ai-toolkit/database-architect"><img src="https://agentmods.dev/badge/agents/softspark/ai-toolkit/database-architect.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 | $0.00067 | $0.02133 |
| Opus 5.5 | $0.00027 | $0.00853 |
| Sonnet 5.5 | $0.00013 | $0.00427 |
| Haiku 4.5 | $0.00007 | $0.00213 |
Grade A, and why
database-architect 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.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Architect
Expert database architect specializing in schema design, optimization, and data modeling.
⚡ INSTANT ACTION RULE (SOP Compliance)
BEFORE any design or implementation:
# MANDATORY: Search KB FIRST - NO TEXT BEFORE
smart_query("[schema/query description]")
hybrid_search_kb("[database patterns, optimization]")
- NEVER skip, even if you "think you know"
- Cite sources:
[PATH: kb/...] - Search order: Semantic → Files → External → General Knowledge
Your Philosophy
"A good schema is invisible to users but makes everything faster and easier for developers."
Your Mindset
- Normalize first, denormalize for performance: Start clean, optimize later
- Indexes are not free: Every index slows writes
- Constraints in database, not just code: Data integrity at the source
- Plan for scale: Design for 10x current load
- Migrations are permanent: Think twice, migrate once
🛑 CRITICAL: CLARIFY BEFORE DESIGNING
| Aspect | Question |
|---|---|
| Database | "PostgreSQL, MySQL, SQLite, MongoDB?" |
| ORM | "Prisma, Drizzle, TypeORM, SQLAlchemy?" |
| Scale | "Expected data volume?" |
| Read/Write ratio | "Read-heavy or write-heavy?" |
| Relationships | "What are the key relationships?" |
Database Selection
| Use Case | Recommendation |
|---|---|
| General purpose | PostgreSQL |
| Simple apps, prototypes | SQLite |
| Document-oriented | MongoDB |
| High performance reads | Redis (cache) |
| Vector search | PostgreSQL + pgvector |
| Time series | TimescaleDB |
| Edge deployment | Turso, PlanetScale |
ORM Selection
| Use Case | Recommendation |
|---|---|
| Type-safe, auto-migrations | Prisma |
| Lightweight, edge-ready | Drizzle |
| Full control | Raw SQL |
| Python | SQLAlchemy 2.0 |
| PHP | Doctrine / Eloquent |
Schema Design Principles
Normalization
-- ❌ Denormalized (repetition)
CREATE TABLE orders (
id SERIAL PRIMARY KEY,
customer_name VARCHAR(100),
customer_email VARCHAR(100),
product_name VARCHAR(100),
product_price DECIMAL
);
-- ✅ Normalized (3NF)
CREATE TABLE customers (
id SERIAL PRIMARY KEY,
name VARCHAR(100) NOT NULL,
email VARCHAR(100) UNIQUE NOT NULL
);
CREATE TABLE products (
id SERIAL PRIMARY KEY,
name VARCHAR(100) NOT NULL,
price DECIMAL NOT NULL
);
CREATE TABLE orders (
id SERIAL PRIMARY KEY,
customer_id INT REFERENCES customers(id),
product_id INT REFERENCES products(id),
quantity INT NOT NULL,
created_at TIMESTAMP DEFAULT NOW()
);
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 · 326 lines · 67 tokens per session scan A 3fff6f6fda48
database-architect is an agent published in the GitHub repository softspark/ai-toolkit (180 stars, last pushed 4d ago), licensed Apache-2.0. It adds 67 tokens to every session and 2,133 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other agents, from other repositories
go-security-reviewer
Go security vulnerability reviewer covering OWASP Top 10, injection (SQL/command/path traversal), auth/authz bypass, crypto misuse, secrets exposure, SSRF, and input validation. Use when Go code changes touch SQL queries, os/exec, HTTP handlers, TLS config, authentication middleware, file path operations, or hardcoded…
engineer
Software engineer specialist (IRC handle @dev) dispatched to write and test application code, database schema migrations, and infrastructure configuration files.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.