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 skills add seb1n/awesome-ai-agent-skills --skill database-schema-designgit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/skills/seb1n/awesome-ai-agent-skills/database-schema-design)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/database-schema-design"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/database-schema-design/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/skills/seb1n/awesome-ai-agent-skills/database-schema-design"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/database-schema-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.01710 |
| Opus 5 | $0.00019 | $0.00855 |
| Sonnet 5 | $0.00008 | $0.00342 |
| Haiku 4.5 | $0.00004 | $0.00171 |
Grade A, and why
database-schema-design 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 13d 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-schema-design — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Schema Design
This skill enables an AI agent to design robust, normalized relational database schemas from application requirements. The agent analyzes entities, defines tables with appropriate data types and constraints, establishes relationships (one-to-one, one-to-many, many-to-many), applies normalization up to 3NF, creates indexes for query performance, and produces complete SQL DDL scripts ready for execution.
Workflow
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Gather and analyze requirements: Interview the user or parse a specification document to identify all entities, their attributes, and the relationships between them. Clarify cardinality (1:1, 1:N, M:N), required vs. optional fields, and any domain-specific constraints such as unique emails, positive prices, or enumerated statuses. Document assumptions explicitly before proceeding.
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Model entities and relationships: Translate requirements into a logical data model. Define each entity as a table, choose appropriate primary keys (prefer surrogate integer or UUID keys for stability), and map relationships. For one-to-many, add a foreign key on the "many" side. For many-to-many, create a junction table with composite primary keys referencing both parent tables. For one-to-one, use a shared primary key or a unique foreign key.
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Apply normalization: Review the schema against normal forms. Ensure every non-key column depends on the whole primary key (2NF) and only on the primary key (3NF). Split tables that contain transitive dependencies. Strategically denormalize only when justified by read-heavy query patterns, and document the trade-off.
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Define constraints and indexes: Add NOT NULL, UNIQUE, CHECK, and DEFAULT constraints to enforce data integrity at the database level. Create indexes on foreign key columns, columns used in WHERE clauses, and columns used for sorting or grouping. Consider composite indexes for multi-column query patterns.
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Generate SQL DDL scripts: Produce complete CREATE TABLE statements with all columns, types, constraints, and indexes. Use IF NOT EXISTS for idempotency. Order statements so that referenced tables are created before referencing tables.
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.
- 13d ago First seen · 127 lines · 38 tokens per session scan A 7876794e8461
database-schema-design is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 1,710 once invoked, about $0.0002 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 2026-08-30.
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