Agent skill

Database Schema Designer

by meshery in meshery/meshery-operator

Design robust, scalable database schemas for SQL and NoSQL databases.

MITAuto-check passedDatabases

Install Database Schema Designer

skills CLI
$ npx skills add meshery/meshery-operator --skill database-schema-designer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install meshery/meshery-operator database-schema-designer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/meshery/meshery-operator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/database-schema-designer .claude/skills/database-schema-designer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
database-schema-designer
GitHub stars
151
Used in
3 other repos
Token cost
~4.4k tokens
SKILL.md length
967 words
Files
4 (incl. references, assets)
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

Design robust, scalable database schemas for SQL and NoSQL databases.

  • Works in 4 steps: Database-Specific Patterns: Add MySQL vs… → Advanced Patterns: Time-series, event… → ORM Integration: TypeORM, Prisma,… → …
  • Tasks that involve Database schema design
  • SKILL.md covers Quick Start, Triggers, Key Terms and Quick Reference, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Database Schema Designer is an agent skill from meshery/meshery-operator. Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `README.md` and `references/schema-design-checklist.md`).

It sits in Databases, covering Database schema design. It works with SQL and Kubernetes. The repository describes itself as: Meshery Operator is a Kubernetes Operator that deploys and manages the lifecycle of two Meshery components critical to Meshery's operations of Kubernetes clusters. The licence is MIT.

When your agent uses it

  • Tasks that involve Database schema design

Example prompts

  • “/database-schema-designer”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Database-Specific Patterns: Add MySQL vs PostgreSQL vs SQLite variations
  2. Advanced Patterns: Time-series, event sourcing, CQRS, multi-tenancy
  3. ORM Integration: TypeORM, Prisma, SQLAlchemy patterns
  4. Monitoring: Query performance tracking, slow query alerts

What it can do on your machine

Read from SKILL.md and the folder at commit 632cd41. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are sql, json, javascript and python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Database Schema Designer loads about 4.4k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 967 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from meshery/meshery-operator at commit 632cd41, republished under its MIT licence (© meshery). 967 words, ~4,406 tokens.

Download SKILL.mdSave it as .claude/skills/database-schema-designer/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
database-schema-designer
description
Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.
license
MIT

Database Schema Designer

Design production-ready database schemas with best practices built-in.


Quick Start

Just describe your data model:

design a schema for an e-commerce platform with users, products, orders

You'll get a complete SQL schema like:

sql
CREATE TABLE users (
  id BIGINT AUTO_INCREMENT PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE orders (
  id BIGINT AUTO_INCREMENT PRIMARY KEY,
  user_id BIGINT NOT NULL REFERENCES users(id),
  total DECIMAL(10,2) NOT NULL,
  INDEX idx_orders_user (user_id)
);

What to include in your request:

  • Entities (users, products, orders)
  • Key relationships (users have orders, orders have items)
  • Scale hints (high-traffic, millions of records)
  • Database preference (SQL/NoSQL) - defaults to SQL if not specified

Triggers

TriggerExample
design schema"design a schema for user authentication"
database design"database design for multi-tenant SaaS"
create tables"create tables for a blog system"
schema for"schema for inventory management"
model data"model data for real-time analytics"
I need a database"I need a database for tracking orders"
design NoSQL"design NoSQL schema for product catalog"

Key Terms

TermDefinition
NormalizationOrganizing data to reduce redundancy (1NF → 2NF → 3NF)
3NFThird Normal Form - no transitive dependencies between columns
OLTPOnline Transaction Processing - write-heavy, needs normalization
OLAPOnline Analytical Processing - read-heavy, benefits from denormalization
Foreign Key (FK)Column that references another table's primary key
IndexData structure that speeds up queries (at cost of slower writes)
Access PatternHow your app reads/writes data (queries, joins, filters)
DenormalizationIntentionally duplicating data to speed up reads

Quick Reference

TaskApproachKey Consideration
New schemaNormalize to 3NF firstDomain modeling over UI
SQL vs NoSQLAccess patterns decideRead/write ratio matters
Primary keysINT or UUIDUUID for distributed systems
Foreign keysAlways constrainON DELETE strategy critical
IndexesFKs + WHERE columnsColumn order matters
MigrationsAlways reversibleBackward compatible first

