Atlas / Skills / wshobson / Vector Index Tuning

Vector Index TuningSAFE

skills/wshobson/vector-index-tuning

Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
40,146
01

Overview

Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi

Read from source at commit adb71e0b2512OBSERVED · 2026-10-02
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: vector-index-tuning
description: Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
---

# Vector Index Tuning

Guide to optimizing vector indexes for production performance.

## When to Use This Skill

- Tuning HNSW parameters
- Implementing quantization
- Optimizing memory usage
- Reducing search latency
- Balancing recall vs speed
- Scaling to billions of vectors

## Core Concepts

### 1. Index Type Selection

```
Data Size           Recommended Index
────────────────────────────────────────
< 10K vectors  →    Flat (exact search)
10K - 1M       →    HNSW
1M - 100M      →    HNSW + Quantization
> 100M         →    IVF + PQ or DiskANN
```

### 2. HNSW Parameters

| Parameter          | Default | Effect                                               |
| ------------------ | ------- | ---------------------------------------------------- |
| **M**              | 16      | Connections per node, ↑ = better recall, more memory |
| **efConstruction** | 100     | Build quality, ↑ = better index, slower build        |
| **efSearch**       | 50      | Search quality, ↑ = better recall, slower search     |

### 3. Quantization Types

```
Full Precision (FP32): 4 bytes × dimensions
Half Precision (FP16): 2 bytes × dimensions
INT8 Scalar:           1 byte × dimensions
Product Quantization:  ~32-64 bytes total
Binary:                dimensions/8 bytes
```

## Templates and detailed worked examples

Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.

## Best Practices

### Do's

- **Benchmark with real queries** - Synthetic may not represent production
- **Monitor recall continuously** - Can degrade with data drift
- **Start with defaults** - Tune only when needed
- **Use quantization** - Significant memory savings
- **Consider tiered storage** - Hot/cold data separation

### Don'ts

- **Don't over-optimize early** - Profile first
- **Don't ignore build time** - Index updates have cost
- **Don't forget reindexing** - Plan for maintenance
- **Don't skip warming** - Cold indexes are slow
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (1)

LOWInventory / provenance · inv.symlink · CWE-1104
CLAUDE.md
CLAUDE.md
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-10-02 · audit v0.4.1 · source sha adb71e0b2512full audit observations/trust-audit/skill/wshobson__vector-index-tuning.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-02adb71e0b2512SAFEB89first audit
05

Questions

What does the Vector Index Tuning skill do?

Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi

Is Vector Index Tuning safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Vector Index Tuning access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (adb71e0b2512), read on 2026-10-02. The repository is watched, and a new audit runs when it changes — this is the first audit.

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