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 amansingh63/dbhub-analytics --skill dbhubgit clone --depth 1 https://github.com/amansingh63/dbhub-analyticsWrote 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/amansingh63/dbhub-analytics/dbhub)<a href="https://agentmods.dev/skills/amansingh63/dbhub-analytics/dbhub"><img src="https://agentmods.dev/badge/skills/amansingh63/dbhub-analytics/dbhub.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 5.1 | $0.00098 | $0.01466 |
| Opus 5 | $0.00049 | $0.00733 |
| Sonnet 5 | $0.00020 | $0.00293 |
| Haiku 4.5 | $0.00010 | $0.00147 |
Grade A, and why
dbhub 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 6d 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.
This is a copy
86% identical to dbhub — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DBHub Database Query Guide
When working with databases through DBHub's MCP server, always follow the explore-then-query pattern. Jumping straight to SQL without understanding the schema is the most common mistake — it leads to failed queries, wasted tokens, and frustrated users.
Available Tools
DBHub provides two MCP tools:
| Tool | Purpose |
|---|---|
search_objects |
Explore database structure — schemas, tables, columns, indexes, procedures, functions |
execute_sql |
Run SQL statements against the database |
If multiple databases are configured, DBHub registers separate tools for each source (for example, search_objects_prod_pg, execute_sql_staging_mysql). Select the desired database by calling the correspondingly named tool.
The Explore-Then-Query Workflow
Every database task should follow this progression. The key insight is that each step narrows your focus, so you never waste tokens loading information you don't need.
Step 1: Discover what schemas exist
search_objects(object_type="schema", detail_level="names")
This tells you the lay of the land. Most databases have a primary schema (e.g., public in PostgreSQL, dbo in SQL Server) plus system schemas you can ignore.
Step 2: Find relevant tables
Once you know the schema, list its tables:
search_objects(object_type="table", schema="public", detail_level="names")
If you're looking for something specific, use a pattern:
search_objects(object_type="table", schema="public", pattern="%user%", detail_level="names")
The pattern parameter uses SQL LIKE syntax: % matches any characters, _ matches a single character.
If you need more context to identify the right table (row counts, column counts, table comments), use detail_level="summary" instead.
Step 3: Inspect table structure
Before writing any query, understand the columns:
search_objects(object_type="column", schema="public", table="users", detail_level="full")
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.
- 6d ago First seen · 149 lines · 98 tokens per session scan A 3d3a78b561f0
dbhub is a skill published in the GitHub repository amansingh63/dbhub-analytics (0 stars, last pushed 5mo ago), licensed MIT. It adds 98 tokens to every session and 1,466 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to dbhub, differing in 10 lines, and is treated as a copy.
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