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 selvarajmurugesan90/ops-engineering-skills --skill vector-database-configuration-validationgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-skillsWrote 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/selvarajmurugesan90/ops-engineering-skills/vector-database-configuration-validation)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/vector-database-configuration-validation"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/vector-database-configuration-validation.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00124 | $0.03836 |
| Opus 5 | $0.00062 | $0.01918 |
| Sonnet 5 | $0.00025 | $0.00767 |
| Haiku 4.5 | $0.00012 | $0.00384 |
Grade A, and why
vector-database-configuration-validation 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 — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Configuration Validation
Purpose
A vector index with the wrong dimension, distance metric, or HNSW parameter set for its embedding model doesn't fail loudly — it either rejects vectors with a dimension-mismatch error (the easy case) or, worse, silently accepts vectors and returns technically valid but degraded or nonsensical similarity results (the hard case, because nothing in the API response signals anything is wrong). This skill covers validating a vector index's configuration and actual query-performance behavior before it takes production traffic: confirming dimension and distance-metric agreement with the embedding model, running a labeled recall evaluation against real queries, and gating a cutover behind that evaluation rather than a visual "looks right" review. It assumes the index's operational tuning (sharding, replication, HNSW parameter selection) is handled in vector-database-operations-pinecone-weaviate-milvus and that vectors are arriving via vector-database-ingestion-pipeline-for-rag — this skill is specifically the pre-cutover validation gate sitting between those two.
When to use
- Before cutting a RAG system's retrieval traffic over to a new or reconfigured vector index (new embedding model, new HNSW parameters, new sharding scheme, vendor migration).
- Standing up a new index/collection and confirming its schema (dimension, distance metric, indexed metadata fields) actually matches what the embedding model and query patterns require.
- Retrieval quality (recall, relevance) degraded after a re-index or an embedding-model change, and you need to confirm whether the index configuration itself is the cause.
- Reviewing a Pinecone/Weaviate/Milvus index/collection definition in a PR before it's applied, to catch a dimension or metric mistake before it reaches production.
- Migrating a corpus to a new vendor or a new index within the same vendor, and needing a go/no-go gate before the alias/pointer swap.
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 · 328 lines · 124 tokens per session scan A 3085071b4754
vector-database-configuration-validation is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 124 tokens to every session and 3,836 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.
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