Process Overview

Your Data Requirements
    |
    v
+-----------------------------------------------------+
| Phase 1: ANALYSIS                                   |
| * Identify entities and relationships               |
| * Determine access patterns (read vs write heavy)   |
| * Choose SQL or NoSQL based on requirements         |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 2: DESIGN                                     |
| * Normalize to 3NF (SQL) or embed/reference (NoSQL) |
| * Define primary keys and foreign keys              |
| * Choose appropriate data types                     |
| * Add constraints (UNIQUE, CHECK, NOT NULL)         |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 3: OPTIMIZE                                   |
| * Plan indexing strategy                            |
| * Consider denormalization for read-heavy queries   |
| * Add timestamps (created_at, updated_at)           |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 4: MIGRATE                                    |
| * Generate migration scripts (up + down)            |
| * Ensure backward compatibility                     |
| * Plan zero-downtime deployment                     |
+-----------------------------------------------------+
    |
    v
Production-Ready Schema

Commands

CommandWhen to UseAction
design schema for {domain}Starting freshFull schema generation
normalize {table}Fixing existing tableApply normalization rules
add indexes for {table}Performance issuesGenerate index strategy
migration for {change}Schema evolutionCreate reversible migration
review schemaCode reviewAudit existing schema

Workflow: Start with design schema → iterate with normalize → optimize with add indexes → evolve with migration


Core Principles

PrincipleWHYImplementation
Model the DomainUI changes, domain doesn'tEntity names reflect business concepts
Data Integrity FirstCorruption is costly to fixConstraints at database level
Optimize for Access PatternCan't optimize for bothOLTP: normalized, OLAP: denormalized
Plan for ScaleRetrofitting is painfulIndex strategy + partitioning plan

Anti-Patterns

AvoidWhyInstead
VARCHAR(255) everywhereWastes storage, hides intentSize appropriately per field
FLOAT for moneyRounding errorsDECIMAL(10,2)
Missing FK constraintsOrphaned dataAlways define foreign keys
No indexes on FKsSlow JOINsIndex every foreign key
Storing dates as stringsCan't compare/sortDATE, TIMESTAMP types
SELECT * in queriesFetches unnecessary dataExplicit column lists
Non-reversible migrationsCan't rollbackAlways write DOWN migration
Adding NOT NULL without defaultBreaks existing rowsAdd nullable, backfill, then constrain

Verification Checklist

After designing a schema:

  • Every table has a primary key
  • All relationships have foreign key constraints
  • ON DELETE strategy defined for each FK
  • Indexes exist on all foreign keys
  • Indexes exist on frequently queried columns
  • Appropriate data types (DECIMAL for money, etc.)
  • NOT NULL on required fields
  • UNIQUE constraints where needed
  • CHECK constraints for validation
  • created_at and updated_at timestamps
  • Migration scripts are reversible
  • Tested on staging with production data

<details>
<summary><strong>Deep Dive: Normalization (SQL)</strong></summary>
Normal Forms
FormRuleViolation Example
1NFAtomic values, no repeating groupsproduct_ids = '1,2,3'
2NF1NF + no partial dependenciescustomer_name in order_items
3NF2NF + no transitive dependenciescountry derived from postal_code
1st Normal Form (1NF)
sql
-- BAD: Multiple values in column
CREATE TABLE orders (
  id INT PRIMARY KEY,
  product_ids VARCHAR(255)  -- '101,102,103'
);

-- GOOD: Separate table for items
CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT
);

CREATE TABLE order_items (
  id INT PRIMARY KEY,
  order_id INT REFERENCES orders(id),
  product_id INT
);
2nd Normal Form (2NF)
sql
-- BAD: customer_name depends only on customer_id
CREATE TABLE order_items (
  order_id INT,
  product_id INT,
  customer_name VARCHAR(100),  -- Partial dependency!
  PRIMARY KEY (order_id, product_id)
);

-- GOOD: Customer data in separate table
CREATE TABLE customers (
  id INT PRIMARY KEY,
  name VARCHAR(100)
);
3rd Normal Form (3NF)
sql
-- BAD: country depends on postal_code
CREATE TABLE customers (
  id INT PRIMARY KEY,
  postal_code VARCHAR(10),
  country VARCHAR(50)  -- Transitive dependency!
);

