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/GktuOktay/ai-skillsWrote 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/rules/gktuoktay/ai-skills/schema)<a href="https://agentmods.dev/rules/gktuoktay/ai-skills/schema"><img src="https://agentmods.dev/badge/rules/gktuoktay/ai-skills/schema.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 5.1 | $0.00000 | $0.01112 |
| Opus 5 | $0.00000 | $0.00556 |
| Sonnet 5 | $0.00000 | $0.00222 |
| Haiku 4.5 | $0.00000 | $0.00111 |
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 3d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database and API Schema Design
Effective schema design is foundational to application performance, data integrity, and future scalability. This guide covers relational, NoSQL, and API schema design principles.
1. Relational Database Schema Design (SQL)
Relational databases (PostgreSQL, MySQL) excel at structured data with complex relationships.
Normalization
The process of organizing data to reduce redundancy and improve data integrity.
- 1NF: Eliminate repeating groups (each column contains atomic values).
- 2NF: Eliminate partial dependencies (non-key columns depend on the entire primary key).
- 3NF: Eliminate transitive dependencies (non-key columns depend only on the primary key, not on other non-key columns). Rule of thumb: Normalize to 3NF by default, denormalize only for specific read-performance bottlenecks.
Indexing
Indexes drastically speed up read operations but slow down writes and consume disk space.
- Primary Keys: Automatically indexed.
- Foreign Keys: Always index foreign keys to speed up JOIN operations.
- Frequent Queries: Create B-Tree indexes on columns frequently used in
WHERE,ORDER BY, orGROUP BYclauses. - Composite Indexes: Use for queries that filter on multiple columns simultaneously (order matters: put the most selective column first).
Constraints
Enforce data integrity at the database level.
NOT NULL,UNIQUE,CHECK(e.g.,price > 0),FOREIGN KEY(referential integrity).
Migrations
Always use a migration tool (e.g., Prisma, Flyway, Alembic) to version control schema changes. Never modify the production schema manually.
2. NoSQL Schema Patterns
NoSQL databases (MongoDB, DynamoDB, Neo4j) are chosen for flexibility, horizontal scalability, or specific data models.
Document Databases (MongoDB)
- Embedding vs. Referencing:
- Embed data (sub-documents) if it is frequently accessed together and rarely changes (e.g., User addresses).
- Reference data (store IDs) if the nested data is large, grows unbounded, or is accessed independently (e.g., Post comments).
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.
- 3d ago First seen · 88 lines · 1,112 tokens per session scan A c766670540f9
schema is a cursor rule published in the GitHub repository GktuOktay/ai-skills (2 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,112 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 2026-09-03.
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