Antigravity-awesome-skills vector-index-tuning

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

install

source · Clone the upstream repo

git clone https://github.com/benjaminasterA/antigravity-awesome-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/benjaminasterA/antigravity-awesome-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/vector-index-tuning" ~/.claude/skills/benjaminastera-antigravity-awesome-skills-vector-index-tuning && rm -rf "$T"

manifest: skills/vector-index-tuning/SKILL.md

source content

Vector Index Tuning

Guide to optimizing vector indexes for production performance.

Use this skill when

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

Do not use this skill when

You only need exact search on small datasets (use a flat index)
You lack workload metrics or ground truth to validate recall
You need end-to-end retrieval system design beyond index tuning

Instructions

Gather workload targets (latency, recall, QPS), data size, and memory budget.
Choose an index type and establish a baseline with default parameters.
Benchmark parameter sweeps using real queries and track recall, latency, and memory.
Validate changes on a staging dataset before rolling out to production.

Refer to

resources/implementation-playbook.md

for detailed patterns, checklists, and templates.

Safety

Avoid reindexing in production without a rollback plan.
Validate changes under realistic load before applying globally.
Track recall regressions and revert if quality drops.

Resources

```
resources/implementation-playbook.md
```
for detailed patterns, checklists, and templates.