-- GOOD: Separate postal_codes table
CREATE TABLE postal_codes (
  code VARCHAR(10) PRIMARY KEY,
  country VARCHAR(50)
);
When to Denormalize
ScenarioDenormalization Strategy
Read-heavy reportingPre-calculated aggregates
Expensive JOINsCached derived columns
Analytics dashboardsMaterialized views
sql
-- Denormalized for performance
CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT,
  total_amount DECIMAL(10,2),  -- Calculated
  item_count INT               -- Calculated
);
</details>
<details>
<summary><strong>Deep Dive: Data Types</strong></summary>
Show full SKILL.md (382 more words)Show less
String Types
TypeUse CaseExample
CHAR(n)Fixed lengthState codes, ISO dates
VARCHAR(n)Variable lengthNames, emails
TEXTLong contentArticles, descriptions
sql
-- Good sizing
email VARCHAR(255)
phone VARCHAR(20)
country_code CHAR(2)
Numeric Types
TypeRangeUse Case
TINYINT-128 to 127Age, status codes
SMALLINT-32K to 32KQuantities
INT-2.1B to 2.1BIDs, counts
BIGINTVery largeLarge IDs, timestamps
DECIMAL(p,s)Exact precisionMoney
FLOAT/DOUBLEApproximateScientific data
sql
-- ALWAYS use DECIMAL for money
price DECIMAL(10, 2)  -- $99,999,999.99

-- NEVER use FLOAT for money
price FLOAT  -- Rounding errors!
Date/Time Types
sql
DATE        -- 2025-10-31
TIME        -- 14:30:00
DATETIME    -- 2025-10-31 14:30:00
TIMESTAMP   -- Auto timezone conversion

-- Always store in UTC
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
Boolean
sql
-- PostgreSQL
is_active BOOLEAN DEFAULT TRUE

-- MySQL
is_active TINYINT(1) DEFAULT 1
</details>
<details>
<summary><strong>Deep Dive: Indexing Strategy</strong></summary>
When to Create Indexes
Always IndexReason
Foreign keysSpeed up JOINs
WHERE clause columnsSpeed up filtering
ORDER BY columnsSpeed up sorting
Unique constraintsEnforced uniqueness
sql
-- Foreign key index
CREATE INDEX idx_orders_customer ON orders(customer_id);

-- Query pattern index
CREATE INDEX idx_orders_status_date ON orders(status, created_at);
Index Types
TypeBest ForExample
B-TreeRanges, equalityprice > 100
HashExact matches onlyemail = 'x@y.com'
Full-textText searchMATCH AGAINST
PartialSubset of rowsWHERE is_active = true
Composite Index Order
sql
CREATE INDEX idx_customer_status ON orders(customer_id, status);

-- Uses index (customer_id first)
SELECT * FROM orders WHERE customer_id = 123;
SELECT * FROM orders WHERE customer_id = 123 AND status = 'pending';

-- Does NOT use index (status alone)
SELECT * FROM orders WHERE status = 'pending';

Rule: Most selective column first, or column most queried alone.

Index Pitfalls
PitfallProblemSolution
Over-indexingSlow writesOnly index what's queried
Wrong column orderUnused indexMatch query patterns
Missing FK indexesSlow JOINsAlways index FKs
</details>
<details>
<summary><strong>Deep Dive: Constraints</strong></summary>
Primary Keys
sql
-- Auto-increment (simple)
id INT AUTO_INCREMENT PRIMARY KEY

-- UUID (distributed systems)
id CHAR(36) PRIMARY KEY DEFAULT (UUID())

-- Composite (junction tables)
PRIMARY KEY (student_id, course_id)
Foreign Keys
sql
FOREIGN KEY (customer_id) REFERENCES customers(id)
  ON DELETE CASCADE     -- Delete children with parent
  ON DELETE RESTRICT    -- Prevent deletion if referenced
  ON DELETE SET NULL    -- Set to NULL when parent deleted
  ON UPDATE CASCADE     -- Update children when parent changes
StrategyUse When
CASCADEDependent data (order_items)
RESTRICTImportant references (prevent accidents)
SET NULLOptional relationships
Other Constraints
sql
-- Unique
email VARCHAR(255) UNIQUE NOT NULL

