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 db-specialized-fitgit 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/db-specialized-fit)<a href="https://agentmods.dev/skills/hainrixz/claude-db/db-specialized-fit"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/db-specialized-fit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hainrixz/claude-db/db-specialized-fit"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/db-specialized-fit.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00134 | $0.01560 |
| Opus 5 | $0.00067 | $0.00780 |
| Sonnet 5 | $0.00027 | $0.00312 |
| Haiku 4.5 | $0.00013 | $0.00156 |
Grade A, and why
db-specialized-fit 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 12d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
db-specialized-fit (M20) — vector · time-series · graph · search
M20 covers purpose-built engines whose correctness depends on parameters a generic relational audit
ignores. Sub-modules carry a letter (M20a..M20d); the scorer maps them to the parent M20 in the
paradigm profile (Vector / Time-series / Graph categories in references/scoring-model.md).
M20a — Vector (pgvector, Qdrant, Pinecone, Weaviate)
Feeds: design (Métrica & dimensión, Modelo-version, Metadata/filtro) + performance (Índice & params, Búsqueda filtrada, Recall-vs-latencia).
- Dimension match — column/collection dim equals the embedding model's output dim
(e.g.
vector(1536)for text-embedding-3-small). A mismatch is a hard bug (design, sev 5). - Distance metric match — the index metric (cosine / L2 / inner-product) matches how the model was
trained; a mismatch silently wrecks recall. Sev-5 only when the model is declared in-repo; else
directionaland do not cap (per scoring-model honesty rule). - Index present & tuned — HNSW (
m,ef_construction,ef_search) or IVFFlat (lists/probes) declared, not a brute-force seq scan on a large table (performance). - Model version captured — embeddings are tied to a model version so a re-embed is possible (design).
- Filtered search — metadata used for pre/post-filtering is itself indexed (performance).
M20b — Time-series / OLAP (TimescaleDB, InfluxDB, ClickHouse)
Feeds: design (Hypertable-fit, Retención, Precisión-ts & tz) + performance (Chunk/retención, Continuous-agg, Compresión, Query).
- Hypertable / partition fit — large append-only time data is a hypertable / partitioned by time, with a sane chunk interval (not one giant chunk, not millions of tiny ones).
- Continuous aggregates / rollups declared for dashboard queries instead of scanning raw rows.
- Compression / TTL retention policy present for cold chunks; raw retention bounded.
- Timestamp precision & timezone —
timestamptz/UTC, not naive local time (shares the M4 rule).
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.
- 12d ago First seen · 82 lines · 134 tokens per session scan A 2e4945792e73
db-specialized-fit is a skill published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 134 tokens to every session and 1,560 once invoked, about $0.0007 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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