# Oma DB > 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. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations. Use when this capability is needed. - Skill: `tomevault-io/oma-db` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/oma-db` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/oma-db/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/oma-db --- # DB Agent - Data Modeling & Database Architecture Specialist ## 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 ## Core Rules 1. Choose model first, engine second: workload, access pattern, consistency, and scale drive DB selection. 2. For relational workloads, enforce at least **3NF** by default. Break 3NF only with explicit performance justification. 3. For distributed/non-relational workloads, model around aggregates and access paths; document **BASE** and consistency tradeoffs. 4. For relational transaction semantics, document **ACID** expectations explicitly. For distributed/non-relational tradeoffs, document consistency compromises explicitly. 5. Always document the three schema layers: **external schema**, **conceptual schema**, **internal schema**. 6. Treat integrity as first-class: entity, domain, referential, and business-rule integrity must be explicit. 7. Concurrency is never implicit: define transaction boundaries, locking strategy, and isolation level per critical flow. 8. Data standards are mandatory: naming, definition, format, allowed values, and validation rules. 9. Maintain living artifacts: glossary, schema decision log, and capacity estimation must be updated whenever the model changes. 10. Proactively flag anti-patterns and insecure shortcuts instead of silently implementing them. 11. If the design weakens auditability, least privilege, traceability, backup/recovery, or data integrity, propose ISO 27001 / 27002 / 22301-friendlier alternatives. 12. Vector DBs are retrieval infrastructure, not source-of-truth databases. Store embeddings and lightweight metadata there; keep canonical documents elsewhere. 13. Never treat vector search as a drop-in replacement for lexical search. Default to hybrid retrieval when exact match, compliance filtering, or explainability matters. 14. Embeddings are schema-like assets: version model, dimension, chunking, and preprocessing, and plan re-embedding migrations explicitly. 15. Retrieval quality is won at chunking, filtering, reranking, and observability, not only at the vector index layer. ## Default Workflow 1. **Explore** - Identify business entities, events, access patterns, volume, latency, retention, and recovery targets - Classify workload: OLTP, analytics, eventing, cache, search, mixed - Decide relational vs non-relational with explicit justification 2. **Design** - Produce external/conceptual/internal schema documentation - Model SQL or NoSQL structures, keys, indexes, constraints, and lifecycle fields - Define integrity, transaction scope, isolation level, and transparency requirements 3. **Optimize** - Validate 3NF or deliberate denormalization - Tune indexes, partitioning, archival strategy, hot/cold split, and backup plan - For vector systems, tune ANN, chunking, filtering, reranking, and observability as one pipeline - Run anti-pattern review and update glossary and capacity estimation with every structural change ## Required Deliverables - External schema summary by user/view/consumer - Conceptual schema with core entities or aggregates and relationships - Internal schema with physical storage, indexes, partitioning, and access paths - Data standards table: name, definition, type/format, rule - Glossary / terminology dictionary - Capacity estimation sheet - Backup and recovery strategy including full + incremental backup cadence - For vector/RAG systems: embedding version policy, chunking policy, hybrid retrieval strategy, and re-index / re-embedding plan ## How to Execute Follow `resources/execution-protocol.md` step by step. See `resources/examples.md` for input/output examples. Use `resources/document-templates.md` when you need concrete deliverable structure. Use `resources/anti-patterns.md` when reviewing or remediating logical, physical, query, and application-facing DB issues. Use `resources/vector-db.md` when the task involves vector databases, ANN tuning, semantic search, or RAG retrieval. Use `resources/iso-controls.md` when the user needs security-control, continuity, or audit-oriented DB recommendations. Before submitting, run `resources/checklist.md`. ## Execution Protocol (CLI Mode) Vendor-specific execution protocols are injected automatically by `oh-my-agent agent:spawn`. Source files live under `../_shared/runtime/execution-protocols/{vendor}.md`. ## References - Execution steps: `resources/execution-protocol.md` - Self-check: `resources/checklist.md` - Examples: `resources/examples.md` - Deliverable templates: `resources/document-templates.md` - Anti-pattern review guide: `resources/anti-patterns.md` - Vector DB and RAG guide: `resources/vector-db.md` - ISO control guide: `resources/iso-controls.md` - Error recovery: `resources/error-playbook.md` - Context loading: `../_shared/core/context-loading.md` - Reasoning templates: `../_shared/core/reasoning-templates.md` - Clarification: `../_shared/core/clarification-protocol.md` - Context budget: `../_shared/core/context-budget.md` - Lessons learned: `../_shared/core/lessons-learned.md` --- > Source: [gracefullight/kaltura-downloader](https://github.com/gracefullight/kaltura-downloader) — distributed by [TomeVault](https://tomevault.io).