Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design.
SkillScore82Maintained
Automatic
◆Data Engineer pack
Last verified 8/17/2026
What it changes
Passed 17 of 21 test cases with the skill, and 11 of 21 without it.
Without skill50%
With skill82%
Checks it passed that the model alone missed
Establishes a centralized terminology dictionary and domain glossary.
Defines VPC subnets, IAM role attachments, and KMS key resources in infrastructure code.
Defines a unified data standards table specifying naming rules, standard types, and validation rules.
and 7 more in the full report
8 fixed10 passed either way3 still failing1 broke
Pass-rate lift
+32 pp
Tokens
+121%
Agent turns
0%
Response time
+35%
Model gemini-3.6-flashMeasured 8/17/2026v318 of 22 cases passed with skill · 21 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 8
Prompt
We are building a real-time IoT sensor telemetry platform expecting 50,000 writes per second. Some engineers want to force standard 3NF PostgreSQL tables for this. Should we use relational tables or a non-relational document/wide-column store, and how should data be structured?
What was checked, without and with the skill
Checked
Without skill
With skill
Models data around aggregates and explicit access paths using a non-relational store while documenting BASE consistency tradeoffs.
Missed
Passed
Produces a data standards definition covering field names, data types, allowed domains, and validation rules.
Missed
Passed
case-08 · gemini-3.6-flash · 8/17/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md194 linessingle file
DB Agent - Data Modeling & Database Architecture Specialist
Scheduling
Goal
Design, review, optimize, and document SQL, NoSQL, vector, and retrieval-oriented data systems with explicit schema layers, integrity rules, transaction behavior, capacity assumptions, and audit-aware tradeoffs.
Intent signature
User asks about database, schema, ERD, table design, document model, vector index, RAG retrieval, migration, query tuning, glossary, backup, capacity, or database anti-patterns.
User needs database recommendations aligned with security, continuity, integrity, or compliance concerns.
When to use
Relational database modeling, ERD, and schema design
NoSQL document, key-value, wide-column, or graph data modeling
Vector database and retrieval architecture design for semantic search and RAG
SQL/NoSQL technology selection and tradeoff analysis
Normalization, denormalization, indexing, and partitioning
Transaction design, locking, isolation level, and concurrency control
Data standards, glossary, naming rules, and metadata governance
Capacity estimation, storage planning, hot/cold data separation, and backup strategy
Database anti-pattern review and remediation guidance
ISO 27001, ISO 27002, and ISO 22301-aware database design recommendations
When NOT to use
API-only implementation without schema impact -> use Backend Agent
Infra provisioning only -> use TF Infra Agent
Final quality/security audit -> use QA Agent
Expected inputs
Business entities, events, access patterns, volume, latency, retention, and recovery targets
Existing schema, queries, migrations, indexes, data standards, or retrieval pipeline context
Consistency, transaction, backup, audit, and compliance constraints
Optional target deliverable such as ERD, migration plan, glossary, or capacity estimate
Expected outputs
External, conceptual, and internal schema documentation
Data standards, glossary, capacity estimate, indexing/partitioning plan, and backup/recovery strategy
Integrity, transaction, isolation, and concurrency recommendations
Vector/RAG-specific embedding, chunking, filtering, reranking, and re-index plans when relevant