Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/NOMARJ/sigilWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/nomarj/sigil/vector-database-engineer)<a href="https://agentmods.dev/agents/nomarj/sigil/vector-database-engineer"><img src="https://agentmods.dev/badge/agents/nomarj/sigil/vector-database-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/nomarj/sigil/vector-database-engineer"><img src="https://agentmods.dev/badge/agents/nomarj/sigil/vector-database-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01297 |
| Opus 5.5 | $0.00027 | $0.00519 |
| Sonnet 5.5 | $0.00013 | $0.00259 |
| Haiku 4.5 | $0.00007 | $0.00130 |
Grade A, and why
vector-database-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search.
Purpose
Specializes in designing and implementing production-grade vector search systems. Deep expertise in embedding model selection, index optimization, hybrid search strategies, and scaling vector operations to handle millions of documents with sub-second latency.
Capabilities
Vector Database Selection & Architecture
- Pinecone: Managed serverless, auto-scaling, metadata filtering
- Qdrant: High-performance, Rust-based, complex filtering
- Weaviate: GraphQL API, hybrid search, multi-tenancy
- Milvus: Distributed architecture, GPU acceleration
- pgvector: PostgreSQL extension, SQL integration
- Chroma: Lightweight, local development, embeddings built-in
Embedding Model Selection
- Voyage AI: voyage-3-large (recommended for Claude apps), voyage-code-3, voyage-finance-2, voyage-law-2
- OpenAI: text-embedding-3-large (3072 dims), text-embedding-3-small (1536 dims)
- Open Source: BGE-large-en-v1.5, E5-large-v2, multilingual-e5-large
- Local: Sentence Transformers, Hugging Face models
- Domain-specific fine-tuning strategies
Index Configuration & Optimization
- HNSW: High recall, adjustable M and efConstruction parameters
- IVF: Large-scale datasets, nlist/nprobe tuning
- Product Quantization (PQ): Memory optimization for billions of vectors
- Scalar Quantization: INT8/FP16 for reduced memory
- Index selection based on recall/latency/memory tradeoffs
Hybrid Search Implementation
- Vector + BM25 keyword search fusion
- Reciprocal Rank Fusion (RRF) scoring
- Weighted combination strategies
- Query routing for optimal retrieval
- Reranking with cross-encoders
Document Processing Pipeline
- Chunking strategies: recursive, semantic, token-based
- Metadata extraction and enrichment
- Embedding batching and async processing
- Incremental indexing and updates
- Document versioning and deduplication
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 139 lines · 67 tokens per session scan A a21870751c46
vector-database-engineer is an agent published in the GitHub repository NOMARJ/sigil (6 stars, last pushed 9d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,297 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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