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-operations-pinecone-weaviate-milvusgit 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-operations-pinecone-weaviate-milvus)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/vector-database-operations-pinecone-weaviate-milvus"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/vector-database-operations-pinecone-weaviate-milvus/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/selvarajmurugesan90/ops-engineering-skills/vector-database-operations-pinecone-weaviate-milvus"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/vector-database-operations-pinecone-weaviate-milvus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 74 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 338 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00127 | $0.04031 |
| Opus 5 | $0.00063 | $0.02015 |
| Sonnet 5 | $0.00025 | $0.00806 |
| Haiku 4.5 | $0.00013 | $0.00403 |
Grade A, and why
vector-database-operations-pinecone-weaviate-milvus 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 11d 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 — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Operations (Pinecone, Weaviate, Milvus)
Purpose
A vector database's default configuration works fine for a demo and starts showing real operational pain exactly when it matters most: at production scale, under real query load, with a corpus that keeps growing. This skill covers operating a vector database day to day — configuring the index correctly for its workload, scaling it horizontally (sharding) and for availability (replication), and tuning upsert and query performance — with concrete, comparable guidance across the three most common choices (Pinecone as a managed service, Weaviate and Milvus as commonly self-hosted or managed alternatives). It assumes the index schema and dimension are already correct and validated (see vector-database-configuration-validation) and that data is already flowing in via an ingestion pipeline (see vector-database-ingestion-pipeline-for-rag); this skill is specifically the operate-and-tune layer underneath a RAG system's retrieval stage (see rag-pipeline-design for the retrieval pattern itself, which this skill doesn't repeat).
When to use
- Standing up a new vector index/collection in Pinecone, Weaviate, or Milvus and choosing its core configuration.
- Query latency has degraded as the corpus or query volume has grown, and it needs concrete tuning, not just "add more hardware."
- Deciding how to shard or partition a large corpus (by tenant, by data recency, by content type) across index namespaces/collections.
- Setting up replication for read throughput or availability during upgrades/maintenance.
- Upserts are slow, timing out, or backing up during a bulk load or a re-indexing run.
- Capacity planning before a corpus grows significantly (more documents, more tenants, higher QPS).
Prerequisites & environment
- A known embedding dimension and distance metric already fixed for the corpus (changing either requires a full re-embed and a new index, not a config tweak — see vector-database-configuration-validation).
- Estimated corpus size (vector count), expected query QPS, and expected write (upsert) rate — sizing decisions below depend on having real numbers, not guesses.
- For Pinecone: an account with pod-based or serverless index access (capacity/scaling mechanics differ between the two — check current Pinecone documentation for which applies to your plan, since this has changed over time).
- For Weaviate/Milvus: a self-hosted or managed cluster with enough nodes to support the replication/sharding plan you choose — these are self-operated systems, so cluster sizing is your responsibility in a way it isn't with a fully managed Pinecone index.
- Monitoring for index-level metrics (query latency, upsert throughput, index/memory fullness) wired to a dashboard — see prometheus-and-grafana-monitoring-stack for the metrics-pipeline mechanics if self-hosting Weaviate/Milvus.
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.
- 11d ago First seen · 339 lines · 127 tokens per session scan A 1869ddb3e137
vector-database-operations-pinecone-weaviate-milvus is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (38 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 127 tokens to every session and 4,031 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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