Skills vector-index-tuning

📦

vector-index-tuning

Name: vector-index-tuning
Author: sickn33

Content revision r1 Safe 📁 Filesystem access⚙️ External commands

Tune Vector Search Indexes

Vector search tuning can waste memory and hide recall regressions. This skill gives practical benchmarks, parameter guidance, and monitoring checks for production indexes.

Supports: Claude Codex Code(CC)

🥉 79 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "vector-index-tuning" from https://skillstore.io/skills/sickn33-vector-index-tuning.md and its manifest at https://skillstore.io/api/skills/sickn33-vector-index-tuning/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-vector-index-tuning.md Signed manifest GET /api/skills/sickn33-vector-index-tuning/manifest Signed lockfile GET /api/skills/sickn33-vector-index-tuning/lockfile

Test it

Using "vector-index-tuning". Need 95 percent recall for 2 million 768-dimensional vectors with 12 GB memory.

Expected outcome:

Start with HNSW, M near 32, efSearch near 128, and scalar quantization. Validate recall before reducing memory further.

Using "vector-index-tuning". Current search is fast but recall dropped after quantization.

Expected outcome:

Check ground truth recall, enable rescoring, raise oversampling, and compare against an unquantized baseline before rollout.

Using "vector-index-tuning". Memory use is too high for 50 million vectors.

Expected outcome:

Estimate storage by precision, test product quantization, and consider disk-backed segments only after measuring p95 latency.

Security Audit

Safe

v4 • 7/7/2026 Open versioned report

All seven static findings are false positives. The memmap_threshold values are Qdrant configuration examples, Markdown backticks are file references, and no prompt injection or command execution evidence was found.

Files scanned

551

Lines analyzed

Review items

False positives ignored

Risk Factors

📁 Filesystem access (4)

resources/implementation-playbook.md:325 resources/implementation-playbook.md:329 resources/implementation-playbook.md:333 resources/implementation-playbook.md:337

⚙️ External commands (2)

SKILL.md:32 SKILL.md:42

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Open versioned report

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https://skillstore.io/skills/sickn33-vector-index-tuning/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). vector-index-tuning security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-vector-index-tuning/audits/4

BibTeX citation

@techreport{sickn33-sickn33-vector-index-tuning-2026,
  author = {sickn33},
  title = {vector-index-tuning security audit report (audit version 4)},
  institution = {Skillstore},
  year = {2026},
  number = {4},
  url = {https://skillstore.io/skills/sickn33-vector-index-tuning/audits/4},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "vector-index-tuning security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
  - name: "sickn33"
date-released: "2026-07-07"
url: "https://skillstore.io/skills/sickn33-vector-index-tuning/audits/4"
identifiers:
  - type: other
    value: "skillstore:sickn33-vector-index-tuning:audit:4"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

sickn33 Recommended Current

sickn33-vector-index-tuning

Skillstore Score 79

Evidence Confidence High

Skillstore usage 10

Updated

2026-08-21

wshobson

wshobson-vector-index-tuning

Skillstore Score 77

Evidence Confidence High

Skillstore usage 16

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Select Production Index Settings

Choose HNSW and quantization settings that match recall, latency, memory, and build-time targets.

Plan Benchmark Sweeps

Create a repeatable benchmark plan for parameter sweeps using real queries and ground truth.

Control Vector Memory Costs

Estimate memory use and evaluate compression strategies before scaling vector collections.

Try These Prompts

Pick a Starting Configuration

Pick a starting index configuration for <number> vectors with <dimensions> dimensions. Target <recall> recall, <latency> milliseconds, and <memory> GB memory.

Compare HNSW Tradeoffs

Compare HNSW parameter options for my workload: <vectors>, <dimensions>, <QPS>, <recall target>, and <latency target>. Explain tradeoffs.

Design a Benchmark Plan

Design a benchmark plan for vector index tuning using real queries, ground truth, and staging data. Include metrics and rollout gates.

Review a Qdrant Tuning Plan

Review this Qdrant tuning plan for recall, speed, and memory risk: <plan>. Identify likely regressions and safer alternatives.

Best Practices

Benchmark with real queries and representative ground truth before changing production settings.
Track recall, latency percentiles, QPS, build time, and memory for every tuning run.
Roll out index changes through staging with rollback and comparison against a baseline.

Avoid

Tuning parameters without a baseline or success target.
Optimizing latency while ignoring recall loss.
Reindexing production collections without staged validation or rollback.

Frequently Asked Questions

What vector databases does this skill cover?

The examples include HNSW concepts and Qdrant configuration patterns. The tuning principles also apply to similar vector search systems.

Can it choose exact parameter values?

It can suggest starting ranges. Final values require benchmarks with your data, queries, and latency goals.

Does it automate benchmarking?

No. It provides benchmark templates and measurement plans that you run in your own environment.

When should I use quantization?

Use quantization when memory or cost is a constraint. Measure recall loss before enabling it broadly.

Can Claude, Codex, or Claude Code use it?

Yes. The report declares support for Claude, Codex, and Claude Code.

What inputs should I provide?

Provide vector count, dimensions, target recall, latency budget, QPS, memory budget, and representative query sets.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/vector-index-tuning

Ref

f93e9bb0daca99badb6a7e574b97737155d57cb3

Maintenance freshness

7/18/2026

Usage

8 downloads · 102 views

File structure

📁 resources/

📄 implementation-playbook.md

📄 SKILL.md