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 fabioc-aloha/Alex_Skill_Mall --skill database-designgit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote 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/fabioc-aloha/alex_skill_mall/database-design)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/database-design"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/database-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/fabioc-aloha/alex_skill_mall/database-design"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/database-design.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 | $0.00015 | $0.02594 |
| Opus 5.5 | $0.00006 | $0.01038 |
| Sonnet 5.5 | $0.00003 | $0.00519 |
| Haiku 4.5 | $0.00002 | $0.00259 |
Grade A, and why
database-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 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 — 418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Design Skill
Model data to match access patterns. Normalize for integrity, denormalize for performance.
Choosing a Database
Decision Matrix
| Factor | SQL (Relational) | NoSQL (Document) | Key-Value | Graph |
|---|---|---|---|---|
| Schema | Fixed, strict | Flexible | None | Nodes/edges |
| Transactions | ACID built-in | Eventually consistent (usually) | Limited | Varies |
| Joins | Native support | Expensive/manual | N/A | Native (relationships) |
| Scale | Vertical → Horizontal | Horizontal native | Horizontal native | Specialized |
| Best for | Structured data, complex queries | Rapid iteration, varied shape | Cache, sessions | Relationships |
Common Choices (2026)
| Database | Type | Best For |
|---|---|---|
| PostgreSQL | Relational | General purpose, full-featured |
| SQLite | Relational | Embedded, serverless |
| MongoDB | Document | Rapid prototyping, flexible schema |
| Redis | Key-Value | Caching, sessions, pub/sub |
| Cosmos DB | Multi-model | Azure-native, global distribution |
| DynamoDB | Key-Value/Document | AWS-native, massive scale |
| Neo4j | Graph | Social networks, recommendations |
Schema Design Principles
Normalization Levels
| Form | Rule | Trade-off |
|---|---|---|
| 1NF | No repeating groups | Basic structure |
| 2NF | No partial dependencies | Remove redundancy |
| 3NF | No transitive dependencies | Data integrity |
| BCNF | Every determinant is a key | Strict integrity |
When to Denormalize
- Read-heavy workloads
- Complex queries hitting many tables
- When consistency can be eventual
- Reporting/analytics tables
Example: E-Commerce Schema
-- Normalized (3NF)
CREATE TABLE customers (
id SERIAL PRIMARY KEY,
email VARCHAR(255) UNIQUE NOT NULL,
name VARCHAR(255) NOT NULL
);
CREATE TABLE orders (
id SERIAL PRIMARY KEY,
customer_id INTEGER REFERENCES customers(id),
created_at TIMESTAMP DEFAULT NOW(),
status VARCHAR(50) NOT NULL
);
CREATE TABLE order_items (
id SERIAL PRIMARY KEY,
order_id INTEGER REFERENCES orders(id),
product_id INTEGER REFERENCES products(id),
quantity INTEGER NOT NULL,
unit_price DECIMAL(10,2) NOT NULL
);
-- Denormalized for reporting
CREATE TABLE order_summary (
order_id INTEGER PRIMARY KEY,
customer_email VARCHAR(255),
customer_name VARCHAR(255),
total_amount DECIMAL(10,2),
item_count INTEGER,
created_at TIMESTAMP
);
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 · 418 lines · 15 tokens per session scan A cb74d4d26c66
database-design is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (5 stars, last pushed 8d ago), licensed MIT. It adds 15 tokens to every session and 2,594 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
database-redis
Optimize Redis as cache and coordination infrastructure with TTL, eviction, and latency-aware key design. Use when implementing Redis caching, key invalidation, or Redis performance work.
database-mongodb
Apply MongoDB data-modeling, indexing, and query rules from access patterns. Use when designing schemas, choosing embed vs reference, or tuning MongoDB query behavior.
database-postgresql
Apply PostgreSQL standards for migrations, indexing, transactions, and ORM boundaries. Use when editing entities, Prisma schema, migrations, RLS, or query-performance work for PostgreSQL.
database-query-performance
Diagnose database latency with explain plans, index ownership, and query-shape review. Use when a query is slow, an index is missing, or scans and N+1 patterns appear.
database-transactions
Define transaction boundaries, locking, and consistency guarantees for multi-step writes. Use when designing atomic operations, retries, idempotency, or concurrent write behavior.
api-database-mongodb
Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions.