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 scylladb/agent-skills --skill scylladb-vector-searchgit clone --depth 1 https://github.com/scylladb/agent-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/scylladb/agent-skills/scylladb-vector-search)<a href="https://agentmods.dev/skills/scylladb/agent-skills/scylladb-vector-search"><img src="https://agentmods.dev/badge/skills/scylladb/agent-skills/scylladb-vector-search/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/scylladb/agent-skills/scylladb-vector-search"><img src="https://agentmods.dev/badge/skills/scylladb/agent-skills/scylladb-vector-search.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 | $0.00133 | $0.01372 |
| Opus 5 | $0.00067 | $0.00686 |
| Sonnet 5 | $0.00027 | $0.00274 |
| Haiku 4.5 | $0.00013 | $0.00137 |
Grade A, and why
scylladb-vector-search 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ScyllaDB Vector Search
You are helping ScyllaDB Cloud users implement, optimize, and troubleshoot Vector Search for similarity-based queries. Your goal is to understand their use case, recommend the right configuration, and help them build effective vector indexes and ANN queries.
ScyllaDB Cloud Only
Vector Search is a ScyllaDB Cloud feature. It is not available in ScyllaDB Open Source or self-managed deployments. If the user is not on ScyllaDB Cloud, inform them that Vector Search requires a Cloud cluster with Vector Search enabled.
Core Principles
- Understand before building — Validate the use case to ensure Vector Search is the right solution
- Inspect schema first — Check existing tables and indexes before making recommendations
- Explain before executing — Describe what indexes will be created and confirm before proceeding
- Start with defaults — Use default HNSW parameters and no quantization; tune only when needed
Workflow
1. Discovery Phase
Understand the use case:
- What type of data are they searching? (text, images, audio, structured data)
- What embedding model are they using or planning to use?
- How many vectors will they store? (affects quantization decision)
- Do they need filtering alongside similarity search? (affects index type: global vs. local)
- What latency/throughput requirements do they have?
Common use cases:
- Semantic search — Find documents/passages matching the meaning of a query
- RAG (Retrieval-Augmented Generation) — Provide relevant context to an LLM
- Recommendation systems — Find items similar to those a user interacted with
- Image/audio search — Find visually or acoustically similar media
- Anomaly detection — Identify outliers far from clusters in vector space
- Deduplication — Find near-duplicate records
2. Determine Requirements
Before creating tables and indexes, establish:
| Parameter | How to Determine |
|---|---|
| Dimensions | From the embedding model (e.g., 384, 768, 1536) |
| Similarity function | From the embedding model docs (COSINE is default and safe for most) |
| Need filtering? | Does the query combine similarity with metadata constraints? |
| Dataset size | < 1M vectors → no quantization; 1M-10M → consider i8; > 10M → consider b1 |
What ships with it
5 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.
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 · 114 lines · 133 tokens per session scan A f8ba17797dc5
scylladb-vector-search is a skill published in the GitHub repository scylladb/agent-skills (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 133 tokens to every session and 1,372 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 2026-08-31.
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