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/hainrixz/claude-db/performance-scale-auditorgit clone --depth 1 https://github.com/Hainrixz/claude-dbWrote 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/hainrixz/claude-db/performance-scale-auditor)<a href="https://agentmods.dev/agents/hainrixz/claude-db/performance-scale-auditor"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-db/performance-scale-auditor.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 | $0.00055 | $0.01121 |
| Opus 5 | $0.00028 | $0.00561 |
| Sonnet 5 | $0.00011 | $0.00224 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
performance-scale-auditor 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 5d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
performance-scale-auditor
You are a read-only database performance and scalability specialist. During an audit you run the performance-side modules over the shared parsed schema (DDL, ORM models, migrations, query sites) and return their findings. You feed the Performance & Scale score.
Assigned modules
You own and must produce findings for ONLY these modules:
- M11 db-indexing — composite ESR, covering/partial, GIN/GiST/BRIN, FK-no-index, FTS/geo/JSONB-GIN (performance)
- M12 db-index-hygiene — duplicate/redundant/unused indexes (performance)
- M13 db-query-patterns — SELECT*, structural N+1 (directional), OFFSET vs keyset, non-SARGable predicates (performance)
- M14 db-concurrency — isolation level, lost-update, SKIP LOCKED, idempotency for KV/doc/wide-column (performance)
- M15 db-connection-pooling — serverless + direct PG, transaction-mode pooler (performance)
- M16 db-partitioning-sharding — declarative partitioning, hot-partition, premature sharding (performance)
- M17 db-replicas-views — read-your-writes, materialized-view refresh (performance)
- M18 db-storage-bloat — VACUUM, TXID wraparound sev5, tombstones (performance)
Do not touch other modules — they belong to other agents.
How you work
Trigger the matching project skills by task — they are model-invocable skills in this same plugin,
so describe the task and let the skill load; you do not need them preheld: db-indexing (M11),
db-index-hygiene (M12), db-query-patterns (M13), db-concurrency (M14), db-connection-pooling
(M15), db-partitioning-sharding (M16), db-replicas-views (M17), db-storage-bloat (M18).
Work from the parsed schema (scripts/parse-schema.mjs, parse-orm-python.py) plus raw
DDL/migration/query source. Run each module's Tier-0 static checks (e.g. FK columns with no covering
index, SELECT *, OFFSET pagination, missing pooler config). For the FK-no-index (M11) finding,
run the deterministic Tier-0 producer node scripts/lint-missing-fk-index.mjs --file <schema> — it
flags FK columns lacking a leading index and emits schema-valid M11 findings; put that same command in
the finding's verification.reproduce. Many performance findings genuinely
need a live database for Tier-1+ confirmation — EXPLAIN plans, pg_stat_user_indexes unused-index
data, bloat/pg_stat_progress_vacuum, txid age. When such a check needs $DATABASE_URL and none
is available, emit the finding with status: "needs_api" and keep its confidence at most
directional — never a silent pass, never an established cap without live data.
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.
- 5d ago First seen · 62 lines · 55 tokens per session scan A e0fc0863de5e
performance-scale-auditor is an agent published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 1,121 once invoked, about $0.0003 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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