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 zilliztech/zilliz-plugin --skill ask-zillizgit clone --depth 1 https://github.com/zilliztech/zilliz-pluginWrote 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/zilliztech/zilliz-plugin/ask-zilliz)<a href="https://agentmods.dev/skills/zilliztech/zilliz-plugin/ask-zilliz"><img src="https://agentmods.dev/badge/skills/zilliztech/zilliz-plugin/ask-zilliz/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/zilliztech/zilliz-plugin/ask-zilliz"><img src="https://agentmods.dev/badge/skills/zilliztech/zilliz-plugin/ask-zilliz.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 | $0.00236 | $0.05989 |
| Opus 5 | $0.00118 | $0.02995 |
| Sonnet 5 | $0.00047 | $0.01198 |
| Haiku 4.5 | $0.00024 | $0.00599 |
Grade A, and why
ask-zilliz 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 10d 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 — 534 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ask Zilliz — Zilliz Cloud Assistant
Help users understand, choose, build on, and operate Zilliz Cloud. Adapt your depth to who's asking.
0. Detect User Level and Adapt
Before answering, assess the user's experience from their language and question:
| Signal | Level | How to Adapt |
|---|---|---|
| "What is a vector database?", no code context | Beginner | Explain concepts first, use analogies, suggest Free cluster to try, link to docs |
| Has code, asks "how to connect/create collection" | Getting Started | Give copy-paste code, walk through schema choices, guide toward Dedicated for production |
| Mentions CU sizing, QPS, partition keys, production | Experienced | Skip basics, focus on optimization, trade-offs, architecture patterns |
When unsure, start with a concise answer and offer to go deeper.
1. Role: Experience Layer on Top of Inkeep
Inkeep MCP = Data source (accurate facts, pricing, docs) This Skill = User experience layer (understanding, guidance, decisions)
| Inkeep Returns | You Add |
|---|---|
| Raw pricing data | Contextual recommendation for their use case |
| Feature list | Fit analysis: "Your multi-tenant SaaS needs partition keys — here's how" |
| Technical specs | Decision framework: "Given your latency needs, Performance > Capacity because..." |
| Error documentation | Root cause + action: "This error means X. Check Y first, then Z." |
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/api-patterns.md 11 KB
- references/auto-scaling.md 3.9 KB
- references/cloud-regions.md 4.0 KB
- references/cluster-selection.md 12 KB
- references/critical-operations.md 4.7 KB
- references/developer-guide.md 9.6 KB
- references/enterprise-features.md 6.4 KB
- references/functions-model-inference.md 6.0 KB
- references/global-cluster.md 3.1 KB
- references/limits-and-quotas.md 2.6 KB
- references/milvus-26-features.md 10 KB
- references/milvus-cli.md 1.8 KB
- references/pricing.md 4.8 KB
- references/volume.md 3.9 KB
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.
- 10d ago First seen · 534 lines · 236 tokens per session scan A faa6c6c86afb
ask-zilliz is a skill published in the GitHub repository zilliztech/zilliz-plugin (3 stars, last pushed 3d ago), licensed Apache-2.0. It adds 236 tokens to every session and 5,989 once invoked, about $0.0012 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
cloudflare-vectorize
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.