-- Composite unique
UNIQUE (student_id, course_id)

-- Check
price DECIMAL(10,2) CHECK (price >= 0)
discount INT CHECK (discount BETWEEN 0 AND 100)

-- Not null
name VARCHAR(100) NOT NULL
</details>
<details>
<summary><strong>Deep Dive: Relationship Patterns</strong></summary>
One-to-Many
sql
CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT NOT NULL REFERENCES customers(id)
);

CREATE TABLE order_items (
  id INT PRIMARY KEY,
  order_id INT NOT NULL REFERENCES orders(id) ON DELETE CASCADE,
  product_id INT NOT NULL,
  quantity INT NOT NULL
);
Many-to-Many
sql
-- Junction table
CREATE TABLE enrollments (
  student_id INT REFERENCES students(id) ON DELETE CASCADE,
  course_id INT REFERENCES courses(id) ON DELETE CASCADE,
  enrolled_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  PRIMARY KEY (student_id, course_id)
);
Self-Referencing
sql
CREATE TABLE employees (
  id INT PRIMARY KEY,
  name VARCHAR(100) NOT NULL,
  manager_id INT REFERENCES employees(id)
);
Polymorphic
sql
-- Approach 1: Separate FKs (stronger integrity)
CREATE TABLE comments (
  id INT PRIMARY KEY,
  content TEXT NOT NULL,
  post_id INT REFERENCES posts(id),
  photo_id INT REFERENCES photos(id),
  CHECK (
    (post_id IS NOT NULL AND photo_id IS NULL) OR
    (post_id IS NULL AND photo_id IS NOT NULL)
  )
);

-- Approach 2: Type + ID (flexible, weaker integrity)
CREATE TABLE comments (
  id INT PRIMARY KEY,
  content TEXT NOT NULL,
  commentable_type VARCHAR(50) NOT NULL,
  commentable_id INT NOT NULL
);
</details>
<details>
<summary><strong>Deep Dive: NoSQL Design (MongoDB)</strong></summary>
Embedding vs Referencing
FactorEmbedReference
Access patternRead togetherRead separately
Relationship1:few1:many
Document sizeSmallApproaching 16MB
Update frequencyRarelyFrequently
Embedded Document
json
{
  "_id": "order_123",
  "customer": {
    "id": "cust_456",
    "name": "Jane Smith",
    "email": "jane@example.com"
  },
  "items": [
    { "product_id": "prod_789", "quantity": 2, "price": 29.99 }
  ],
  "total": 109.97
}
Referenced Document
json
{
  "_id": "order_123",
  "customer_id": "cust_456",
  "item_ids": ["item_1", "item_2"],
  "total": 109.97
}
MongoDB Indexes
javascript
// Single field
db.users.createIndex({ email: 1 }, { unique: true });

// Composite
db.orders.createIndex({ customer_id: 1, created_at: -1 });

// Text search
db.articles.createIndex({ title: "text", content: "text" });

// Geospatial
db.stores.createIndex({ location: "2dsphere" });
</details>
<details>
<summary><strong>Deep Dive: Migrations</strong></summary>
Migration Best Practices
PracticeWHY
Always reversibleNeed to rollback
Backward compatibleZero-downtime deploys
Schema before dataSeparate concerns
Test on stagingCatch issues early
Adding a Column (Zero-Downtime)
sql
-- Step 1: Add nullable column
ALTER TABLE users ADD COLUMN phone VARCHAR(20);

-- Step 2: Deploy code that writes to new column

-- Step 3: Backfill existing rows
UPDATE users SET phone = '' WHERE phone IS NULL;

-- Step 4: Make required (if needed)
ALTER TABLE users MODIFY phone VARCHAR(20) NOT NULL;
Renaming a Column (Zero-Downtime)
sql
-- Step 1: Add new column
ALTER TABLE users ADD COLUMN email_address VARCHAR(255);

-- Step 2: Copy data
UPDATE users SET email_address = email;

-- Step 3: Deploy code reading from new column
-- Step 4: Deploy code writing to new column

