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 skills/ancoleman/ai-design-components/using-relational-databasesnpx skills add ancoleman/ai-design-components --skill using-relational-databasesgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/skills/ancoleman/ai-design-components/using-relational-databases)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/using-relational-databases"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-relational-databases.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 | $0.00092 | $0.02526 |
| Opus 5 | $0.00046 | $0.01263 |
| Sonnet 5 | $0.00018 | $0.00505 |
| Haiku 4.5 | $0.00009 | $0.00253 |
Grade A, and why
using-relational-databases 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 today.
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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Relational Databases
Purpose
This skill guides relational database selection and implementation across multiple languages. Choose the optimal database engine, ORM/query builder, and deployment strategy for transactional systems, CRUD applications, and structured data storage.
When to Use This Skill
Trigger this skill when:
- Building user authentication, content management, e-commerce applications
- Implementing CRUD operations (Create, Read, Update, Delete)
- Designing data models with relationships (users → posts, orders → items)
- Migrating schemas safely in production
- Setting up connection pooling for performance
- Evaluating serverless database options (Neon, PlanetScale, Turso)
- Integrating with frontend skills (forms, tables, dashboards, search-filter)
Skip this skill for:
- Time-series data at scale (use time-series databases)
- Real-time analytics (use columnar databases)
- Document-heavy workloads (use document databases)
- Key-value caching (use Redis, Memcached)
Quick Reference: Database Selection
Database Selection Decision Tree
═══════════════════════════════════════════════════════════
PRIMARY CONCERN?
├─ MAXIMUM FLEXIBILITY & EXTENSIONS (JSON, arrays, vector search)
│ └─ PostgreSQL
│ ├─ Serverless → Neon (scale-to-zero, database branching)
│ └─ Traditional → Self-hosted, AWS RDS, Google Cloud SQL
│
├─ EMBEDDED / EDGE DEPLOYMENT (local-first, global latency)
│ └─ SQLite or Turso
│ ├─ Global distribution → Turso (libSQL, edge replicas)
│ └─ Local-only → SQLite (embedded, zero-config)
│
├─ LEGACY SYSTEM / MYSQL REQUIRED
│ └─ MySQL
│ ├─ Serverless → PlanetScale (non-blocking migrations)
│ └─ Traditional → Self-hosted, AWS RDS, Google Cloud SQL
│
└─ RAPID PROTOTYPING
├─ Python → SQLModel (FastAPI) or SQLAlchemy 2.0
├─ TypeScript → Prisma (best DX) or Drizzle (performance)
├─ Rust → SQLx (compile-time checks)
└─ Go → sqlc (type-safe code generation)
Quick Reference: ORM vs Query Builder
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/python-sqlalchemy/main.py 4.7 KB runs code
- examples/python-sqlalchemy/README.md 2.8 KB
- examples/python-sqlalchemy/requirements.txt 170 B
- outputs.yaml 13 KB
- references/connection-pooling.md 9.4 KB
- references/migrations-guide.md 9.6 KB
- references/mysql-guide.md 12 KB
- references/orms-go.md 12 KB
- references/orms-python.md 16 KB
- references/orms-rust.md 13 KB
- references/orms-typescript.md 14 KB
- references/postgresql-guide.md 16 KB
- references/serverless-databases.md 12 KB
- references/sqlite-guide.md 7.8 KB
- scripts/generate_migration.py 6.8 KB runs code
- scripts/validate_schema.py 6.4 KB runs code
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.
- today First seen · 254 lines · 92 tokens per session scan A 9a981374b2c4
using-relational-databases is a skill published in the GitHub repository ancoleman/ai-design-components (517 stars, last pushed 8mo ago), licensed MIT. It adds 92 tokens to every session and 2,526 once invoked, about $0.0005 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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