WhoDB is a self-hosted workspace for exploring and managing databases through a browser, desktop app, or terminal interface. Developers and data teams use it to browse and edit data, run queries, inspect table relationships, move data, and optionally ask questions through an AI provider. The catalogue includes add-ons for operating WhoDB and working with its database tools.
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 clidey/whodb --skill schema-designergit clone --depth 1 https://github.com/clidey/whodbWrote 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/clidey/whodb/schema-designer)<a href="https://agentmods.dev/skills/clidey/whodb/schema-designer"><img src="https://agentmods.dev/badge/skills/clidey/whodb/schema-designer.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.01339 |
| Opus 5 | $0.00017 | $0.00669 |
| Sonnet 5 | $0.00007 | $0.00268 |
| Haiku 4.5 | $0.00003 | $0.00134 |
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 8d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schema Designer
Help users design database schemas, create tables, and model data relationships.
When to Use
Activate when user asks:
- "Create a table for storing orders"
- "Design a schema for a blog"
- "Add a column to track user preferences"
- "How should I model this relationship?"
Workflow
1. Understand Requirements
Ask clarifying questions:
- What data needs to be stored?
- What are the relationships between entities?
- What queries will be common?
- What's the expected data volume?
2. Check Existing Schema
whodb_tables() → See what already exists
whodb_columns(table="related_table") → Understand existing structure
3. Design the Schema
Follow database design principles:
- Normalize to reduce redundancy
- Use appropriate data types
- Define primary keys
- Establish foreign key relationships
- Add indexes for common queries
4. Generate DDL
Provide CREATE TABLE statements with explanations.
Data Type Guidelines
Identifiers
| Use Case | PostgreSQL | MySQL | SQLite |
|---|---|---|---|
| Auto-increment ID | SERIAL / BIGSERIAL |
INT AUTO_INCREMENT |
INTEGER PRIMARY KEY |
| UUID | UUID |
CHAR(36) |
TEXT |
Text
| Use Case | PostgreSQL | MySQL | SQLite |
|---|---|---|---|
| Short text (<255) | VARCHAR(n) |
VARCHAR(n) |
TEXT |
| Long text | TEXT |
TEXT |
TEXT |
| Fixed length | CHAR(n) |
CHAR(n) |
TEXT |
Numbers
| Use Case | PostgreSQL | MySQL | SQLite |
|---|---|---|---|
| Integer | INTEGER |
INT |
INTEGER |
| Big integer | BIGINT |
BIGINT |
INTEGER |
| Decimal (money) | NUMERIC(10,2) |
DECIMAL(10,2) |
REAL |
| Float | REAL |
FLOAT |
REAL |
Dates
| Use Case | PostgreSQL | MySQL | SQLite |
|---|---|---|---|
| Date only | DATE |
DATE |
TEXT |
| Timestamp | TIMESTAMP |
DATETIME |
TEXT |
| With timezone | TIMESTAMPTZ |
TIMESTAMP |
TEXT |
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.
- 8d ago First seen · 191 lines · 34 tokens per session scan A 92b601743b75
schema-designer is a skill published in the GitHub repository clidey/whodb (5,019 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 1,339 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.
Other skills, from other repositories
graphjin-eval
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.
graphjin-env
Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.
Drizzle ORM Testing
Testing patterns for Drizzle ORM covering migration testing, query builder testing, transaction testing, and database integration testing with PostgreSQL, SQLite, and MySQL.
sql-expert
Expert-level SQL database design, querying, optimization, and administration across PostgreSQL, MySQL, and SQL Server. Use when the user mentions database, PostgreSQL, MySQL, or query optimization, or when the task involves Database Design, Advanced Queries, Indexes and Performance, or Transactions and Concurrency.
Database Schema Reviewer
Reviews database schemas for normalization issues, missing indexes, naming inconsistencies, and scalability risks.
banco-de-dados-ops
Operações de banco de dados: queries otimizadas, migrations versionadas, estratégia de indexação, modelagem relacional e NoSQL, backup e recovery. Foco em PostgreSQL e MySQL com contexto de dados brasileiros.