DBHub is a lightweight Model Context Protocol server that lets compatible AI clients connect to and explore PostgreSQL, MySQL, MariaDB, SQL Server, and SQLite databases through one interface. Developers and other users employ it for schema inspection, query validation, debugging, and controlled read-only database access; the catalogue entries provide agent skills, instructions, an MCP configuration, and a plugin.
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 bytebase/dbhub --skill exploregit clone --depth 1 https://github.com/bytebase/dbhubWrote 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/bytebase/dbhub/explore)<a href="https://agentmods.dev/skills/bytebase/dbhub/explore"><img src="https://agentmods.dev/badge/skills/bytebase/dbhub/explore/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/bytebase/dbhub/explore"><img src="https://agentmods.dev/badge/skills/bytebase/dbhub/explore.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 | $0.00024 | $0.00298 |
| Opus 5.5 | $0.00010 | $0.00119 |
| Sonnet 5.5 | $0.00005 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
explore 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 27d 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.
What it actually says
DBHub Schema Exploration
Use search_objects with progressive disclosure — always coarse to fine:
- Orient:
object_type: "table",detail_level: "names"(pattern defaults to%, matching all). Cheap even on large schemas. - Narrow:
detail_level: "summary"for the tables that matter (row/column counts, comments), using a pattern like%order%rather than listing everything. - Drill in:
detail_level: "full"only for the tables you will actually query (complete columns, types, keys) — never as a first, pattern-less call on an unfamiliar schema.
Searching object_type: "column" with a pattern like %email% is the fastest way to find which tables hold a concept.
Querying
- Always add
LIMIT(TOPon SQL Server,FETCH FIRST n ROWS ONLYon Oracle) when sampling data — start withLIMIT 10. Prefer aggregates (COUNT,GROUP BY) over pulling rows to characterize data. - Check a table's
fulldetail before writing JOINs so you use real column names — don't guess and retry. - This plugin is read-only; if a write is rejected, tell the user rather than trying to work around it.
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.
- 27d ago Changed bd4d44b884c6
- 1mo ago First seen · 20 lines · 24 tokens per session scan A 087e361d1bc0
explore is a skill published in the GitHub repository bytebase/dbhub (3,664 stars, last pushed 3d ago), licensed MIT. It adds 24 tokens to every session and 298 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.
Other skills, from other repositories
Audit Database
Comprehensive audit of the Supabase PostgreSQL database. Use when asked to audit, review, or assess the database — covering schema design, RLS policies, multi-tenancy isolation, indexes, migrations, data integrity, and query performance.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
event-store-design
Design and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing event persistence patterns.
cqrs-implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
amazon aurora dsql
Deprecated compatibility redirect for Aurora DSQL guidance. Use when a request concerns DSQL, Aurora DSQL, distributed SQL, DSQL schemas, migrations, queries, authentication, performance, or application development.