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/bytebase/dbhub/explorenpx skills add bytebase/dbhub --skill exploregit clone --depth 1 https://github.com/bytebase/dbhubWhat 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 | $0.00024 | $0.00289 |
| Opus 5 | $0.00012 | $0.00144 |
| Sonnet 5 | $0.00005 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
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 yesterday.
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(orTOPon SQL Server) 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.
- yesterday First seen · 20 lines · 24 tokens per session scan A 087e361d1bc0
explore is a skill published in the GitHub repository bytebase/dbhub (3,431 stars, last pushed 11d ago), licensed MIT. It adds 24 tokens to every session and 289 once invoked, about $0.0001 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 2026-08-30.
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