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 designgit 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/design)<a href="https://agentmods.dev/skills/hainrixz/claude-db/design"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/design.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 | $0.00081 | $0.00922 |
| Opus 5 | $0.00041 | $0.00461 |
| Sonnet 5 | $0.00016 | $0.00184 |
| Haiku 4.5 | $0.00008 | $0.00092 |
Grade A, and why
design 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 8d 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/claude-db:design
Greenfield engine choice + starter model. This is module M0 (engine-selection): a recommendation, not a score — /claude-db:design never produces the two audit scores and never writes to a database.
$ARGUMENTS = a plain-language description of the project (the data, the access patterns, the scale, any constraints). If it's too thin to choose well, ask 2–3 sharp questions first — or hand off to /claude-db:start for the full guided wizard.
What to do
- Walk the M0 decision tree (
references/engine-selection-tree.md; see alsoreferences/detection-signals.mdanddata-tiers.mdfor the paradigm signals): from the access patterns and shape of the data, narrow to a paradigm (relational / document / key-value / wide-column / vector / time-series / graph), then to a concrete engine. - Recommend an engine — and always compare it against the boring default (Postgres for most app workloads). State plainly when the boring default wins (it usually does) and what specific, concrete need would justify reaching for something else. Be honest about lock-in and operational cost; never fabricate prices, latency, throughput, or benchmark numbers — describe trade-offs qualitatively or mark
needs_apiif a real figure is required. - Hand back a starter data model for the recommended engine — core entities, keys (UUIDv7/ULID/bigint as appropriate, never floats for money, timestamptz/UTC), the obvious relationships/embeddings, and the constraints/indexes a sane first migration would include.
- Draw a paradigm-aware diagram with
node scripts/gen-diagram.mjs --file <schema> [--paradigm relational|document|key-value|wide-column|graph](paradigm-aware: ERD for relational, access-pattern map for document, key+GSI sketch for DynamoDB/KV, node/edge for graph).
Format — novice-first, with an expandable technical layer
- Lead with a plain, novice-friendly explanation: which database, in one sentence, and why — no jargon up front.
- Then an expandable technical layer: the DDL/collection spec, index choices, key strategy, and the design-rule rationale (which audit modules each choice satisfies, e.g. M2 keys, M4 types, M11 indexing) for the reader who wants depth.
- Close by offering: "When you have a first schema, run
/claude-db:auditto score it on Design & Integrity and Performance & Scale."
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.
- 8d ago First seen · 32 lines · 81 tokens per session scan A f35b3c49c0ac
design is a skill published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 922 once invoked, about $0.0004 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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