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 Hainrixz/claude-db --skill auditgit 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/skills/hainrixz/claude-db/audit)<a href="https://agentmods.dev/skills/hainrixz/claude-db/audit"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/audit.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 | $0.00126 | $0.00762 |
| Opus 5 | $0.00063 | $0.00381 |
| Sonnet 5 | $0.00025 | $0.00152 |
| Haiku 4.5 | $0.00013 | $0.00076 |
Grade A, and why
audit 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 7d 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 — 24 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/claude-db:audit
A full, read-only database audit. Never writes files, never mutates the database — even at Tier 1/2 it runs only read-only verification queries.
$ARGUMENTS = <path|connection-target> [flags]. The target is a repo path (schema/ORM/migration files) and, optionally, a live database via $DATABASE_URL for Tier-1/2 verification. If no target is given and no schema/ORM/migration artifacts are found in the working directory, do not fabricate a pass: say so plainly and suggest /claude-db:start (guided wizard, zero artifacts) or /claude-db:design (greenfield).
What to do
- Invoke the db-orchestrator skill with the target and flags. It detects the stack (
scripts/detect-stack.mjs— paradigm/engine/ORM/platform), parses the schema (scripts/parse-schema.mjs/parse-orm-python.py), records the data tier reached (0/1/2), dispatches the read-only auditor subagents in parallel, merges findings, and runs db-score. - Present:
- The two scores — Design & Integrity and Performance & Scale — each with a band (A–F) and a one-line interpretation. Show the uncapped
computedalongside anycapped:true(afail+severity:5on that axis caps it at F). Never blend the two. - A per-category breakdown for each score (value, weight, active?), the detected paradigm/engine, the tier reached, and the count of
needs_apichecks (score confidence — never a silent pass). - If multiple datastores were detected, the worst-of-across-stores roll-up per axis with the flooring store named, plus the per-store breakdown.
- A prioritized action list sorted by impact: each item with status, severity, evidence (
evidence.observedquoting real DDL/query with secrets redacted), recommendation, fixability (auto/proposed/advisory), andexpected_impact(axis + confidence + magnitude, banded high|medium|low — never a fabricated %/latency/row-count).
- The two scores — Design & Integrity and Performance & Scale — each with a band (A–F) and a one-line interpretation. Show the uncapped
- End by offering: "Run
/claude-db:fixto apply the safe, reversible changes (you confirm each one),/claude-db:migrateto lint a migration, or/claude-db:nextto see what to tackle first."
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.
- 7d ago First seen · 24 lines · 126 tokens per session scan A 46d994f4b4cc
audit is a skill published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 126 tokens to every session and 762 once invoked, about $0.0006 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.
Other skills, from other repositories
dynamodb
Use when modeling or operating a DynamoDB table: deriving partition/sort keys from access patterns, single-table vs table-per-entity, adding a GSI/LSI, on-demand vs provisioned capacity, or diagnosing hot-partition throttling. NOT relational schema/SQL/EXPLAIN (that is postgresdb), NOT aggregation-pipeline document…
malloy-lookml-review
Analyze LookML files as prior art for Malloy modeling. Used during Step 1 (DISCOVER) when .lkml files are present. Coordinates reference files that extract business logic, relationships, and curation decisions. Works with or without a database connection.
convex-migrations
Schema migration strategies for evolving applications including adding new fields, backfilling data, removing deprecated fields, index migrations, and zero-downtime migration patterns.
database-design-patterns
Database schema design patterns and optimization strategies for relational and NoSQL databases. Use when designing database schemas, optimizing query performance, or implementing data persistence layers at scale.
database-sql
Design database schemas, write efficient SQL queries, create migrations, and optimize database performance. Use when working with databases, writing queries, or designing data models.
data-design
Data modeling, schema design, and data architecture.