.cursor/skills/debug-verify-benchmark/SKILL.md
Debug, verify, and compare elix-db to industry after each plan step. Use after implementing any plan step or changing vector/store/API logic; run tests, IEx checks, and document efficiency vs Qdrant/Milvus/pgvector.
npx skillsauth add 8dazo/elix-db debug-verify-benchmarkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Apply after every plan step or change to vector store, search, or API.
ElixDb vector/store/API logicmix testiex -S mix and exercise the new APIs (create collection, upsert, search, get, delete as applicable).Logger.debug/2 or :sys.trace for GenServer; fix failures before proceeding.For each step, fill in the Industry comparison table in the step file:
Document efficiency notes at the bottom of the step: gaps, improvement ideas, and follow-up tasks.
When implementing step 8 or ad-hoc benchmarking:
| Metric | How | Use | |--------|-----|-----| | Latency | Per-operation timing; compute mean, p50, p99 | Compare to industry; track over time | | QPS | Queries per second under fixed concurrency | Throughput vs Qdrant/Milvus | | Recall@k | True k-NN vs returned k; fraction overlap | Search quality when ground truth exists | | Memory/CPU | BEAM process stats | Resource efficiency |
Store results in a simple format (e.g. JSON struct or Markdown table) under docs/benchmarks.md or script output for future comparison.
business
When publishing a new elix-db version or making version changes, run versioned sample use cases, collect memory/time/latency, compare to the previous version report, and update reports. Remember this workflow for releases.
development
Storage backends and indexing for vector databases in Elixir. Use when choosing persistence, implementing exact vs approximate k-NN, or integrating pgvector, file, or ETS for vector storage.
development
Design and implement a vector database in Elixir. Use when building embedding storage, similarity search, k-NN retrieval, or when the user mentions vector DB, embeddings, or semantic search in Elixir.
data-ai
Elixir patterns for vector storage and similarity search. Use when implementing GenServer-based vector store, ETS index, Nx/Scholar distance math, or supervision for a vector DB in Elixir.