-- Step 5: Drop old column
ALTER TABLE users DROP COLUMN email;
Migration Template
sql
-- Migration: YYYYMMDDHHMMSS_description.sql

-- UP
BEGIN;
ALTER TABLE users ADD COLUMN phone VARCHAR(20);
CREATE INDEX idx_users_phone ON users(phone);
COMMIT;

-- DOWN
BEGIN;
DROP INDEX idx_users_phone ON users;
ALTER TABLE users DROP COLUMN phone;
COMMIT;
</details>
<details>
<summary><strong>Deep Dive: Performance Optimization</strong></summary>
Query Analysis
sql
EXPLAIN SELECT * FROM orders
WHERE customer_id = 123 AND status = 'pending';
Look ForMeaning
type: ALLFull table scan (bad)
type: refIndex used (good)
key: NULLNo index used
rows: highMany rows scanned
N+1 Query Problem
python
# BAD: N+1 queries
orders = db.query("SELECT * FROM orders")
for order in orders:
    customer = db.query(f"SELECT * FROM customers WHERE id = {order.customer_id}")

# GOOD: Single JOIN
results = db.query("""
    SELECT orders.*, customers.name
    FROM orders
    JOIN customers ON orders.customer_id = customers.id
""")
Optimization Techniques
TechniqueWhen to Use
Add indexesSlow WHERE/ORDER BY
DenormalizeExpensive JOINs
PaginationLarge result sets
CachingRepeated queries
Read replicasRead-heavy load
PartitioningVery large tables
</details>

Extension Points

  1. Database-Specific Patterns: Add MySQL vs PostgreSQL vs SQLite variations
  2. Advanced Patterns: Time-series, event sourcing, CQRS, multi-tenancy
  3. ORM Integration: TypeORM, Prisma, SQLAlchemy patterns
  4. Monitoring: Query performance tracking, slow query alerts

© meshery, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references, assets) in .claude/skills/database-schema-designer of meshery/meshery-operator.

  • SKILL.md
  • README.md
  • assets/templates/migration-template.sql
  • references/schema-design-checklist.md

Open the folder on GitHubat commit 632cd41

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in meshery/meshery-operator, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Database Schema Designer

What does Database Schema Designer do?

Design robust, scalable database schemas for SQL and NoSQL databases. Database Schema Designer is an agent skill from meshery/meshery-operator. Design robust, scalable database schemas for SQL and NoSQL databases.

When should I use Database Schema Designer?

Database Schema Designer fits situations like: tasks that involve Database schema design.

How do I install Database Schema Designer in Claude Code?

Run `npx skills add meshery/meshery-operator --skill database-schema-designer -a claude-code`. Or copy the skill folder (.claude/skills/database-schema-designer in meshery/meshery-operator) into .claude/skills/database-schema-designer in your project. Claude Code loads it when a task matches its description.

How do I install Database Schema Designer in Codex?

Run `npx skills add meshery/meshery-operator --skill database-schema-designer -a codex`. Or copy the skill folder (.claude/skills/database-schema-designer in meshery/meshery-operator) into .agents/skills/database-schema-designer in your project. Codex loads it when a task matches its description.

Can I use Database Schema Designer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add meshery/meshery-operator --skill database-schema-designer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/database-schema-designer, .gemini/skills/database-schema-designer, .github/skills/database-schema-designer and .opencode/skills/database-schema-designer in your project.

What does Database Schema Designer need to run?

SKILL.md names no scripts, command-line tools or credentials: Database Schema Designer is instructions for the agent only.

Does Database Schema Designer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Database Schema Designer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Database Schema Designer use?

Database Schema Designer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Database Schema Designer use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 846 tokens, read only when the agent opens those files.

What are the alternatives to Database Schema Designer?

Skills that share tags, products or a category with Database Schema Designer: Schema Exploration (timescale/pg-aiguide, 1.9k stars), Cursor BYOK Database Schema (leookun/cursor-byok, 3.2k stars), SQL Optimization Patterns (ynulihao/AgentSkillOS, 617 stars) and Replica Architect (Jakeschincariol/replica-skill, 908 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Database Schema Designer?

meshery (a GitHub organization) maintains it in meshery/meshery-operator, which has 151 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 21, 2026.

Source: meshery/meshery-operator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.