oh-my-agent is a harness for checking whether coding agents actually completed their work by verifying tests, required artifacts, independent reviews, and recorded decisions. It is used across multiple agent runtimes to make workflow results auditable instead of relying on an agent's own report. The catalogue add-ons provide parts of its skills, agents, hooks, MCP integrations, instructions, and plugins.
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 first-fluke/oh-my-agent --skill oma-dbgit clone --depth 1 https://github.com/first-fluke/oh-my-agentWrote 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/oh-my-agent/oma-db)<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-db"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-db/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-db"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-db.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00027 | $0.02717 |
| Opus 5.5 | $0.00011 | $0.01087 |
| Sonnet 5.5 | $0.00005 | $0.00543 |
| Haiku 4.5 | $0.00003 | $0.00272 |
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 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 — 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
- Application ownership recommendations, alternatives, transition priorities, and technical retention evidence when DB business logic is affected
- 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 12 KB
- resources/checklist.md 4.4 KB
- resources/document-templates.md 2.1 KB
- resources/error-playbook.md 3.1 KB
- resources/examples.md 7.3 KB
- resources/execution-protocol.md 6.7 KB
- resources/iso-controls.md 3.3 KB
- resources/migration-playbook.md 7.2 KB
- resources/query-tuning.md 4.5 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.
- 8d ago Changed · +7 lines f1664e8655bd
- 20d ago Changed · +4 lines 8e0364ca5d26
- 1mo ago Changed · -6 lines · -84 tokens per session 4811e35b59de
- 1mo ago Changed · -5 lines 3fef4375037a
- 1mo ago Changed 51bf84680917
- 1mo ago First seen · 200 lines · 111 tokens per session scan A 480be5c534d3
oma-db is a skill published in the GitHub repository first-fluke/oh-my-agent (1,354 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 2,717 once invoked, about $0.0001 per session on Opus 5.5. 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.
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