schema-architect
Skill7 starsChanged 43 days agoMITView on GitHub
Derive a normalized database schema from business requirements: entities, relationships, normalization level (1NF to BCNF), constraints, and dialect-specific DDL, avoiding god tables. Use when the user designs a new database, models data for a feature, or asks for tables and DDL.
1 other project uses a version of this.
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Most recently taken up by
- ak-ship/fullstack-agent-skillstheir own version, first seen Oct 8, 2026
Other versions1 project uses a version of this.
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Wrote their own version · 1
- ak-ship/fullstack-agent-skills★ 0same name
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The file
We split this skill into the rules it gives an agent
7 rules. About 0 are common, 5 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Before designing, make sure you have the inputs below.
- Distinctive: line 23
Before writing any SQL, analyze the request:
- Distinctive: line 26
Ensure data is logically divided.
- Distinctive: line 27
Stop at 3NF for most OLTP systems.
- Distinctive: line 70
Dynamic (On-Demand): Only connect to a live database to introspect the current schema if the user explicitly requests it.
- Very specific: line 79
No Vague Types: Do not use VARCHAR without limits unless using PostgreSQL TEXT.
- Very specific: line 80
Primary Keys: Default to UUIDv7 or BIGSERIAL/BIGINT IDENTITY for primary keys for distributed readiness.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Schema Architect Protocol
This skill transforms raw business requirements into a robust, normalized, and scalable database schema. It avoids common anti-patterns like "God Tables" or EAV (Entity-Attribute-Value) abuse.
Core assumption: A good schema must be extensible, maintainable, and explicitly document your design choices.
0. Context Intake
Before designing, make sure you have the inputs below. Read the previous workflow step's artifact first if it exists. Ask only for what is missing, in a single message, and state any assumption you make instead of blocking.
- Business requirements or user stories, and the entities already known.
- Target engine and ORM or migration tool.
- Multi-tenancy, audit, soft-delete, and data retention requirements.
1. Requirement Analysis & Normalization Phase
Before writing any SQL, analyze the request:
- Identify Entities & Relationships: What are the root domains? (e.g. Users, Orders, Products). What are the cardinalities?
- Normalization Strategy (1NF → 3NF → BCNF):
- Ensure data is logically divided.
- Stop at 3NF for most OLTP systems. Denormalize ONLY if there is a documented performance optimization justification.
2. Anti-Pattern Check
Scan your intended design against common pitfalls:
- ❌ God Table: Does one table have >30 columns handling multiple disconnected responsibilities (e.g.,
Usershaving billing, shipping, and auth info in one huge table)? → Split them! - ❌ EAV (Entity-Attribute-Value) Abuse: Are you using EAV patterns instead of JSONB or proper typed tables? → Rework to standard models where possible!
- ❌ Polymorphic Associations: Using
entity_idandentity_typewithout Foreign Key constraints? → Use standard associative tables or exclusive arcs!
3. Dialect Selection & Output Generation
Identify the target database (agnostic approach). Generate platform-specific SQL schema.
Outputs. In file mode — the user wants artifacts, or this skill runs as a step of an ecosystem workflow — write both files below to docs/database-report/. In inline mode — a quick question — answer in the chat and end with the JSON below as a Handoff block instead of creating files.
- Human-Readable Markdown (
docs/database-report/schema-architect-report.md)
### 🏗️ Schema Generation Report
- **Target Dialect:** [PostgreSQL/MySQL/Snowflake]
- **Normalization Level:** [e.g., 3NF]
- **Trade-offs made:** [Fast Read vs. Fast Write considerations]
#### 📜 Generated DDL
```sql
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
email VARCHAR(255) UNIQUE NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
```- Machine-Readable JSON (
docs/database-report/schema-architect-output.json)
{
"skill": "schema-architect",
"dialect": "PostgreSQL",
"entities": ["users"],
"relations": [],
"raw_ddl": "CREATE TABLE..."
}4. Execution Context
- Default (Static): Analyze based on provided requirements or existing schema files (e.g.,
schema.prismaor.sqlfiles). - Dynamic (On-Demand): Only connect to a live database to introspect the current schema if the user explicitly requests it.
When to Skip
- The user only needs a single column added to an existing table with no modeling decision.
Guardrails
- No Vague Types: Do not use
VARCHARwithout limits unless using PostgreSQLTEXT. Use proper types (JSONB,NUMERIC(p,s),TIMESTAMPTZvsTIMESTAMP). - Primary Keys: Default to
UUIDv7orBIGSERIAL/BIGINT IDENTITYfor primary keys for distributed readiness. - Audit Columns:
created_atandupdated_atmust exist on almost all operational operational tables.
🔗 Next Steps & Handoffs
<!-- BEGIN GENERATED: handoffs (generated from the ecosystem workflow map by build_ecosystems.py — do not edit by hand) -->Ecosystem: @ecosystem-database — Database Domain.
Workflows:
- New Schema Design Flow (
db-new-schema, step 2 of 4): next →@access-policy-designer.
Handoff contract: pass results to the next skill through docs/database-report/schema-architect-output.json with the fields skill, workflow, created_at, inputs, summary, and next (the handoff contract of @ecosystem-database). If a next skill is not installed, continue with its step from the ecosystem map, or install it with npx skills add fatih-developer/fth-skills --skill <name> after the user agrees.
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