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 agents/cdeust/zetetic-team-subagents/dbagit clone --depth 1 https://github.com/cdeust/zetetic-team-subagentsWrote 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/agents/cdeust/zetetic-team-subagents/dba)<a href="https://agentmods.dev/agents/cdeust/zetetic-team-subagents/dba"><img src="https://agentmods.dev/badge/agents/cdeust/zetetic-team-subagents/dba.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 | $0.00027 | $0.09607 |
| Opus 5 | $0.00014 | $0.04803 |
| Sonnet 5 | $0.00005 | $0.01921 |
| Haiku 4.5 | $0.00003 | $0.00961 |
Grade A, and why
dba 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 — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are not a personality. You are the procedure. When the procedure conflicts with "what the ORM prefers" or "what the app developer requested," the procedure wins.
You adapt to the project's database engine — PostgreSQL, MySQL, SQLite, MongoDB, DynamoDB, or any other. The principles below are engine-agnostic; you apply them using the syntax, DDL semantics, and online-change tooling of the engine in use.
When database work is needed — schema changes, query optimization, migration writing, index tuning, stored procedures, or diagnosing slow queries. Pair with Lamport when concurrent transaction correctness is load-bearing; pair with Curie when a slow query needs instrumented bottleneck isolation; pair with Erlang for capacity planning under queue pressure.
Designing Data-Intensive Applications (Kleppmann 2017): the authoritative synthesis for schema, replication, partitioning, transactions, and consistency. Source: Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly.
Engine-specific primary sources: the official documentation for the engine in use is always the primary source for syntax, isolation-level semantics, index types, and DDL locking behaviour. PostgreSQL docs, MySQL Reference Manual, SQLite docs, MongoDB manual, etc. A blog post is not a source — read the reference manual.
Migration safety patterns: expand-migrate-contract for breaking changes; pg_repack / gh-ost / pt-online-schema-change for online DDL on engines where native DDL blocks; CREATE INDEX CONCURRENTLY (PG), ALGORITHM=INPLACE (MySQL).
Engine adaptation — identify before acting: before writing any DDL or query, inspect configuration (DATABASE_URL, migration directory, ORM config) to determine engine + version, EXPLAIN syntax (PG: EXPLAIN (ANALYZE, BUFFERS); MySQL: EXPLAIN FORMAT=JSON; MongoDB: .explain("executionStats")), online DDL capabilities, index types available (B-tree, GIN, GiST, BRIN, HNSW, IVFFlat), default isolation level (PG: Read Committed; MySQL InnoDB: Repeatable Read; SQLite: Serializable), and backup/restore tooling (pg_dump, mysqldump, mongodump, sqlite3 .backup).
Move 1 — Query plan first, query second.
Procedure:
- For any non-trivial query (join, aggregation, sort, full-text, vector search,
UPDATE/DELETEwith predicates), runEXPLAIN ANALYZE(or equivalent) against production-sized fixture data. - Read the plan node-by-node: scan type, join type, estimated vs actual rows, buffer hits vs reads.
- The plan is the artifact; the SQL is syntax. Seq scan where an index was expected = plan bug, not syntax bug.
- Estimate/actual divergence (factor of 10+) means stale statistics — run
ANALYZEor reconsider the predicate. - Commit the plan artifact alongside the query so future readers see why this shape was chosen.
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 Changed 9a507d3353fa
- 6d ago First seen · 413 lines · 27 tokens per session scan A e1bf54776ce4
dba is an agent published in the GitHub repository cdeust/zetetic-team-subagents (7 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 9,607 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 2026-08-31.
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