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 collectiongit 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/collection)<a href="https://agentmods.dev/skills/zilliztech/zilliz-plugin/collection"><img src="https://agentmods.dev/badge/skills/zilliztech/zilliz-plugin/collection/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/collection"><img src="https://agentmods.dev/badge/skills/zilliztech/zilliz-plugin/collection.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 | $0.00032 | $0.01691 |
| Opus 5.5 | $0.00013 | $0.00676 |
| Sonnet 5.5 | $0.00006 | $0.00338 |
| Haiku 4.5 | $0.00003 | $0.00169 |
Grade A, and why
collection 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 1mo 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prerequisites
- CLI installed, logged in, and cluster context set (see setup skill).
Commands Reference
All collection commands accept an optional --database <db-name> flag to target a non-default database. If omitted, the database from the current context is used.
Collection metrics
Query per-collection metrics against POST /v2/clusters/{clusterId}/metrics/query with collectionName set in the request body. Mirrors the web console's Collection Detail > Metrics page.
zilliz collection metrics --collection-name <collection-name> --metric <metric-name>
# Optional:
# --cluster-id <cluster-id> Override cluster context
# --period <duration> e.g. 1h, 24h, 7d (mutually exclusive with --start/--end)
# --start <iso-8601> --end <iso-8601>
# --granularity <duration> / -g e.g. 30s, 5m, 1h (auto-selected if omitted)
# -o table Render pivot table instead of the default inline chart
The default output is an inline text chart: one block per metric with a summary line (min / max / avg / last) and a Braille-rendered line chart. Pass an explicit -o table (or --output table) to render the pivot-table layout — useful for terminals without good Braille font support. -o json, -o yaml, -o csv, and --query always bypass both renderers and return the raw response.
Examples:
# Period shorthand: last hour of SEARCH_QPS for a collection
zilliz collection metrics -c my_coll -m SEARCH_QPS --period 1h
# Explicit range with 1h granularity
zilliz collection metrics -c my_coll -m ENTITIES_LOADED \
--start 2026-04-13T00:00:00Z --end 2026-04-14T00:00:00Z -g 1h
# Multiple metrics in a single call
zilliz collection metrics -c my_coll -m SEARCH_QPS -m SEARCH_LATENCY_P99 --period 6h
Metric scope
Each metric is tagged with a scope. zilliz collection metrics only accepts metrics whose scope is Collection or Both. Using a Cluster-only metric emits:
Metric '<NAME>' is cluster-scope only and cannot be used with --collection-name.
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.
- 1mo ago First seen · 203 lines · 32 tokens per session scan A 9833b802b198
collection is a skill published in the GitHub repository zilliztech/zilliz-plugin (3 stars, last pushed 2d ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,691 once invoked, about $0.0001 per session on Opus 5.5. 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…
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.
chromadb
Build and operate Chroma collections for RAG: PersistentClient vs HttpClient, embedding-function contracts, metadata/where filters, and dimension-safe upsert/query paths agents routinely break.
retrieve
Search a knowledge base using Amazon Bedrock RAG (Retrieval-Augmented Generation). Use when users want to find information from uploaded documents, ask questions about stored knowledge, or retrieve relevant content from the knowledge base. Returns matched content with source references and URLs.