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 agentmods add skills/first-fluke/fullstack-starter/oma-dbnpx skills add first-fluke/fullstack-starter --skill oma-dbgit clone --depth 1 https://github.com/first-fluke/fullstack-starterWrote 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/first-fluke/fullstack-starter/oma-db)<a href="https://agentmods.dev/skills/first-fluke/fullstack-starter/oma-db"><img src="https://agentmods.dev/badge/skills/first-fluke/fullstack-starter/oma-db.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00111 | $0.02713 |
| Opus 5 | $0.00056 | $0.01357 |
| Sonnet 5 | $0.00022 | $0.00543 |
| Haiku 4.5 | $0.00011 | $0.00271 |
Grade A, and why
oma-db 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 today.
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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- resources/anti-patterns.md 6.9 KB
- resources/checklist.md 3.4 KB
- resources/document-templates.md 2.1 KB
- resources/error-playbook.md 3.1 KB
- resources/examples.md 5.6 KB
- resources/execution-protocol.md 6.1 KB
- resources/iso-controls.md 3.2 KB
- resources/migration-playbook.md 5.2 KB
- resources/query-tuning.md 4.4 KB
- resources/vector-db.md 3.9 KB
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.
- today First seen · 200 lines · 111 tokens per session scan A 480be5c534d3
oma-db is a skill published in the GitHub repository first-fluke/fullstack-starter (222 stars, last pushed 3d ago), licensed MIT. It adds 111 tokens to every session and 2,713 once invoked, about $0.0006 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 2026-09-03.
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