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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.

techwavedev/agi-agent-kit0installs4stars

SKILL.md

Full skill instructions

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

  1. Gather workload targets (latency, recall, QPS), data size, and memory budget.
  2. Choose an index type and establish a baseline with default parameters.
  3. Benchmark parameter sweeps using real queries and track recall, latency, and memory.
  4. 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.
<!-- AGI-INTEGRATION-START -->

AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

# Check for prior backend/​API context before starting
python3 execution/​memory_manager.py auto --query "API design patterns and architecture decisions for Vector Index Tuning"

Storing Results

After completing work, store backend/​API decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/​min per tenant" \
  --type decision --project <project> \
  --tags vector-index-tuning backend

Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>

Agent Team: Code Review

After implementation, dispatch code_review_team for two-stage review (spec compliance + code quality) before merging.

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