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 Aizaz-Noor/Agent-Startup-Skills --skill schema-designergit clone --depth 1 https://github.com/Aizaz-Noor/Agent-Startup-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/aizaz-noor/agent-startup-skills/schema-designer)<a href="https://agentmods.dev/skills/aizaz-noor/agent-startup-skills/schema-designer"><img src="https://agentmods.dev/badge/skills/aizaz-noor/agent-startup-skills/schema-designer/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/aizaz-noor/agent-startup-skills/schema-designer"><img src="https://agentmods.dev/badge/skills/aizaz-noor/agent-startup-skills/schema-designer.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 | $0.00047 | $0.00495 |
| Opus 5.5 | $0.00019 | $0.00198 |
| Sonnet 5.5 | $0.00009 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
schema-designer 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- schema-designer — 100% identical, 0 lines differ
What it actually says
Schema Designer — Data Modeling Agent
Purpose
Design a complete, normalized database schema before coding begins. Prevent data integrity issues, missing relationships, and poor query performance.
When to Use
- After system architecture is approved
- When designing a new database from scratch
- When adding new tables to an existing schema
Process
Step 1: Entity Identification
From the MVP feature list, identify every "thing" the system stores:
- Users, Products, Orders, Subscriptions, etc.
Step 2: Table Definitions
For each entity, define the complete table:
TABLE: users
├── id UUID PRIMARY KEY, DEFAULT gen_random_uuid()
├── email TEXT UNIQUE, NOT NULL
├── password_hash TEXT NOT NULL
├── name TEXT NOT NULL
├── role TEXT DEFAULT 'user', CHECK (role IN ('user','admin'))
├── created_at TIMESTAMP DEFAULT NOW()
└── updated_at TIMESTAMP DEFAULT NOW()
Step 3: Relationships
Define all foreign keys and relationships:
- One-to-Many: "A user HAS MANY orders"
- Many-to-Many: "A product BELONGS TO MANY categories" (junction table)
Step 4: Indexes
Add indexes for:
- All foreign key columns
- Columns used in WHERE clauses frequently
- Columns used for sorting (ORDER BY)
Step 5: Seed Data
Provide 2-3 sample rows per table for testing.
Rules
- Every table MUST have
id,created_at,updated_at - Never store plain-text passwords (use
password_hash) - Use UUIDs over auto-incrementing integers for primary keys
- Add CHECK constraints for enum-like columns
Output
Produce a schema.md file with all table definitions, relationships,
indexes, and sample seed data.
Exit Criteria
- Every MVP feature's data needs are covered
- All relationships are explicitly defined
- Indexes exist for expected query patterns
- No plain-text password fields
- Seed data is provided for testing
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 · 72 lines · 47 tokens per session scan A 9733800322e8
schema-designer is a skill published in the GitHub repository Aizaz-Noor/Agent-Startup-Skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 47 tokens to every session and 495 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
Schema Design
Scalable and secure schema design patterns for relational databases, NoSQL, and APIs. / TR: İlişkisel veri tabanları, NoSQL ve API'ler için ölçeklenebilir ve güvenli şema tasarım kalıpları.
db-architect-security
Database schema design, ORM model configuration, query optimization, and database security standards. / TR: Veritabanı mimarisi, güvenlik standartları, ORM yapılandırmaları ve veritabanı tasarımı için yetenek.
admin-access-register
Admin account register: system, main and backup admin, seats, plan, 2FA, shared logins and access-review dates, as CSV, SQL, JSON Schema or Notion on request. Use for access reviews.
access-matrix
Access matrix of role-by-module permissions, with per-role scope, confidentiality level and SME tier, as CSV, SQL, JSON Schema or Notion on request. Use for access reviews.
alumni-re-hire-tracker
Alumni and re-hire register: former role, last working day, re-hire eligibility, current employer and re-engagement date, as CSV, SQL, JSON Schema or Notion on request. Use for alumni outreach.
cqrs-implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.